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INTERNATIONAL JOURNAL OF COMPUTER SCIENCE & TECHNOLOGY (IJCST)-SPL


International Journal of Computer Science and Technology (SPL)
S.No. Research Topic Paper ID
   1

Quality Preferences by using H.2.4.k Spatial Databases

D. Hari Krishna, Ch. Sowjanya, P. Radhakrishna

Abstract

A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., rest aurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branchand- bound solution is efficient and robust with respect to different parameters.


IJCST/31/4/
A-570
   2

Performances of Data Compression using CE, GMPMine and 2P2D Algorithms

S. Shalini, J. Srinivasa Rao, U. Sri Rekha

Abstract

In this work, we exploit the characteristics of group movements to discover the information about groups of moving objects in tracking applications. We propose a distributed mining algorithm, which consists of a local GMPMine algorithm and a CE algorithm, to discover group movement patterns. With the discovered information, we devise the 2P2D algorithm, which comprises a sequence merge phase and an entropy reduction phase. In the sequence merge phase, we propose the Merge algorithm to merge the location sequences of a group of moving objects with the goal of reducing the overall sequence length. In the entropy reduction phase, we formulate the HIR problem and propose a Replace algorithm to tackle the HIR problem. In addition, we devise and prove three replacement rules, with which they Replace algorithm obtains the optimal solution of HIR efficiently. Our experimental results show that the proposed compression algorithm effectively reduces the amount of Delivered data and enhances compressibility and, by extension, reduces the energy consumption expense for data transmission in WSNs. We are using these algorithms to find out the performances on the bases on time complexity and space complexity by using visualization technique.

IJCST/31/4/
A-571
   3

Performance of Web Information Gathering System using TREC Model, Category Model, Web Model and Ontology Model

K. Leela Lakshmi Devi, P. Radhakrishna, K. Parimala

Abstract

In this paper, an ontology model is proposed for representing user background knowledge for personalized web information gathering. The model constructs user personalized ontologies by extracting world knowledge from the LCSH system and discovering user background knowledge from user local instance repositories. A multidimensional ontology mining method, exhaustively and specificity, is also introduced for user background knowledge discovery. In evaluation, the standard topics and a large tested were used for experiments. The model was compared against benchmark models by applying it to a common system for Information gathering. The experiment results demonstrate that our proposed model is promising. A sensitivity analysis was also conducted for the ontology model. In this investigation, we found that the combination of global and local knowledge works better than using any one of them. In addition, the ontology model using knowledge with both is-a and part-of semantic relations works better than using. Only one of them. When using only global knowledge, these two kinds of relations have the same Contributions to the performance of the ontology model. The proposed ontology model in this paper provides a solution to emphasizing global and local knowledge in a single computational model. The findings in this paper can be applied to the design of web information gathering systems. The model also has extensive contributions to the fields of Information Retrieval, web Intelligence, Recommendation Systems, and Information Systems.

IJCST/31/4/
A-572
   4

A Comparative Study of Search Logs by using ZEALOUS and Privacy Preserving Algorithms

M. Venkatesh Kodali, K.Subhashini

Abstract

This paper contains a comparative study about publishing frequent keywords, queries, and clicks in search logs. We compare the disclosure limitation guarantees and the theoretical and practical utility of various approaches. Our comparison includes earlier work on anonymity and in distinguishes ability and our proposed solution to achieve probabilistic differential privacy in search logs. In our comparison, we revealed interesting relationships between in distinguish ability and probabilistic differential privacy which might be of independent interest. Our results (positive as well as negative) can be applied more generally to the problem of publishing frequent items or item sets .Our paper concludes with a large experimental study using real applications where we compare ZEALOUS and previous work that achieves k-anonymity in search log publishing. Our results show that ZEALOUS yields comparable utility to k−anonymity while at the same time achieving much stronger privacy guarantees.

IJCST/31/4/
A-573
   5

Collective Behavior of Social Networking Information by using Edge Clustering

S. Rajitha, P. RadhaKrishna, P. Aslesha

Abstract

It is well known that actors in a network demonstrate correlated behaviors. In this work, we aim to predict the outcome of collective behavior given a social network and the behavioral information of some actors. In particular, we explore scalable learning of collective behavior when millions of actors are involved in the network. Our approach follows a social-dimension based learning framework. Social dimensions are extracted to represent the potential affiliations of actors before discriminative learning occurs. As existing approaches to extract social dimensions suffer from scalability, it is imperative to address the scalability issue. We propose an edge-centric clustering scheme to extract social dimensions and a scalable k-means variant to handle edge clustering. Essentially, each edge is treated as one data instance, and the connected nodes are the corresponding features. Then, the proposed k-means clustering algorithm can be applied to partition the edges into disjoint sets, with each set representing one possible affiliation. With this edge-centric view, we show that the extracted social dimensions are guaranteed to be sparse. This model, based on the sparse social dimensions, shows comparable prediction performance with earlier social dimension approaches. An incomparable advantage of our model is that it easily scales to handle networks with millions of actors while the earlier models fail. This scalable approach offers a viable solution to effective learning of online collective behavior on large scale.

IJCST/31/4/
A-574
   6

A Comparative Performance of Monitoring Service Systems Frameworks

K. Kiran Kumar, K. Ammaji

Abstract

The Exponential growth in the global economy is being supported by service systems, realized by recasting mission-critical application services accessed across organizational boundaries. Language-Action Perspective (LAP) is based upon the notion as proposed that “expert behavior requires an exquisite sensitivity to context and that such sensitivity is more in the realm of the human than in that of the artificial. Business processes are increasingly distributed and open, making them prone to failure. Monitoring is, therefore, an important concern not only for the processes themselves but also for the services that comprise these processes. We present a framework for multilevel monitoring of these service systems. It formalizes interaction protocols, policies, and commitments that account for standard and extended effects following the language-action perspective, and allows specification of goals and monitors at varied abstraction levels. We demonstrate how the framework can be implemented and evaluate it with multiple scenarios like between merchant and customer transaction that include specifying and monitoring open-service policy commitments.

IJCST/31/4/
A-575
   7

Performance Evaluating of Single and Parallel Data Processing in the Cloud

K. Kiran Kumar, N. Sunita, J. Srinivasa Rao

Abstract

In this paper we have discussed the challenges and opportunities for efficient parallel data processing in cloud environments and presented Nephele, the first data processing framework to exploit the dynamic resource provisioning offered by today’s I as clouds. We have described Nephele’s basic architecture and presented a performance comparison to the well-established data processing framework Hadoop. The performance evaluation gives a first impression on how the ability to assign specific virtual machine types to specific tasks of a processing job, as well as the possibility to automatically allocate/deal locate virtual machines in the course of a job execution, can help to improve the overall resource utilization and, consequently, reduce the processing cost. With a framework like Nephele at hand, there are a variety of open research issues, which we plan to address for future work. In particular, we are interested in improving Nephele’s ability to adapt to resource overload or underutilization during the job execution automatically. Our current profiling approach builds a valuable basis for this, however, at the moment the system still requires a reasonable amount of user annotations. In general, we think our Work represents an important contribution to the growing field of Cloud computing.

IJCST/31/4/
A-576
   8

Performance of Asynchronous Sensor Networks

K. Parimala, P. Radha Krishna, K. Leela Lakshmi Devi

Abstract

We exposed a new problem in wireless sensor networks, referred to as ongoing continuous neighbor discovery. We argue that continuous neighbor discovery is crucial even if the sensor nodes are static. If the nodes in a connected segment work together on this task, hidden nodes are guaranteed to be detected within a certain probability P and a certain time period T, with reduced expended on the detection. We showed that our scheme works well if every node connected to a segment estimates the in-segment degree of its possible hidden neighbors. To this end, we proposed three estimation algorithms and analyzed their mean square errors. We then presented a continuous neighbor discovery algorithm that determines the frequency with which every node enters the HELLO period. We simulated a sensor network to analyze our algorithms and showed that when the hidden nodes are uniformly distributed in the area, the simplest estimation algorithm is good enough. When the hidden nodes are concentrated around some dead areas, the third algorithm, which requires every node to take into account not only its own degree, but also the average degree of all the nodes in the segment, was shown to be the best.

IJCST/31/4/
A-577
   9

Evaluate the Performance of Data Integrity Proofs in Cloud Storage

D. Hari Krishna, R. Pushpa Latha, J. Srinivasa Rao

Abstract

Cloud computing has been envisioned as the de-facto solution to the rising storage costs of IT Enterprises. With the high costs of data storage devices as well as the rapid rate at which data is being generated it proves costly for enterprises or individual users to frequently update their hardware. Apart from reduction in storage costs data outsourcing to the cloud also helps in reducing the maintenance. Cloud storage moves the user’s data to large data centers, which are remotely located, on which user does not have any control. However, this unique feature of the cloud poses many new security challenges which need to be clearly understood and resolved. One of the important concerns that need to be addressed is to assure the customer of the integrity i.e. correctness of his data in the cloud. As the data is physically not accessible to the user the cloud should provide a way for the user to check if the integrity of his data is maintained or is compromised. In this paper, we provide a scheme which gives a proof of data integrity in the cloud which the customer can employ to check the correctness of his data in the cloud. This proof can be agreed upon by both the cloud and the customer and can be incorporated in the Service Level Agreement (SLA). This scheme ensures that the storage at the client side is minimal which will be beneficial for thin clients. And also we propose The network bandwidth is also minimized as the size of the proof is comparatively very less (k+1 bits for one proof). It should be noted that our scheme applies only to static storage of data. It cannot handle to case when the data need to be dynamically changed. Hence developing on this will be a future challenge. Also the number of queries that can be asked by the client is fixed apriori. But this number is quite large and can be sufficient if the period of data storage is short. It will be a challenge to increase the number of queries using this scheme.

IJCST/31/4/
A-578
   10

Analyzing the Reduced Markov Chain with the Basic Diffusion Map

R. Sowmya, J. Srinivasa Rao, Dr. K. Rama Krishnaiah

Abstract

This work introduces a link-analysis procedure for discovering relationships in a relational database or a graph, generalizing both simple and multiple correspondence analysis. It is based on a random-walk model through the database defining a Markov chain having as many states as elements in the database. Suppose we are interested in analyzing the relationships between some elements (or records) contained in two different tables of the relational database. To this end, in a first step, a reduced, much smaller, Markov chain containing only the elements of interest and preserving the main characteristics of the initial chain is extracted by stochastic complementation. This reduced chain is then analyzed by projecting jointly the elements of interest in the diffusion-map subspace and visualizing the results. This two-step procedure reduces to simple correspondence analysis when onlytwo tables are defined and to multiple correspondence analyses when the database takes the form of a simple star schema. On the other hand, a kernel version of the diffusion-map distance, generalizing the basic diffusion-map Distance to directed graphs, is also introduced and the links with spectral clustering are discussed. Several datasets are analyzed by using the proposed methodology, showing the usefulness of the technique for extracting relationships in relational databases or graphs.

IJCST/31/4/
A-579
   11

Horizontal Aggregation using SPJ Method and Equivalence of Methods

K. Anusha, P. Radhakrishna, P. Sirisha

Abstract

We introduced a new class of extended aggregate functions, called horizontal aggregations which help preparing data sets for data mining and OLAP cube exploration. Specifically, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 14 horizontal aggregations are useful to create data sets with a horizontal layout, as commonly required by data mining algorithms and OLAP cross-tabulation. Basically, a horizontal aggregation returns a set of numbers instead of a single number for each group, resembling a multi-dimensional vector. We proposed an abstract, but minimal, extension to SQL standard aggregate functions to compute horizontal aggregations which just requires specifying sub grouping columns inside the aggregation function call. From a query optimization perspective, we proposed three query evaluation methods. The first one (SPJ) relies on standard relational operators. The second one (CASE) relies on the SQL CASE construct. The third (PIVOT) Uses a builtin operator in a commercial DBMS that is not widely available. The SPJ method is important from a theoretical point of view because it is based on select, project and joins (SPJ) queries. The CASE method is our most important contribution. It is in general the most efficient evaluation method and it has wide applicability since it can be programmed combining GROUP-BY and CASE statements. We proved the three methods produce the same result. We have explained it is not possible to evaluate horizontal aggregations using standard SQL without either joins or “case” constructs using standard SQL operators. Our proposed horizontal aggregations can be used as a database method to automatically generate efficient SQL queries with three sets of parameters: grouping columns, sub grouping columns and aggregated column. The fact that the output horizontal columns are not available when the query is parsed (when the query plan is explored and chosen) makes its evaluation through standard SQL mechanisms infeasible. Our experiments with large tables show our proposed horizontal aggregations evaluated with the CASE method have similar performance to the built-in PIVOT operator. We believe this is remarkable since our proposal is based on generating SQL code and not on internally modifying the query optimizer. Both CASE and PIVOT evaluation methods are significantly faster than the SPJ method. Precomputing a cube on selected dimensions produced acceleration on all methods

IJCST/31/4/
A-580
   12

Encrypted Attack Traffic Through Correlation True and False Positive by using Watermarking

P. Sirisha, Dr. K. Ramakrishnaiah, K. Anusha

Abstract

Tracing attackers’ traffic through stepping stones is a challenging problem, especially when the attack traffic is encrypted, and its timing is manipulated (perturbed) to interfere with traffic analysis. The random timing perturbation by the adversary can greatly reduce the effectiveness of passive, timing-based correlation techniques. We presented a novel active timing-based correlation approach to deal with random timing perturbations. By embedding a unique watermark into the inter-packet timing, with sufficient redundancy, we can make the correlation of encrypted flows substantially more robust against random timing perturbations. Our analysis and our experimental results confirm these assertions. Our watermark-based correlation is provably effective against correlated random timing perturbation as long as the covariance of the timing perturbations on different packets is fixed. Specifically, the proposed watermark-based correlation can, with arbitrarily small average time adjustment, achieve arbitrarily close to 100% watermark detection (correlation true positive) rate and arbitrarily close to 0% collision (correlation false positive) probability at the same time against arbitrarily large (but bounded) random timing perturbation of arbitrary distribution (or process), as long as there are enough packets in the flow to be watermarked

IJCST/31/4/
A-581
   13

The Conditional Random Fields of Layered Approach using Intrusion Detection

Shaik. Riaz, K. V. S. S. Ramakrishna

Abstract

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. Two issues of accuracy and efficiency using conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD’99 intrusion data set show that our proposal system based on Layered conditional Random fields outperforms other well-known methods such as the decision trees and the naïve Bayes. the improvement in attack detection accuracy is very high, particularly, for the U2R attacks and the R2L attacks. Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance

IJCST/31/4/
A-582
   14

Performance of Data Cleaning Techniques

K. Kiran Kumar, M. Radha Madhavi

Abstract

Conditional Functional Dependencies (CFDs) are an extension of Functional Dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules for cleaning relational data. However, finding CFDs is an expensive process that involves intensive manual effort. To effectively identify data cleaning rules, we take 4 techniques for cleaning the data from sample relations. CFDMiner, is based on techniques for mining closed item sets, and is used to detect constant CFDs, namely, CFDs with constant patterns only. It provides a heuristic efficient algorithm for discovering patterns from a fixed FD. It leverages closed-item set mining to reduce search space. CTANE works well when the arity of a sample relation is small and the support threshold is high, but it scales poorly when the arity of a relation increases. FastCFD is more efficient when the arity of a relation is large. Greedy Method formally based on the desirable properties of support and confidence. It studying the computational complexity of automatic generation of optimal tables and providing an efficient approximation algorithm. These techniques are already implemented in the previous papers. We take algorithms of these 4 techniques and find out time and space complexity of each algorithm to know which technique will be helpful in which case and display the results in the form of line and bar charts

IJCST/31/4/
A-583
   15

The Wireless Networks of 802.11 and Buffer Sizing

D. Hari Krishna, B. Bathemma, J. Srinivasa Rao

Abstract

we consider the sizing of network buffers in 802.11 based networks. Wireless networks face a number of fundamental issues that do not arise in wired networks. We demonstrate that the use of fixed size buffers in 802.11 networks inevitably leads to either undesirable channel under-utilization or unnecessary high delays. We present two novel dynamic buffer sizing algorithms that achieve high throughput while maintaining low delay across a wide range of network conditions. Experimental measurements demonstrate the utility of the proposed algorithms in a production WLAN and a lab testbed

IJCST/31/4/
A-584
   16

Linear and Non Linear Programming in Cloud Computing

P. Aslesha, P. Radha Krishna, S. Rajitha

Abstract

In this paper, for the first time, we formalize the problem of securely outsourcing LP computations in cloud computing, and provide such a practical mechanism design which fulfills input/output privacy, cheating resilience, and efficiency. By explicitly decomposing LP computation outsourcing into public LP solvers and private data, our mechanism design is able to explore appropriate security/ efficiency tradeoffs via higher level LP computation than the general circuit representation. We develop problem transformation techniques that enable customers to secretly transform the original LP into some arbitrary one while protecting sensitive input/output information. We also investigate duality theorem and derive a set of necessary and sufficient condition for result verification. Such a cheating resilience design can be bundled in the overall mechanism with close-to-zero additional overhead. Both security analysis and experiment results demonstrates the immediate practicality of the proposed mechanism. We plan to investigate some interesting future work as follows: 1. Devise robust algorithms to achieve numerical stability 2. Explore the sparsity structure of problem for further efficiency improvement 3. Establish formal security framework 4. Extend our result to non-linear programming computation outsourcing in cloud

IJCST/31/4/
A-585
   17

The Clustering with Multi-Viewpoint based Similarity Measure

S. Sesha Sai Priya, K. Rajini Kumari

Abstract

Clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. we compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal

IJCST/31/4/
A-586
   18 This Paper is Removed Due to Copyright Issue IJCST/31/4/
A-587
   19

Robust Video Data Hiding with using Forbidden Zone and Data Hiding and Selective Embedding

P. Ravi Kiran, P. Archana

Abstract

Video data hiding is still an important research topic due to the design complexities involved. We purpose a new video data hiding method that makes use of erasure correction capability of repeat accumulate codes and superiority of forbidden zone data hiding. Selective embedding is utilized in the proposed method to determine host signal samples suitable for data hiding. This method also contains a temporal synchronization scheme in order to withstand frame drop and insert attacks. The proposed framework is tested by typical broadcast material against MPEG-2, H.264 compression, frame-rate conversion attacks, as well as other well-known video data hiding methods. The decoding error values are reported for typical system parameters. The simulation results indicate that the framework can be successfully utilized in video data hiding applications

IJCST/31/4/
A-588
   20

Excellence Predilection by using H.2.4.k Spatial Databases

CH. Shanthi, T. Rajesh

Abstract

A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., rest aurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branchand- bound solution is efficient and robust with respect to different parameters

IJCST/31/4/
A-589
   21

The Clustering Tentative Data using Voronoi Trick Diagrams

V. Sreenivasulu, K.Yamuna Devi, A.Madhavi

Abstract

In this paper, we have studied the problem of clustering uncertain objects whose locations are represented by probability density functions. We have discussed the UKmeans algorithm which was the first algorithm to solve the problem. We have explained that the computation of expected distances dominates the clustering process, especially when the number of samples used in representing objects’ pdfs is large. We have mentioned the existing pruning techniques MinMax-BB and CS. Although these techniques can improve the efficiency of UK-means, they do not consider the spatial relationship among cluster representatives, nor make use of the proximity between groups of uncertain objects to perform pruning in batch. To further improve the performance of UK-means, we have first devised new pruning techniques that are based on Voronoi diagrams. The VDBi algorithm achieves effective pruning by two pruning methods: Voronoi cell pruning and bisector pruning. We have proved theoretically that bisector pruning is strictly stronger than MinMax-BB. Furthermore, we have proposed the idea of pruning by partial ED calculations and have incorporated the method in VDBiP. Having pruned away more than 95 percent of the ED calculations, the execution time has been significantly reduced. It has been reduced to such an extent that the originally relatively cheap pruning overhead has become a dominating term in the total execution time. To further improve efficiency, we exploit the spatial grouping derived from an R-tree index built to organize the uncertain objects. This R-tree boosting technique turns out to cut down the pruning costs significantly. We have also noticed that some of the pruning techniques and R-tree boosting can be effectively combined. Employing different pruning criteria, the combination of these different techniques yields very impressive pruning effectiveness. We have conducted extensive experiments to evaluate the relative performance of the various pruning algorithms and combinations. The results show that our new pruning techniques outperform MinMax- BB consistently over a wide range of experimental parameters. The overhead of computing Voronoi diagrams for our Voronoidiagrambased technique is paid off by the large number of ED calculations saved. The overhead of building an R-tree index also gets compensated by the large reduction of pruning costs. The experiments also consistently demonstrated that the hybrid algorithms can prune more effectively than the other algorithms. Therefore, we conclude that our innovative techniques based on Voronoi diagrams and R-tree index are effective and practical.

IJCST/31/4/
A-590
   22

VANET Network for Trajectory-Based Data Forwarding for Light-Traffic Vehicular

A Nageswara Rao, G. Raja Sekhar, M V Rajesh

Abstract

In this paper, we propose a trajectory-based data forwarding scheme for light-traffic road networks, where the carry delay is the dominating factor for the end-to-end delivery delay. We compute the aggregated end-to-end carry delay using the individual vehicle trajectory along with the vehicular traffic statistics. Our design allows vehicles to share their trajectory information without exposing their actual trajectory to neighbor vehicles. This privacy-preserving trajectory sharing scheme is made possible by exchanging only the expected delay value using local vehicle trajectory information. We also propose a link delay model based on the common assumption of exponential vehicle inter arrival time. It is shown to be more accurate than the state-of-the-art solution. With the increasing popularity of vehicular Ad-Hoc networking, we believe that our forwarding scheme opens the first door for exploiting the potential benefit of the vehicle trajectory for the performance of VANET networking. As a future work, we will develop a data forwarding scheme from stationary nodes (i.e., Internet access points) to moving vehicles for supporting the Infrastructure-to-Vehicle data delivery in vehicular networks. This reverse forwarding to moving vehicles is needed to deliver the road condition information such as the bumps and holes for the driving safety. However, this reverse data forwarding is a more challenging problem because we need to consider both the destination vehicle’s mobility and the packet delivery delay. Also, we will investigate the impact of data traffic volume on the trajectory-based data forwarding in light-traffic vehicular networks and develop a data forwarding scheme considering the data traffic volume, the vehicle trajectory, and the vehicle contact time for communications along the road segment.

IJCST/31/4/
A-591
   23

Performance Evolution of Scalable Multicasting Over Mobile Ad-Hoc Networks

Dr. K. Ramakrishnaiah, P. Prathibha, D. Hari Krishna

Abstract

There is an increasing demand and a big challenge to design more scalable and reliable multicast protocol over a dynamic Ad- Hoc network (MANET). In this paper, we propose an efficient and scalable geographic multicast protocol, EGMP, for MANET. The scalability of EGMP is achieved through a two-tier virtualzone- based structure, which takes advantage of the geometric information to greatly simplify the zone management and packet forwarding. A zone-based bi-directional multicast tree is built at the upper tier for more efficient multicast membership management and data delivery, while the intra-zone management is performed at the lower tier to realize the local membership management. The position information is used in the protocol to guide the zone structure building, multicast tree construction, maintenance, and multicast packet forwarding. Compared to conventional topology based multicast protocols, the use of location information in EGMP significantly reduces the tree construction and maintenance overhead, and enables quicker tree structure adaptation to the network topology change. We also develop a scheme to handle the empty zone problem, which is challenging for the zonebased protocols. Additionally, EGMP makes use of geographic forwarding for reliable packet transmissions, and efficiently tracks the positions of multicast group members without resorting to an external location server. We make a quantitative analysis on the control overhead of the proposed EGMP protocol and our results indicate that the per-node cost of EGMP keeps relatively constant with respect to the network size and the group size. We also performed extensive simulations to evaluate the performance of EGMP. Compared to the classical protocol ODMRP, both geometric multicast protocols SPBM and EGMP could achieve much higher delivery ratio in all circumstances, with respect to the variation of mobility, node density, group size and network range. However, compared to EGMP, SPBM incurs several times of control overhead, redundant packet transmissions and multicast group joining delay. Although SPBM is designed to be scalable to the group size, it has very low packet delivery ratio when the group size is small without a stable membership in each level of quad-tree square, and cannot perform well under a large network size due to the use of multi-level network-wide flooding of control messages. ODMRP takes advantage of broadcasting to achieve more efficient packet forwarding, but the transmissions are much more unreliable due to its difficulty of maintaining forwarding mesh under mobility, which leads to a lower packet delivery ratio. The multicast group joining delay of ODMRP is also much higher than that of EGMP. Our results indicate that geometric information can be used to more efficiently construct and maintain multicast structure, and to achieve more scalable and reliable multicast transmissions in the presence of constant topology change of MANET. Our simulation results demonstrate that EGMP has high packet delivery ratio, and low control overhead and multicast group joining delay under all cases studied, and s scalable to both the group size and the network size. Compared to the geographic multicast protocol SPBM, it has significantly lower control overhead, data transmission overhead, and multicast group joining delay.

IJCST/31/4/
A-592
   24

Performance of Clustering with Multi-Viewpoint based Similarity Measure and Optimization Technique

N. Balayesu, M. Rambabu, D. Anusha

Abstract

Clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. we compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal.

IJCST/31/4/
A-593
   25

Performance Evolution of Ctane, CFD and Fast CFD Algorithms

Pradeep Kalapala, CH. Raja Jacob

Abstract

We propose a class of integrity constraints for relational databases, referred to as Conditional Functional Dependencies (cfds), and study their applications in data cleaning. In contrast to traditional Functional Dependencies (fds) that were developed mainly for schema design, cfds aim at capturing the consistency of data by enforcing bindings of semantically related values. For static analysis of cfds we investigate the consistency problem, which is to determine whether or not there exists a nonempty database satisfying a given set of cfds, and the implication problem, which is to decide whether or not a set of cfds entails another cfd. We show that while any set of transitional fds is trivially consistent, the consistency problem is np-complete for cfds, but it is in ptime when either the database schema is predefined or no attributes involved in the cfds have a finite domain. For the implication analysis of cfds, we provide an inference system analogous to Armstrong’s axioms for fds, and show that the implication problem is conp-complete for cfds in contrast to the linear-time complexity for their traditional counterpart. We also present an algorithm for computing a minimal cover of a set of cfds. Since cfds allow data bindings, in some cases cfds may be physically large, complicating detection of constraint violations. We develop techniques for detecting cfd violations in sql as well as novel techniques for checking multiple constraints in a single query. We also provide incremental methods for checking cfds in response to changes to the database. We experimentally verify the effectiveness of our cfd-based methods for inconsistency detection. This work not only yields a constraint theory for cfds but is also a step toward a practical constraint-based method for improving data quality.

IJCST/31/4/
A-594
   26

Optimized Queue-Overflow Probability on Wireless Scheduling

V Rajesh Babu Darsanala, K.John Paul

Abstract

In this paper, we are interested in wireless scheduling algorithms for the downlink of a single cell that can minimize and closely stabilizes the queue-overflow probability. Specifically, in a largedeviation setting, we are interested in algorithms that maximize the asymptotic decay rate of the queue-overflow probability; as the queue-overflow threshold approaches infinity. We first derive an upper bound on the decay rate of the queue-overflow probability over all scheduling policies. We then focus on a class of scheduling algorithms collectively referred to as the alpha algorithms, the -algorithm picks the user for service at each time that has the largest product of the transmission rate multiplied by the backlog raised to the power alpha. We show that when the overflow metric is appropriately modified, the minimum-cost-to-overflow under the algorithm can be achieved by a simple linear path, and it can be written as the solution of a vector-optimization problem. Using this structural property, we then show that when approaches infinity, the -algorithms asymptotically achieve the largest decay rate of the queue-overflow probability and high over flows rates are identified using multi-level caching technique. Finally, this result enables us to design scheduling algorithms that are both close to optimal in terms of the asymptotic decay rate of the overflow probability and we apply multi level caching technique Then, we calculate the base drop probability for resolving congestion with a stable queue, and apply it to individual flows differently depending on their flow rates and empirically shown to maintain small queue-overflow probabilities over queue-length ranges of practical interest.

IJCST/31/4/
A-595
   27

Analyze Condensed MARKOV Link Chain with KDM PCA

Pavani Thentepudi, K.Subhashini

Abstract

This work introduces a link-analysis procedure for discovering relationships in a relational database or a graph, generalizing both simple and multiple correspondence analysis. It is based on a random-walk model through the database defining a Markov chain having as many states as elements in the database. Suppose we are interested in analyzing the relationships between some elements (or records) contained in two different tables of the relational database. To this end, in a first step, a reduced, much smaller, Markov chain containing only the elements of interest and preserving the main characteristics of the initial chain is extracted by stochastic complementation. This reduced chain is then analyzed by projecting jointly the elements of interest in the diffusion-map subspace and visualizing the results. This two-step procedure reduces to simple correspondence analysis when only two tables are defined and to multiple correspondence analyses when the database takes the form of a simple star schema. On the other hand, a kernel version of the diffusion-map distance, generalizing the basic diffusion-map. Distance to directed graphs, is also introduced and the links with spectral clustering are discussed. Several datasets are analyzed by using the proposed methodology, showing the usefulness of the technique for extracting relationships in relational databases or graphs.

IJCST/31/4/
A-596
   28

Performance of Traffic through Correlation True and False Positive by using Watermarking

Shaik.RIAZ, K.V.S.S.Rama Krishna

Abstract

Tracing attackers’ traffic through stepping stones is a challenging problem, especially when the attack traffic is encrypted, and its timing is manipulated (perturbed) to interfere with traffic analysis. The random timing perturbation by the adversary can greatly reduce the effectiveness of passive, timing-based correlation techniques. We presented a novel active timing-based correlation approach to deal with random timing perturbations. By embedding a unique watermark into the inter-packet timing, with sufficient redundancy, we can make the correlation of encrypted flows substantially more robust against random timing perturbations. Our analysis and our experimental results confirm these assertions. Our watermark-based correlation is provably effective against correlated random timing perturbation as long as the covariance of the timing perturbations on different packets is fixed. Specifically, the proposed watermark-based correlation can, with arbitrarily small average time adjustment, achieve arbitrarily close to 100% watermark detection (correlation true positive) rate and arbitrarily close to 0% collision (correlation false positive) probability at the same time against arbitrarily large (but bounded) random timing perturbation of arbitrary distribution (or process), as long as there are enough packets in the flow to be watermarked.

IJCST/31/4/
A-597
   29

A Performance of Monitoring Service Systems

Anil Kumar Dasari, P.Nageswara Rao

Abstract

We have presented a framework and an approach for Multilevel monitoring of service systems. It draws inspiration from the language-action perspective and extends recent work related to commitments. The key contributions, specific to the use of language-action perspective, include the following: Specification of an ontology of communicative acts that adds semantic content to messages. Support for the specification of policies that address standard and extended effects of communicative effects for multilevel monitoring. In addition, the framework specified supports the following Support for the specification of abstractions over agents and their operations, and decoupling operations from commitments via a mapping specification. Service system specifications for an arbitrary number of services and processes. Specification of message semantics. Specification of local service behaviors that contribute to the participation in multiple conversations. Following the design-science approach the framework embodies our working theory for multilevel monitoring, which we evaluate by. Demonstrating feasibility application to scenarios. Comparison against prior efforts. The evaluation demonstrates comparative advantages of the framework against similar approaches, and how the capabilities of the framework address the needs of multilevel monitoring of service systems. Future work is focused on applying the framework to realworld case studies, and extending the approach to encompass additional levels suggested by LAP frameworks, such as contracts and multiple contracts that require ongoing relationships. For example, the goal hierarchy we have outlined contains two goal categories (agent service and agent protocol goals). We anticipate additional goal categories to account for complex service systems. The framework we have outlined provides an important foundation for these later investigations.

IJCST/31/4/
A-598
   30

Automatic Protocol Blocker for Privacy-Preserving Public Auditing in Cloud Computing

K.Kiran Kumar, K.Padmaja, P.Radha Krishna

Abstract

Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective Third Party Auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The Third Party Auditing process should bring in no new vulnerabilities towards user data privacy. In this paper we are extending the previous system by using automatic blocker for privacy preserving public auditing for data storage security in cloud computing. we utilize the public key based homomorphic authenticator and uniquely integrate it with random mask technique and automatic blocker. to achieve a privacy-preserving public auditing system for cloud data storage security while keeping all above requirements in mind. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.

IJCST/31/4/
A-599
   31

Adaptive Join Operators for Result Rate Maximization on Multiple Inputs

N.Veerendra Reddy, G.Aparna

Abstract

Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data are provided by autonomous data sources through heterogeneous network environments. Their main advantage over traditional join techniques is that they can start producing join results as soon as the first input tuples are available, thus, improving pipelining by smoothing join result production and by masking source or network delays. In this paper, We are introducing the concept of Split Query Parallel Processing (SPQP) before implementing Double Index NEsted-loops Reactive join (DINER), a new adaptive two-way join algorithm for result rate maximization. DINER combines two key elements: an intuitive flushing policy that aims to increase the productivity of in-memory tuples in producing results during the online phase of the join, and a novel reentrant join technique that allows the algorithm to rapidly switch between processing in-memory and disk-resident tuples, thus, better exploiting temporary delays when new data are not available. We then extend the applicability of the proposed technique for a more challenging setup: handling more than two inputs. Multiple Index NEsted-loop Reactive join (MINER) is a multiway join operator that inherits its principles from DINER. Our experiments using real and synthetic data sets demonstrate that the proposed method outperforms than the previousoptimization techniques Ourexperiments also shows that in the presence of multiple inputs, MINER manages to produce a high percentage of early results, outperforming existing techniques for adaptive multiway join.

IJCST/31/4/
A-600
   32

Concert of Human Proficiency in Tune-up-Leaning Systems

N.Tulasi Radha, G.Vijay Kumar

Abstract

In this paper, we motivated the trend towards socio-technical systems in SOA. In such environments social implications must be handled properly. With the human user in the loop numerous concepts, including personalization, expertise involvement, drift interests, and social dynamics become of paramount importance. Therefore, we discussed related Web standards and showed ways to extend them to fit the requirements of a people-centric Web. In particular, we outlined concepts that let people offer their expertise in a service-oriented manner and covered the deployment, discovery and selection of Human-Provided Services. In the future, we aim at providing more fine-grained monitoring and adaptation strategies. An example is the translation service presented in this paper, where some language options are typically used more often, or even more successfully than others. In that case, data types could be modified to reduce the number of available language options in the WSDL interface description and to restrict input parameters. Harnessing delegation patterns that involve various participants, a complex social network perspective is established in which connections are not only maintained between one client and an avatar, but also among avatars.

IJCST/31/4/
A-601
   33

Predicting Hidden Sensors in Wireless Sensor Networks

V.Ravi Kishore, M.V.Rajesh

Abstract

In most sensor networks the nodes are static. Nevertheless, node connectivity is subject to changes because of disruptions in wireless communication, transmission power changes, or loss of synchronization between neighboring nodes. Hence, even after a sensor is aware of its immediate neighbors, it must continuously maintain its view, a process we call continuous neighbor discovery. In this work we distinguish between neighbor discovery during sensor network initialization and continuous neighbor discovery. We focus on the latter and view it as a joint task of all the nodes in every connected segment. Each sensor employs a simple protocol in a coordinate effort to reduce power consumption without increasing the time required to detect hidden sensors.

IJCST/31/4/
A-602
   34

Evolution of Encrypted Attack Traffic through Association True and False Positive Watermarking

P.Revathi, E.Lakshmi Prasanna, P.Pedda Sadhu Naik

Abstract

Network based intruders seldom attack their victims directly from their own computer. Often, they stage their attacks through intermediate “stepping stones” in order to conceal their identity and origin. To identify the source of the attack behind the stepping stone (s), it is necessary to correlate the incoming and outgoing flows or connections of a stepping stone. To resist attempts at correlation, the attacker may encrypt or otherwise manipulate the connection traffic. Timing based correlation approaches have been shown to be quite effective in correlating encrypted connections. However, timing based correlation approaches are subject to timing perturbations that may be deliberately introduced by the attacker at stepping stones. In this paper we propose a novel watermarkbased correlation scheme that is designed specifically to be robust against timing perturbations. Unlike most previous timing based correlation approaches, our watermark-based approach is “active” in that it embeds a unique watermark into the encrypted flows by slightly adjusting the timing of selected packets. The unique watermark that is embedded in the encrypted flow gives us a number of advantages over passive timing based correlation in resisting timing perturbations by the attacker. In contrast to existing passive correlation approaches, our active watermark based correlation does not make any limiting assumptions about the distribution or random process of the original inter-packet timing of the packet flow. In theory, our watermark based correlation can achieve arbitrarily close to 100% correlation true positive rate and arbitrarily close to 0% false positive rate at the same time for sufficiently long flows, despite arbitrarily large (but bounded) timing perturbations of any distribution by the attacker. Our work is the first that identifies 1. Accurate quantitative tradeoffs between the achievable correlation effectiveness and the defining characteristics of the timing perturbation. 2. A provable upper bound on the number of packets needed to achieve a desired correlation effectiveness, given the amount of timing perturbation. Experimental results show that our active watermark based correlation performs better and requires fewer packets than existing, passive timing based correlation methods in the presence of random timing perturbations.

IJCST/31/4/
A-603
   35

The Credit Card Fraud Detection Analysis With Neural Network Methods

M.Jeevana Sujitha, K. Rajini Kumari, N.Anuragamayi

Abstract

Due to the rise and rapid growth of E-Commerce, use counter the credit card fraud effectively, it is necessary to of credit cards for online purchases has dramatically increased understand the technologies involved in detecting credit card and it caused an explosion in the credit card fraud. As credit card frauds and to identify various types of credit card frauds becomes the most popular mode of payment for both online as. There are multiple algorithms for credit card fraud well as regular purchase, cases of fraud associated with it are also detection. They are artificial neural-network models rising. In real life, fraudulent transactions are scattered with which are based upon artificial intelligence and machine genuine transactions and simple pattern matching techniques are learning approach, distributed data mining not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus systems, sequence alignment algorithm which is become imperative for all credit card issuing banks to minimize based upon the spending profile of the cardholder, their losses. Many modern techniques based on Artificial intelligent decision engines which is based on artificial Intelligence, Data mining, Fuzzy logic, Machine learning, intelligence, Meta learning Agents and Fuzzy based Sequence Alignment, Genetic Programming etc., has evolved in systems. The other technologies involved in credit card detecting various credit card fraudulent transactions. A clear fraud detection are Web Services-Based Collaborative Scheme understanding on all these approaches will certainly lead to an for Credit Card Fraud Detection in which participant banks can efficient credit card fraud detection system. This paper presents a share the knowledge about fraud patterns in a heterogeneous survey of various techniques used in credit card fraud detection and distributed environment to enhance their fraud detection mechanisms and evaluates each methodology based on certain design criteria.

IJCST/31/4/
A-604
   36

Accuracy and Efficiency in Intrusion Detection System

J.S.Narendra Kumar, T.Sudha Rani, M.Raja Babu

Abstract

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach and automatic pre defined user prompts. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach and prompts. Experimental results on the benchmark KDD ’99 intrusion data et show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.

IJCST/31/4/
A-605
   37

Optimizing Query Results using Middle Layers Based on Concept of Hierarchies

K.S.N.V. Jyotsna Devi, K. Rajini Kumari

Abstract

Search queries on biomedical databases, such as PubMed, often return a large number of results, only a small subset of which is relevant to the user. Ranking and categorization, which can also be combined, have been proposed to alleviate this information overload problem. Result optimization and results categorization for biomedical databases is the focus of this work. A natural way to organize biomedical citations is according to their MeSH annotations. MeSH is a comprehensive concept hierarchy used by PubMed. In this paper, we present the BioIntelR (BIR) system, adopts the BioNav system enables the user to navigate large number of query results by organizing them using the MeSH concept hierarchy. First, BioIntelR (BIR) system prompts the user for the search criteria and the system automatically connects to a middle layer created at the application level which directs the query to the proper valid query path to select correct criteria of the search result from the biomedical database. The query results are organized into a navigation tree. At each node expansion step, BIR system reveals only a small subset of the concept nodes, selected such that the expected user navigation cost is minimized. In contrast, to the previous systems, the BIR system outperforms and optimizes the query result time and minimizes query result set for easy user navigation, Data Warehousing.

IJCST/31/4/
A-606
   38

The Anonymizing Networks of Blocking Misbehaving Users

G.Naga Mallika, K.Rajini Kumari, T.Rajesh

Abstract

Anonymizing networks such as Tor allow users to access internet services privately by using a series of routers to hide the client’s IP address from the server. The success of such networks However has been limited by users employing this anonymity for abusive purposes such as defacing popular web sites. Web site administrators routinely rely on IP-address for blocking or disabling access to misbehaving users, but blocking IP addresses is not practical if the abuser routes through an anonymizing network. As a result, administrators block all known exit nodes of anonymizing networks, denying anonymous access misbehaving and behaving users alike. To address this problem, we present Nymble, a system in which servers can “blacklist” misbehaving users, thereby blocking users without compromising their anonymity. Our system is thus agnostic to different server definitions of misbehavior servers can blacklist users for whatever reason, and the privacy of blacklisted users is maintained.

IJCST/31/4/
A-607
   39

Cloning Attack Authenticator in Wireless Sensor Networks

A Vanathi, B.Sowjanya Rani

Abstract

A central problem in sensor network security is that sensors are susceptible to physical capture attacks. Once a sensor is compromised, the adversary can easily launch clone attacks by replicating the compromised node, distributing the clones throughout the network, and starting a variety of insider attacks. Previous works against clone attacks suffer from either a high communication/storage overhead or a poor detection accuracy. Wireless Sensor Networks (WSNs) offer an excellent opportunity to monitor environments, and have a lot of interesting applications, some of which are quite sensitive in nature and require full proof secured environment. The security mechanisms used for wired networks cannot be directly used in sensor networks as there is no user-controlling of each individual node, wireless environment, and more importantly, scarce energy resources. In this paper, we address some of the special security threats and attacks in WSNs. We propose a scheme for detection of distributed sensor cloning attack and use of zero knowledge protocol (ZKP) for verifying the authenticity of the sender sensor node. The cloning attack is addressed by attaching a unique fingerprint to each node, that depends on the set of neighboring nodes and itself. The fingerprint is attached with every message a sensor node sends. The ZKP is used to ensure non transmission of crucial cryptographic information in the wireless network in order to avoid Man-In-The Middle (MITM) attack and replay attack.We are extending the previous method and proposed a new method by introducing workrate measure to detect the cloned node.The security and performance analysis indicate that our algorithm can identify clone attacks with a high detection probability at the cost of a low computation/ communication/storage overhead.

IJCST/31/4/
A-608
   40

The Distribution Information for Text Classification for Future Selection Method and Categorization

A.Lakshman Rao, MD.Safura Reshma

Abstract

Text Classification is a well-studied issue because of its widespread application, especially in the field of information Retrieval (IR). For text classification, a major problem is the high dimensionality of the feature space,. It’s very common the number of features is high to hundreds of thousands. However, few of them are usually informative and beneficial for classification task, that may slow down accuracy and efficiency, leads to the curse of dimensionality and makes many methods based on machine learning and statistical theory inapplicable. Thus efficient feature reduction is highly desired and essential. Selecting the most informative features or removing non-informative features from the original feature space to reduce the feature dimension, is more popular than feature extraction due to its lower computational cost in text domain. Moreover many theoretical and empirical researches have proved that feature selection can not only reduces computational cost in space and time, but also improves performance by carefully selecting good features for classification. Depending on whether rely on categorization algorithms or not, FS methods can be classified into wrapper and filter. Generally, the former may be more effective than the later but a higher computational cost. Filter methods, with an evaluation function independent of categorization algorithms, are often much less time consuming than wrapper ones and have been widely used in text classification. A variety of FS methods and feature evaluation metrics have been explored from different perspectives in the past.

IJCST/31/4/
A-609