Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

The ever increasing number of Android malware has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, we hypothesize that the performance of existing approaches can be improved by deriving relevant attributes through effective feature selection methods. In this paper, we propose a novel two-step feature selection approach based on Rough Set and Statistical Test named as RSST to extract refined system calls, which can effectively discriminate malware from benign apps. By refined set of system call, we mean the existence of highly relevant calls that are uniformly distributed thought target classes. Moreover, an optimal attribute set is created, which is devoid of redundant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on three datasets resulted in an accuracy of 99.9%, Area Under Curve&nbsp;(AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors&nbsp;(Information Gain, CFsSubsetEval, ChiSquare, FreqSel, and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Moreover, the empirical analysis of RSST derived system calls outperformed other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes, and network traces.

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

World University root node

Concurrency and Computation Practice and Experience

Identification of Android malware using refined system calls

Department of Digital Communications

2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT)

A study on possible application of RFID system in different real-time environments

Indian Journal of Science and Technology

Medical Query Expansion using UMLS

Social networks have become a part of human life. Starting from sharing information like text, photos, messages, many have started share latest news, and news related pictures in the Media domain, question papers, assignments, and workshops in Education domain, online survey, marketing, and targeting customers in Business domain, and jokes, music, and videos in Entertainment domain. Because of its usage by Internet surfers in all possible ways, even wee would mention the social networking media as the current Internet culture. While enjoying the information sharing on Social Medias, yet it requires a great deal for security and privacy. The users' information that are to be kept undisclosed, should be made private.

Department of Computer Applications

Fulltext

Procedia Computer Science

On Privacy and Security in Social Media – A Comprehensive Study

In this paper we present a method for detecting malicious Android applications using feature selection methods. Three distinguishing features i.e. opcodes, methods and strings are extracted from each Android file and using feature selection techniques, prominent and diverse, top ranking features are mined. Different tree classifiers are used to categorize Android files as either malware or benign. Results show that methods is the most credible feature, which gives accuracy of 88.75% with 600 attributes using Correlation Feature Selection method and Adaboost with J48 as base classifier. © 2015 The Authors.

Investigation of feature selection methods for android malware analysis

Department of Mathematics

School of Advanced Sciences

Global Journal of Mathematical Analysis

Holder Inequalities for a subclass of univalent functions involving Dziok-Srivastava Operator

Communications in Computer and Information Science Global Trends in Information Systems and Software Applications

Hybrid Approach for Construction of Summaries and Clusters of Blog Data for Improved Blog Search Results

2011 International Conference on Computational Intelligence and Communication Networks

Alternate Query Construction Agent for Improving Web Search Result Using WordNet