VNR Vignana Jyothi Institute of Engineering and Technology

About: VNR Vignana Jyothi Institute of Engineering and Technology is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 803 authors who have published 798 publications receiving 3681 citations. The organization is also known as: VNRVJIET.

An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks

Abstract: Software-Defined Network (SDN) has become a promising network architecture in current days that provide network operators more control over the network infrastructure. The controller, also called as the operating system of the SDN, is responsible for running various network applications and maintaining several network services and functionalities. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential targets. Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to the Internet. As the control layer is vulnerable to DDoS attacks, the goal of this paper is to detect the attack traffic, by taking the centralized control aspect of SDN. Nowadays, in the field of SDN, various machine learning (ML) techniques are being deployed for detecting malicious traffic. Despite these works, choosing the relevant features and accurate classifiers for attack detection is an open question. For better detection accuracy, in this work, Support Vector Machine (SVM) is assisted by kernel principal component analysis (KPCA) with genetic algorithm (GA). In the proposed SVM model, KPCA is used for reducing the dimension of feature vectors, and GA is used for optimizing different SVM parameters. In order to reduce the noise caused by feature differences, an improved kernel function (N-RBF) is proposed. The experimental results show that compared to single-SVM, the proposed model achieves more accurate classification with better generalization. Moreover, the proposed model can be embedded within the controller to define security rules to prevent possible attacks by the attackers.

A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns

Abstract: Mining temporal association patterns from time-stamped temporal databases, first introduced in 2009, remain an active area of research. A pattern is temporally similar when it satisfies certain specified subset constraints. The naive and apriori algorithm designed for non-temporal databases cannot be extended to find similar temporal patterns in the context of temporal databases. The brute force approach requires performing $$2^{n }$$ true support computations for ‘n’ items; hence, an NP-class problem. Also, the apriori or fp-tree-based algorithms designed for static databases are not directly extendable to temporal databases to retrieve temporal patterns similar to a reference prevalence of user interest. This is because the support of patterns violates the monotonicity property in temporal databases. In our case, support is a vector of values and not a single value. In this paper, we present a novel approach to retrieve temporal association patterns whose prevalence values are similar to those of the user specified reference. This allows us to significantly reduce support computations by defining novel expressions to estimate support bounds. The proposed approach eliminates computational overhead in finding similar temporal patterns. We then introduce a novel dissimilarity measure, which is the fuzzy Gaussian-based dissimilarity measure. The measure also holds the monotonicity property. Our evaluations demonstrate that the proposed method outperforms brute force and sequential approaches. We also compare the performance of the proposed approach with the SPAMINE which uses the Euclidean measure. The proposed approach uses monotonicity property to prune temporal patterns without computing unnecessary true supports and distances.

Addressing Security and Privacy Issues of IoT Using Blockchain Technology

Abstract: Internet of Things (IoT) has been the most emerging technology in the last decade because the number of smart devices and its associated technologies has rapidly grown in both industrial and research prospectives. The applications are developed using IoT techniques for real-time monitoring. Due to low processing power and storage capacity, smart things are vulnerable to the attacks as existing security or cryptography techniques are not suitable. In this study, we initially review and identify the security and privacy issues that exist in the IoT system. Second, as per blockchain technology, we provide some security solutions. The detailed analysis, including enabling technology and integration of IoT technologies, is explained. Finally, a case study is implemented using the Ethererum-based blockchain system in a smart IoT system and the results are discussed.

GARUDA: Gaussian dissimilarity measure for feature representation and anomaly detection in Internet of things

Abstract: The objective of any anomaly detection system is to efficiently detect several types of malicious traffic patterns that cannot be detected by conventional firewall systems. Designing an efficient intrusion detection system has three primary challenges that include addressing high dimensionality problem, choice of learning algorithm, and distance or similarity measure used to find the similarity value between any two traffic patterns or input observations. Feature representation and dimensionality reduction have been studied and addressed widely in the literature and have also been applied for the design of intrusion detection systems (IDS). The choice of classifiers is also studied and applied widely in the design of IDS. However, at the heart of IDS lies the choice of distance measure that is required for an IDS to judge an incoming observation as normal or abnormal. This challenge has been understudied and relatively less addressed in the research literature both from academia and from industry. This research aims at introducing a novel distance measure that can be used to perform feature clustering and feature representation for efficient intrusion detection. Recent studies such as CANN proposed feature reduction techniques for improving detection and accuracy rates of IDS that used Euclidean distance. However, accuracies of attack classes such as U2R and R2L are not significantly promising. Our approach GARUDA is based on clustering feature patterns incrementally and then representing features in different transformation space through using a novel fuzzy Gaussian dissimilarity measure. Experiments are conducted on both KDD and NSL-KDD datasets. The accuracy and detection rates of proposed approach are compared for classifiers such as kNN, J48, naive Bayes, along with CANN and CLAPP approaches. Experiment results proved that proposed approach resulted in the improved accuracy and detection rates for U2R and R2L attack classes when compared to other approaches.