Chandrashekhar Azad

Work place: Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Ranchi-835215, India

E-mail: csazad@bitmesra.ac.in

Website:

Research Interests: Network Architecture, Network Security, Data Mining, World Wide Web, Data Structures and Algorithms

Biography

Mr. Chandrashekhar Azad received his B.Sc. (Honours) in Computer Application form Ranchi University in 2007 and MCA from Ranchi University, Ranchi, Jharkhand (India) in 2011. He is associated with the Central University of Jharkhand as a teaching assistant in the year 2011-2012.  At present, he is research scholar at Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand (India). His research interest includes Data mining, swarm intelligence, network security etc.

Author Articles
Genetic Algorithm to Solve the Problem of Small Disjunct In the Decision Tree Based Intrusion Detection System

By Chandrashekhar Azad Vijay Kumar Jha

DOI: https://doi.org/10.5815/ijcnis.2015.08.07, Pub. Date: 8 Jul. 2015

Intrusion detection system is the most important part of the network security system because the volume of unauthorized access to the network resources and services increase day by day. In this paper a genetic algorithm based intrusion detection system is proposed to solve the problem of the small disjunct in the decision tree. In this paper genetic algorithm is used to improve the coverage of those rules which are cope with the problem of the small disjunct. The proposed system consists of two modules rule generation phase, and the second module is rule optimization module. We tested the effectiveness of the system with the help of the KDD CUP dataset and the result is compared with the REP Tree, Random Tree, Random Forest, Na?ve Bayes, and the DTLW IDS (decision tree based light weight intrusion detection system). The result shows that the proposed system provide the best result in comparison to the above mentioned classifiers.

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Data Mining in Intrusion Detection: A Comparative Study of Methods, Types and Data Sets

By Chandrashekhar Azad Vijay Kumar Jha

DOI: https://doi.org/10.5815/ijitcs.2013.08.08, Pub. Date: 8 Jul. 2013

In the era of information and communication technology, Security is an important issue. A lot of effort and finance are being invested in this sector. Intrusion detection is one of the most prominent fields in this area. Data mining in network intrusion detection can automate the network intrusion detection field with a greater efficiency. This paper presents a literature survey on intrusion detection system. The research papers taken in this literature survey are published from 2000 to 2012. We can see that almost 67 % of the research papers are focused on anomaly detection, 23 % on both anomaly and misuse detection and 10 % on misuse detection. In this literature survey statistics shows that 42 % KDD cup dataset, 20 % DARPA dataset and 38 % other datasets are used by the different researchers for testing the effectiveness of their proposed method for misuse detection, anomaly detection or both.

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