Papers by Mahendra Sisodia
International Journal of Advanced Research in Computer Science and Electronics Engineering, Mar 27, 2012
Intrusion detection by automated means is gaining widespread interest due to the serious impact o... more Intrusion detection by automated means is gaining widespread interest due to the serious impact of Intrusions on computer system or network. Several techniques have been introduced in an effort to minimize up to some extent the risk associated with Intrusion attack. In this respect, we have used a novel Machine learning technique which comprises of Naïve Bayes approach and weighted radial basis function Network approach. The proposed scheme exhibits very promising results in comparison with many earlier techniques while experimenting on KDDCup'99 data set in detecting Intrusions.
International Journal of Advanced Research in Computer Science and Electronics Engineering, Mar 27, 2012
As network attacks have increased in number and severity over the past few years, intrusion detec... more As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. Intrusion poses a serious security risk in a network environment. The ever growing new intrusion types pose a serious problem for their detection. The human labeling of the available network audit data instances is usually tedious, time consuming and expensive. In this paper, we apply one of the efficient data mining algorithms called k-means clustering via naïve bayes classification for anomaly based network intrusion detection. Experimental results on the KDD cup'99 data set show the novelty of our approach in detecting network intrusion. It is observed that the proposed technique performs better in terms of Detection rate when applied to KDD'99 data sets compared to a naïve bayes based approach.
Network Intrusion detection by using Feature Reduction Technique
International Journal of Advanced Research in Computer Science and Electronics Engineering, Mar 27, 2012
As network attacks have increased in number and severity over the past few years, intrusion detec... more As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. Intrusion poses a serious security risk in a network environment. The ever growing new intrusion types pose a serious problem for their detection. In this paper, a new intrusion detection method based on Principle Component Analysis (PCA) and Random Forest with low overhead and high efficiency is presented. System call data and command sequences data are used as information sources to validate the proposed method. The frequencies of individual system calls in a trace and individual commands in a data block are computed and then data column vectors which represent the traces and blocks of the data are formed as data input. PCA is applied to reduce the high dimensional data vectors and distance between a vector and its projection onto the subspace reduced is used for anomaly detection. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for real-time intrusion detection.
Network Protocols and Algorithms, 2011
Network Intrusion Detection Systems (NIDSs) have become an important component in network securit... more Network Intrusion Detection Systems (NIDSs) have become an important component in network security infrastructure. Currently, many NIDSs are rule-based systems whose performances highly depend on their rule sets. Unfortunately, due to the huge volume of network traffic, coding the rules by security experts becomes difficult and time-consuming. Since data mining techniques can build network intrusion detection models adaptively, data mining-based NIDSs have significant advantages over rule-based NIDSs. Network and system security is of paramount importance in the present data communication environment. Hackers and intruders can create many successful attempts to cause the crash of the networks and web services by unauthorized intrusion. New threats and associated solutions to prevent these threats are emerging together with the secured system evolution. Network Intrusion Detection Systems are one of these solutions. The main function of NIDSs is to protect the resources from threats. It analyzes and predicts the behaviors of users, and then these behaviors will be considered an attack or a normal behavior. We use Random projection and Random Tree to detect network intrusions.
Proceedings of the International Conference on Advances in Computer Science and Electronics Engineering, 2012
Mobile Ad-hoc networks are a collection of mobile hosts that communicate with each other without ... more Mobile Ad-hoc networks are a collection of mobile hosts that communicate with each other without any infrastructure. Due to security vulnerabilities of the routing protocols, wireless ad hoc networks may be unprotected against attacks by the malicious nodes. One of these attacks is the Black Hole Attack against network integrity absorbing all data packets in the network. Since the data packets do not reach the destination node on account of this attack, data loss will occur. The damage will be serious if malicious nodes work together as a group. This type of attack is called multiple or cooperative black hole attack. In this paper are doing simulation study of network under multiple black hole nodes and identifying the results after applying defense scheme in multiple Black Hole nodes. We simulated black hole attacks in network simulator 2 (ns-2) and measured the packet loss in the network with and without a black hole. We also proposed a simple solution against black hole nodes attack. Our IDS scheme improved the 90% network performance in the presence of cooperative black hole attack.
An improved network intrusion detection technique based on k-means clustering via Naïve bayes classification
As network attacks have increased in number and severity over the past few years, intrusion detec... more As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. Intrusion poses a serious security risk in a network environment. The ever growing new intrusion types pose a serious problem for their detection. The human labeling of the available network audit data instances is usually tedious, time consuming and expensive. In this paper, we apply one of the efficient data mining algorithms called k-means clustering via naïve bayes classification for anomaly based network intrusion detection. Experimental results on the KDD cup'99 data set show the novelty of our approach in detecting network intrusion. It is observed t...
Network Intrusion Detection by using Supervised and Unsupervised Machine Learning Techniques: A Survey
ijctee.org
AbstractAs network attacks have increased in number and severity over the past few years, intrus... more AbstractAs network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and ...
Design and Implementation of an Algorithm for Finding Frequent Sequential Traversal Patterns from Web Logs Based on Weight Constraint
2009 Second International Conference on Emerging Trends in Engineering & Technology, 2009
Page 1. Abstract Many frequent sequential traversal pattern mining algorithms have been develope... more Page 1. Abstract Many frequent sequential traversal pattern mining algorithms have been developed which mine the set of frequent subsequences traversal pattern satisfying a minimum support constraint in a session database. ...
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Papers by Mahendra Sisodia