Journal ArticleDOI
Intrusion detection system using an optimized kernel extreme learning machine and efficient features
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TLDR
Based on IDS criteria, the proposed method can easily outperform general classification algorithms which use all the features of the employed dataset, especially in R2L and U2R with the accuracy of 98.73% and 98.22% respectively which is the highest among the current literature.Abstract:
In the study of Intrusion Detection System (IDS) choosing proper combination of features is of great importance. Many researchers seek to obtain appropriate features with optimization algorithms. There are several optimization algorithms that can properly select a near-optimal combination of features to reach an improved IDS. Genetic Algorithms (GA) as one of the most powerful methods have been used in this research for feature selection. In this paper, voted outputs of built models on the GA suggested features of a more recent version of KDD CUP 99 dataset, NSL KDD, based on five different labels, have been gathered as a new dataset. Kernel Extreme Learning Machine (KELM), whose parameters have been optimally set by GA, is executed on the obtained dataset and results are collected. Based on IDS criteria, our proposed method can easily outperform general classification algorithms which use all the features of the employed dataset, especially in R2L and U2R with the accuracy of 98.73% and 98.22% respectively which is the highest among the current literature.read more
Citations
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Journal ArticleDOI
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade
TL;DR: This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade.
Journal ArticleDOI
A taxonomy of network threats and the effect of current datasets on intrusion detection systems
Hanan Hindy,David Brosset,David Brosset,Ethan Bayne,Amar Seeam,Christos Tachtatzis,Robert Atkinson,Xavier Bellekens +7 more
TL;DR: In this paper, the authors provide researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks.
Journal ArticleDOI
Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review
Nivedita Mishra,Sharnil Pandya +1 more
TL;DR: In this article, a multi-fold survey of different security issues present in IoT layers: perception layer, network layer, support layer, application layer, with further focus on Distributed Denial of Service (DDoS) attacks.
Posted Content
A Novel Hybrid Kpca and SVM with ga Model for Intrusion Detection
Abstract: A novel support vector machine (SVM) model combining kernel principal component analysis (KPCA) with genetic algorithm (GA) is proposed for intrusion detection. In the proposed model, a multi-layer SVM classifier is adopted to estimate whether the action is an attack, KPCA is used as a preprocessor of SVM to reduce the dimension of feature vectors and shorten training time. In order to reduce the noise caused by feature differences and improve the performance of SVM, an improved kernel function (N-RBF) is proposed by embedding the mean value and the mean square difference values of feature attributes in RBF kernel function. GA is employed to optimize the punishment factor C, kernel parameters @s and the tube size @? of SVM. By comparison with other detection algorithms, the experimental results show that the proposed model performs higher predictive accuracy, faster convergence speed and better generalization.
Journal ArticleDOI
Hybrid Intrusion Detection using MapReduce based Black Widow Optimized Convolutional Long Short-Term Memory Neural Networks
P Rajesh Kanna,P. Santhi +1 more
TL;DR: In this article , an efficient hybrid IDS model is presented which is built using MapReduce based Black Widow Optimized Convolutional-Long Short-Term Memory (BWO-CONV-LSTM) network.
References
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Journal ArticleDOI
Statistical pattern recognition: a review
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Journal ArticleDOI
Statistical Pattern Recognition
TL;DR: In this paper, the primary goal of pattern recognition is supervised or unsupervised classification, and the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been used.
Proceedings ArticleDOI
A detailed analysis of the KDD CUP 99 data set
TL;DR: A new data set is proposed, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.
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