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.

&nbsp;

&nbsp;

VIT University

Communication is the heart of the day to day activity in the current world. Since the world has practiced with electronic devices for carrying out all the daily activities, electronic and wireless communication along with internet plays a major role in the success of providing a sophisticated life. Moreover, the internet users are also gradually increasing in the recent two decades for making their life easy through fast communication. In such a scenario, the numbers of intruders are increasing in the internet dramatically. In this paper, we provide a survey on the use of machine learning algorithms for developing intelligent intrusion detection systems which are most useful for providing secure communication in wireless networks. Moreover, we compare all the important intelligent intrusion detection systems based on their performance and also suggest some new ideas for improving the decision accuracy of current intelligent intrusion detection systems. Copyright © 2019 Inderscience Enterprises Ltd.

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

International Journal of Advanced Intelligence Paradigms

Intelligent intrusion detection techniques for secure communications in wireless networks: a survey

Security in communication is an important and challenging issue in the recent years. In the past, many secure routing algorithms have been proposed to provide energy efficiency and security in Mobile Ad hoc Networks. However due to the increase in attackers and new types of attacks, the existing secure routing techniques are challenged by malicious users. In order to handle these problems, a new trust and fuzzy cluster based dynamic secure routing algorithm is proposed in this paper for enhancing energy efficiency and security. For this purpose, a new trust model which uses historical trust, neighbor trust and behavioral trust is proposed in this work for isolating the malicious nodes in the routing process. Moreover, the fuzzy clustering technique proposed in this paper provides energy efficiency by applying cluster head based routing, cluster head rotation and fuzzy decision making. The fuzzy rules also help to handle the uncertainty occurring due to the movement of nodes in the network. From the experiments conducted in this work, it is proved that the proposed algorithm enhances the security of communication which in turn increases the packet delivery ratio and reduces the delay. © 2017, Springer Science+Business Media, LLC.

Wireless Personal Communications

A Trust and Fuzzy Cluster Based Dynamic Secure Routing Algorithm for Mobile Ad Hoc Networks

Cyber attack detection is an important and challenging area of research in the field of information technology. In such a scenario, intruders introduce new mechanisms by applying polymorphic mechanisms in order to escape from the intrusion detection systems. This leads to loss in data and increase in security vulnerabilities. In the past, many soft computing techniques were used from the field of machine learning for enhancing the efficiency of intrusion detection systems (IDSs) in computer networks. Among them, fuzzy logic played a major role for making effective decisions. In addition, the neural networks are also contributing more in this area for training the datasets to form rules which can be used to develop an effective intrusion detection system. In this paper, we propose a new intrusion detection system by combining neural networks with temporal and type-2 fuzzy logic for performing effective classification of the dataset. In addition, a new feature selection algorithm is also proposed in this paper which uses information gain of attributes with fuzzy logic decision making for selecting the optimal number of features from the dataset. This work has been tested by using NSL-KDD dataset and through the experiments conducted in this work it is proved that the proposed system increases the intrusion detection accuracy and reduces the false positive rate when it is compared with other existing systems. Copyright © 2018 Inderscience Enterprises Ltd.

International Journal of Internet Technology and Secured Transactions

Intelligent intrusion detection system using temporal analysis and type-2 fuzzy neural classification

In this paper, a new intelligent neuro-genetic framework is proposed for detecting the intruders in networks by analysing their behaviour. For this purpose, a new genetic algorithm based feature selection algorithm (GAFSA) and a neuro-genetic fuzzy classification algorithm (NGFCA) have been proposed in this paper which are used to identify the malicious users through classification of user behaviours. The main advantage of this proposed framework is that it reduces the attacks by identifying the intruders with high accuracy and reduced false positive rate. This work has been tested through simulations and also using bench mark dataset for analysing the performance of the proposed algorithms. From the experiments conducted in this work using full features and selected features by applying the proposed classification algorithm, it is proved that the proposed framework detects the intruders more accurately and reduces the attacks leading to increase in packet delivery ratio and reduction in delay. Copyright © 2018 Inderscience Enterprises Ltd.

International Journal of Reasoning-based Intelligent Systems

An intelligent neuro-genetic framework for effective intrusion detection

Due to raising concerns about the privacy preserving of personal information, organizations which are using the customers' records in data mining activities are rammed to take actions for protecting the individual's privacy. Preserving of sensitive and personal information is vital for the success of data mining techniques. Privacy Preserving Data Mining (PPDM) handles such consequences by reconciliation of both preserving privacy and data utilization. Conventionally, Geometrical Data Transformation Methods (GDTMs) have been extensively used for privacy conserving cluster. The major drawback in these GDTMs are geometric conversion function are not reversible, that results in a low level assurance of security. In this paper, the technique that preserves the privacy of delicate information in a multiparty cluster situation called the guideline segment investigation based technique is proposed. The function of this proficiency is assessed advance by employing a classic K-means cluster algorithms and machine learning-based cluster methodology on artificial and realistic world information sets. The effectiveness of grouping is computed prior and then afterward the change of security preserving. Our suggested transformation established on PCA when competed to the traditional GDTMs resulted in superior protection of privacy and improve performance. © 2015 IEEE.

VIT Unitversity

2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM)

Preservation of privacy in data mining by using PCA based perturbation technique

In today's computer world security is the major issue. For every project most of the money spent on the security and testing than developing. Because security issue is one of the major issue for everyone to protect data. According to the security analysts more than seven billion hackers are existing over one hundred and five billion computers. Almost all of them trying to collapse the security system. There are so many security attacks are rotating throughout the network. Phishing is one of the popular security attack on the websites due to fall in this attack we lose our valuable information. Today most of us are using e-commerce websites for online shopping. Most of them are paying money through online. By taking this as an advantage so many hackers are host phishing website for stealing our bank and credit card detail. No only this so many social accounts are hacking daily. Other than this there is a new model of attack that is Desktop phishing. It is one of the advance attack which is applied on victim computer it can prevent the victim user actions in websites. By that we don't allow to the real page even to modify of change our password. By that the attacker success his goal. For phishing detection there are so many solutions are available. But Till now so many antiviruses can't find this attack. So we prose a TSO programmatically solution to Desktop phishing attack. It has a capability to detection and prevention of the attack. © 2015 IEEE.