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ARP spoofing attack, one of the most important security topics, is usually taught in courses such as Intrusion Detection in Local Area Networks (LANs). In such a course, hands-on labs are very important as they facilitate students’ learning on how to detect ARP spoofing using various types of security solutions, such as intrusion detection and prevention systems (IDS/IPS). The preparation of these hands-on labs are usually the task of Security Instructors who are required to select and use efficient security solutions for their hands-on experiments; the problem that presents itself is that most of these security instructors lack the sufficient hands-on experience and skills. For this reason and because of the diversity of the available security solutions, the security instructors are having much difficulty when selecting the adequate security solutions for their hands-on labs. This paper is a comparative study for educational purpose. It provides analysis based on practical experiments carried out on a number of security solutions regarding their ability to detect ARP spoofing. Our analysis provides means for security instructors to evaluate and select the appropriate security solutions for their hands-on labs. In addition, we clearly show that ARP spoofing has not been given enough attention by most tested security solutions, even though this attack presents a serious threat, is very harmful and more dangerously it is easy to conduct. As a solution, we propose the requirements for an ideal algorithm that can be used by security solutions to detect effectively any ARP spoofing attack.

Keywords:- ARP spoofing, ARP spoofing detection, Denial of Service (DoS)

Analysis and interpretation of DNA Microarray data is a fundamental task in bioinformatics. Feature Extraction plays a critical role in better performance of the classifier. We address the dimension reduction of DNA features in which relevant features are extracted among thousands of irrelevant ones through dimensionality reduction. This enhances the speed and accuracy of the classifiers. Principal Component Analysis (PCA) is a technique used for feature extraction which helps to retrieve intrinsic information from high dimensional data in eigen spaces to solve the curse of dimensionality problem. The curse of dimensionality means n >> m, where n is a large number of features and m is a small number of samples (may be too less). Neural Networks (NN) and Support Vector Machine (SVM) are implemented and their performances are measured in terms of predictive accuracy, specificity, and sensitivity. First, we implement PCA for significant feature extraction and then FFNN trained using Backpropagation (BP) and SVM are implemented on the reduced feature set. Next, we propose a Multiobjective Genetic Algorithm-based fuzzy clustering technique using real coded encoding of cluster centers for clustering and clas- si cation. This technique is implemented on microarray cancer data to select training data using multiobjective genetic algorithm with non-dominated sorting (MOGA-NSGA-II). The two objective functions for this multiobjective techniques are optimization of cluster compactness as well as separation simultaneously. This approach identifies the solution i.e. the individual chromosome which gives the optimal value of the compactness and separation. Then we find high con dence points for these non-dominated set using a fuzzy voting technique. Support Vector Machine (SVM) classifier is further trained by the selected training points which have high confidence value. Then remaining points are classified by trained SVM classifier. Finally, the four clustering label vectors through majority vot- ing ensemble are combined, i.e., each point is assigned a class label that obtains he maximum number of votes among the four clustering solutions. The performance of the proposed MOGA-SVM, classification and clustering method has been compared to MOGA-BP, SVM, BP. The performance are measured in terms of Silhoutte Index, ARI Index respectively. The experiment were carried on three public domain cancer data sets, viz., Ovarian, Colon and Leukemia cancer data to establish its superiority.

Keywords:- Cancer Classification; Feature Reduction; Multiobjective genetic al- gorithm; Neural Network; Pareto-optimality; Principal components; Support Vec- tor Machine(SVM)

Group signature is an extension of digital signature, which allows a group member to sign anonymously a document on behalf of the group. Any client can verify the authenticity of the document by using the public parameters of the group. The identity of the group member cannot be revealed from the group signature. In case of a legal dispute, an authorized group member can disclose the identity of the group member from the signed document. Group signature can have wide application to corporate world, banks, and e-commerce applications. In this thesis, we designed a group signature protocol based upon hard computa- tional assumptions such as, Discrete Logarithm Problem (DLP), Integer Factor- ization Problem (IFP), and Computational Die Hellmann (CDH) problem. The proposed scheme is proved to be resistant against colluding attack. Moreover, the group signature remains valid, if some members leave the group or some new mem- bers join the group. Full traceability feature is con rmed in the proposed scheme. The scheme can have wide applications in real life scenarios such as e-banking, e-voting, and e-commerce applications.

Keywords:- anonymity; colluding attack; discrete logarithm; group signature; unforgeability

Image restoration is an essential and unavoidable preprocessing operation for many security applications like biometric security, video surveillance, object tracking, image data communication etc. Images are generally degraded due to faulty sensor, channel transmission error, camera mis-focus, atmospheric turbulence, relative motion between camera and object etc. Such conditions are inevitable while capturing a scene through camera. Restoration of such images is highly essential for further image processing and other tasks.

Keywords:- Image restoration, Impulsive noise, Gaussian noise, Motion blur, Out-of-focus blur, Regularization, Convex minimization.

In today’s world of evolving technology, everybody wishes to accomplish tasks in least time. As information available online is perpetuating every day, it becomes very dicult to summarize any more than 100 documents in acceptable time. Thus, ”text summarization” is a challenging problem in the area of Natural Language Processing (NLP) especially in the context of global languages. In this thesis, we survey taxonomy of text summarization from di erent aspects. It briefly explains di erent approaches to summarization and the evaluation parameters. Also presented are a thorough details and facts about more than fifty automatic text summarization systems to ease the job of researchers and serve as a short encyclopedia for the investigated systems. Keyword extraction methods plays vital role in text mining and document processing. Keywords represent essential content of a document. Text mining applications take the advantage of keywords for processing documents. A quality Keyword is a word that represents the exact content of the text subsetly. It is very dicult to process large number of documents to get high quality keywords in acceptable time. This thesis gives a comparison between the most popular keyword extractions method, tf-idf and the proposed method that is based on Helmholtz Principle. Helmholtz Principle is based on the ideas from image processing and derived from the Gestalt theory of human perception. We also investigate the run time to extract the keywords by both the methods. Experimental results show that keyword extraction method based on Helmholtz Principle outperformancetf-idf.

Keywords:- Keywords: Text Mining, Text Summarization, Stemming, Helmholtz Peinciple, Information Retrieval, Keyword Extraction, Term Frequency - Inverse Document Frequency.

Load balancing is an essential requirement of any multi-hop wireless network. A wireless routing protocol is accessed on its ability to distribute traffic over the network nodes and a good routing protocol achieves this without introducing un- acceptable delay. The most obvious benefit is manifested in increasing the life of a battery operated node which can eventually increase the longevity of the entire network. In the endeavor of finding the shortest distance between any two nodes to transmit data fast the center nodes become the famous picks. The centrally located nodes connect many subnetworks and serve as gateways to some subnetworks that become partitioned from the rest of the network in its absence. Thus, the lifetime of the center nodes become a bottleneck for connectivity of a subnetwork prior to its partition from the rest of the network. An unbiased load can cause congestion in the network which impacts the overall throughput, packet delivery ratio and the average end to end delay. In, this thesis we have mitigated the unbiased load distribution on centrally located nodes by pushing traffic further to the peripheral nodes without compromising the average end to end delay for a greater network longevity and performances. We proposed a novel routing metric , load and a minimization criterion to decide a path that involves nodes with less load burden on them. The simulations of the proposed mechanism run on NS-2.34 for 16 and 50 nodes have revealed an average 2.26% reduction of load on the center node in comparison with AOMDV.

Keywords:- Routing Protocol,Wireless Routing Algorithm,Improving performance of MANET

There has been rapid growth of software development. Due to various causes, the software comes with many defects. In Software development process, testing of software is the main phase which reduces the defects of the software. If a developer or a tester can predict the software defects properly then, it reduces the cost, time and effort. In this paper, we show a comparative analysis of software defect prediction based on classification rule mining. We propose a scheme for this process and we choose different classication algorithms. Showing the comparison of predictions in software defects analysis. This evaluation analyzes the prediction performance of competing learning schemes for given historical data sets(NASA MDP Data Set). The result of this scheme evaluation shows that we have to choose different classifier rule for different data set.

Keywords:- Software defect prediction, classification Algorithm, Cofusion matrix.

Understandability is one of the important characteristics of software quality, because it may influence the maintainability of the software. Cost and reuse of the software is also affected by understandability. In order to maintain the software, the programmers need to understand the source code. The understandability of the source code depends upon the psychological complexity of the software, and it requires cognitive abilities to understand the source code. The understandability of source code is get effected by so many factors, here we have taken different factors in an integrated view. In this we have chosen rough set approach to calculate the understandability based on outlier detection. Generally the outlier is having an abnormal behavior, here we have taken that project has may be easily understandable or difficult to understand. Here we have taken few factors, which affect understandability, an brings forward an integrated view to determine understandability.

Keywords:- Understandability, Rough set, Outlier, Spatial Complexity.

We introduce the Upgrading Shortest Paths Problem, a new combinatorial problem for improving network connectivity with a wide range of applications from multicast communication to wildlife habitat conservation. We define the problem in terms of a network with node delays and a set of node upgrade actions, each associated with a cost and an upgraded (reduced) node delay. The goal is to choose a set of upgrade actions to minimize the shortest delay paths between demand pairs of terminals in the network, subject to a budget constraint. We show that this problem is NP-hard. We describe and test two greedy algorithms against an exact algorithm on synthetic data and on a real-world instance from wildlife habitat conservation. While the greedy algorithms can do arbitrarily poorly in the worst case, they perform fairly well in practice. For most of the instances, taking the better of the two greedy solutions accomplishes within 5% of optimal on our benchmarks.

Keywords:- Shortest Path Problem,improving Network connectivity,demand pairs.

ModiFied Condition / Decision Coverage (MC / DC) is a thesis/Dissertation Submitted For Master in Engineering.ModiFied Condition / Decision Coverage (MC / DC) is a white box testing criteria aiming to prove that all conditions involved in a predicate can in uence the predicate value in the desired way. In regulated domains such as aerospace and safety critical domains, software quality assurance is subjected to strict regulations such as the DO-178B standard. Though MC/DC is a standard coverage criterion, existing automated test data genera- tion approaches like CONCOLIC testing do not support MC/DC. To address this issue we present an automated approach to generate test data that helps to achieve an increase in MC/DC coverage of a program under test. We use code transformation techniques for transforming program. This transformed program is inserted into the CREST TOOL. It drives CREST TOOL to generate test suite and increase the MC/DC coverage. Our tech- nique helps to achieve a signi cant increase in MC/DC coverage as compared to traditional CONCOLIC testings. Our experimental results show that the proposed approach helps to achieve on the average approximately 20.194 % for Program Code Transformer(PCT) and 25.447 % for Exclusive- Nor Code Transformer. The average time taken for seventeen programs is 6.89950 seconds.

Keywords:- CONCOLIC testing, Code transformation techniques, MC/DC, Coverage Analyser,ME Thesis,Master Dissertation

In scientific investigations data oftentimes have different nature. For instance, they might originate from distinct sources or be cast over separate terminologies. In order to gain insight into the phenomenon of interest, a natural task is to identify the correspondences that exist between these different aspects. This is the motivating idea of redescription mining, the data analysis task studied in this thesis. Redescription mining aims to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. A practical example in biology consists in finding geographical areas that admit two characterizations, one in terms of their climatic profile and one in terms of the occupying species. Discovering such redescriptions can contribute to better our understanding of the influence of climate over species distribution. Besides biology, applications of redescription mining can be envisaged in medicine or sociology, among other elds. Previously, redescription mining was restricted to propositional queries over Boolean attributes. However, many conditions, like aforementioned climate, cannot be expressed naturally in this limited formalism. In this thesis, we consider more general query languages and propose algorithms to find the corresponding redescriptions, making the task relevant to a broader range of domains and problems. Specifically, we start by extending redescription mining to non-Boolean attributes. In other words, we propose an algorithm to handle nominal and real-valued attributes natively. We then extend redescription mining to the relational setting, where the aim is to find corresponding connection patterns that relate almost the same object tuples in a network.

Keywords:- Data Mining Thesis,ME Thesis,Master Dissertation,Redescription mining

The quickly growing community of Internet users is requesting multiple applications and services. At the same time the structure of the network is changing. From the performance point of view, there is a tight interplay between the application and the network design. The network must be constructed to provide an adequate performance of the target application. In this thesis we consider how to improve the quality of users’ experience concentrating on two popular and resource-consuming applications: bulk data transfer and real-time video streaming. We share our view on the techniques which enable feasibility and deployability of the network functionality leading to unquestionable performance improvement for the corresponding applications. Modern mobile devices, equipped with several network interfaces, as well as multihomed residential Internet hosts are capable of maintaining multiple simultaneous attachments to the network. We propose to enable simultaneous multipath data transmission in order to increase throughput and speed up such bandwidth-demanding applications as, for example, file download. We design an extension for Host Identity Protocol (mHIP), and propose a multipath data scheduling solution on a wedge layer between IP and transport, which effectively distributes packets from a TCP connection over available paths. We support our protocol with a congestion control scheme iii iv and prove its ability to compete in a friendly manner against the legacy network protocols. Moreover, applying game-theoretic analytical modelling we investigate how the multihomed HIP multipath-enabled hosts coexist in the shared network. The number of real-time applications grows quickly.
Efficient and reliable transport of multimedia content is a critical issue of today’s IP network design. In this thesis we solve scalability issues of the multicast dissemination trees controlled by the hybrid error correction. We propose a scalable multicast architecture for potentially large overlay networks. Our techniques address suboptimality of the adaptive hybrid error correction (AHEC) scheme in the multicast scenarios. A hierarchical multi-stage multicast tree topology is constructed in order to improve the performance of AHEC and guarantee QoS for the multicast clients. We choose an evolutionary networking approach that has the potential to lower the required resources for multimedia applications by utilizing the error-correction domain separation paradigm in combination with selective insertion of the supplementary data from parallel networks, when the corresponding content is available.

Keywords:- Multipath Data Transmission,Multicast Network Thesis,ME Thesis,Master Dissertation,Computer Network

Multi-view learning is a task of learning from multiple data sources where each source represents a different view of the same phenomenon. Typ- ical examples include multimodal information retrieval and classification of genes by combining heterogeneous genomic data. Multi-view learning methods can be motivated by two interrelated lines of thoughts: if single view is not sufficient for the learning task, other views can complement the information. Secondly, learning by searching for an agreement between views may generalize better than learning from a single view. In this thesis, novel methods for unsupervised multi-view learning are proposed. Multi-view learning methods, in general, work by searching for an agree- ment between views. However, defining an agreement is not straightforward in an unsupervised learning task. In this thesis, statistical dependency is used to define an agreement between the views. Assuming that the shared information between the views is more interesting, statistical dependency is used to find the shared information. Based on this principle, a fast linear preprocessing method that performs data fusion during exploratory data analysis is introduced. Also, a novel evaluation approach based on the dependency between views to compare vector representations for bilingual corpora is introduced.

Keywords:- Data Fusion,Multi-View Learning Thesis,ME Thesis,Master Dissertation

The word "biometrics" is derived from the Greek words 'bios' and 'metric' which means life and measurement respectively. This directly translates into "life measurement". Biometrics is the automated recognition of individuals based on their behavioral and biological characteristics. Biometric features are information extracted from biometric samples which can be used for comparison with a biometric reference. Biometrics comprises methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. In computer science, in particular, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. Biometrics has fast emerged as a promising technology for authentication and has already found place in most hi-tech security areas. An efficient clustering technique has been proposed for partitioning large biometric database during identification. The system has been tested using bin-miss rate as a performance parameter. As we are still getting a higher bin-miss rate, so this work is based on devising an indexing strategy for identification of large biometric database and with greater accuracy. This technique is based on the modified B+ tree which reduces the disk accesses. It decreases the data retrieval time and also possible error rates. The indexing technique is used to declare a person‟s identity with lesser number of comparisons rather than searching the entire database. The response time deteriorates, as well as the accuracy of the system degrades as the size of the database increases. Hence for larger applications, the need to reduce the database to a smaller fraction arises to achieve both higher speeds and improved accuracy. The main purpose of indexing is to retrieve a small portion of the database for searching the query. Since applying some traditional clustering schemes does not yield satisfactory results, we go for an indexing strategy based on tree data structures. Index is used to look-up, input and delete data in an ordered manner. Speed and efficiency are the main goals in the different types of indexing. Speed and efficiency include factors like access time, insertion time, deletion time, and space overhead. The main aim is to perform indexing of a database using different trees beginning with Binary Search tree followed by B tree before proceeding to its variations, B+ tree and Modified B+ tree, and subsequently determine their performance based on their respective execution times.

Keywords:-Biometric Data,Tree DataStructure,Biometric Database,Thesis,ME Thesis,Master Dissertation

Intrusion is broadly defined as a successful attack on a network. The definition of attack itself is quite ambiguous and there exists various de nitions of it. With the advent of Internet age and the tremendous increase in the computational resources available to an average user, the security risk of each and every computer has grown exponentially. Intrusion Detection System (IDS) is a software tool used to detect unauthorized access to a computer system or network. It is a dynamic monitoring entity that complements the static monitoring abilities of a rewall. Data Mining techniques provide efficient methods for the development of IDS. The idea behind using data mining techniques is that they can automate the process of creating traffic models from some reference data and thereby eliminate the need of laborious manual intervention. Such systems are capable of detecting not only known attacks but also their variations. Existing IDS technologies, on the basis of detection methodology are broadly clas- sified as Misuse or Signature Based Detection and Anomaly Detection Based System. The idea behind misuse detection consists of comparing network traffic against a Model describing known intrusion. The anomaly detection method is based on the analysis of the pro les that represent normal traffic behavior. Semi-Supervised systems for anomaly detection would reduce the demands of the training process by reducing the requirement of training labeled data. A Self Training Support Vector Machine based detection algorithm is presented in this thesis. In the past, Self-Training of SVM has been successfully used for reducing the size of labeled training set in other domains. A similar method was implemented and results of the simulation performed on the KDD Cup 99 dataset for intrusion detection show a reduction of upto 90% in the size of labeled training set required as compared to the supervised learning techniques.

Keywords:-Intrusion Detection System,IDS,Network Security Thesis,Vector Learning,ME Thesis,Master Dissertation

In the present generation, the social life of everyone has become associated with the online social networks. These sites have made a drastic change in the way we pursue our social life. Making friends and keeping in contact with them and their updates has become easier. But with their rapid growth, many problems like fake profiles, online impersonation have also grown. There are no feasible solution exist to control these problems. In this project, we came up with a framework with which automatic detection of fake profiles is possible and is efficient. This framework uses classification techniques like Support Vector Machine, Nave Bayes and Decision trees to classify the profiles into fake or genuine classes. As, this is an automatic detection method, it can be applied easily by online social networks which has millions of profile whose profiles can not be examined manually.

Keywords:- Social Networking,Fake Profile Detection,Vector Learning,ME Thesis,Master Dissertation

The use of information retrieval (IR) systems is evolving towards larger, more complicated queries. Both the IR industrial and research communities have generated significant evidence indicating that in order to continue improving retrieval effectiveness, increases in retrieval model complexity may be unavoidable. From an operational perspective, this translates into an increasing computational cost to generate the final ranked list in response to a query. Therefore we encounter an increasing tension in the trade-o between retrieval effectiveness (quality of result list) and efficiency (the speed at which the list is generated). This tension creates a strong need for optimization techniques to improve the efficiency of ranking with respect to these more complex retrieval models.
This thesis presents three new optimization techniques designed to deal with different aspects of structured queries. The first technique involves manipulation of interpolated subqueries, a common structure found across a large number of retrieval models today. We then develop an alternative scoring formulation to make retrieval models more responsive to dynamic pruning techniques. The last technique is delayed execution, which focuses on the class of queries that utilize term dependencies and term conjunction operations. In each case, we empirically show that these optimizations can significantly improve query processing efficiency without negatively impacting retrieval effectiveness.

Keywords:- information retrieval (IR) systems,Query Optimization,Query processing,ME Thesis,Master Dissertation

The parallel trends of greater energy-efficiency and more aggressive power management are yielding computers that inch closer to energy-proportional computing with every generation. Energy-proportional computing, in which power consumption scales closely with workload, has unintended side effects for security and privacy. Saving energy is an unqualified boon for computer operators, but it is becoming easier to identify computing activities by observing power consumption because an energy-proportional computer reveals more about its workload. This thesis demonstrates the potential for system-level power analysis—the inference of a computers internal states based on power observation at the “plug.” It also examines which hardware components and software workloads have the greatest impact on information leakage. This thesis identifies the potential for privacy violations by demonstrating that a malicious party could identify which webpage from a given corpus a user is viewing with greater than 99% accuracy. It also identifies constructive applications for power analysis, evaluating its use as an anomaly detection mechanism for embedded devices with greater than 94% accuracy for each device tested. Finally, this thesis includes modeling work that correlates AC and DC power consumption to pinpoint which components contribute most to information leakage and analyzes software workloads to identify which classes of work lead to the most information leakage. Understanding the security and privacy risks and opportunities that come with energy-proportional computing will allow future systems to either apply system-level power analysis fruitfully or thwart its malicious application.

Keywords:- Social Networking,Fake Profile Detection,Vector Learning,ME Thesis,Master Dissertation