Due to increase in intrusion incidents over internet, many network intrusion detection systems are developed to prevent network attacks. Data mining, pattern recognition and classification methods are used to classify network events as a normal or anomalous one. This paper is aimed at evaluating different tree based classification algorithms that classify network events in intrusion detection systems. Experiments are conducted on NSL-KDD 99 dataset. Dimensionality of the attribute of the dataset is reduced. The results show that RandomTree model holds the highest degree of accuracy and reduced false alarm rate. RandomTree model is evaluated with other leading intrusion detection models to determine its better predictive accuracy.

Sumaiya Thaseen I

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Aswani Kumar Cherukuri

Department of Information Technology

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

An analysis of supervised tree based classifiers for intrusion detection system

2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering

A lot of intrusion and hacking events are surrounding the internet domain, bringing in a need for security systems. Intrusion detection system (IDS) is employed in the network to identify the attacks by continuously monitoring the system activities. The major issue for any intrusion detection model is to identify anomalies with maximum accuracy and minimal false alarms. An intrusion detection model is developed combining chi-square feature selection and LPBoost algorithm. Chi-square feature selection is deployed to build the optimal features as the network traffic data consists of many attributes. The optimum features are utilised by the LPBoost algorithm for classification of network traffic. An ensemble classifier is chosen as it typically outperforms a single classifier. The experiments are performed using NSL-KDD and UNSW-NB data sets. The results clearly show that the hybrid model achieves a higher detection and reduced false positive rate in contrast to other techniques.

International Journal of Internet Technology and Secured Transactions

Intrusion detection model using feature extraction and LPBoost technique

Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS) such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusion detection model by integrating the principal component analysis (PCA) and support vector machine (SVM). The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C) and kernel parameter gamma (γ), thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL-KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed.

Fulltext

Journal of Computing and Information Technology

Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

Expert Systems with Applications

A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier

Decision tree based light weight intrusion detection using a wrapper approach

2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications

A detailed analysis of the KDD CUP 99 data set

Data mining-based intrusion detectors

Malaysian Journal of Computer Science

Identifying False Alarm for Network Intrusion Detection System Using Hybrid Data Mining and Decision Tree

Journal	Data powered by Typeset2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering
Publisher	Data powered by TypesetIEEE
Open Access	0