Journal ArticleDOI
Mining network data for intrusion detection through combining SVMs with ant colony networks
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TLDR
Experiments show that CSVAC (Combining Support Vectors with Ant Colony) outperforms SVM alone or CSOACN alone in terms of both classification rate and run-time efficiency.About:
This article is published in Future Generation Computer Systems.The article was published on 2014-07-01. It has received 234 citations till now. The article focuses on the topics: Intrusion detection system & Ant colony optimization algorithms.read more
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Journal ArticleDOI
Network Intrusion Detection for IoT Security Based on Learning Techniques
TL;DR: This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques and provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems.
Journal ArticleDOI
CANN: An intrusion detection system based on combining cluster centers and nearest neighbors
TL;DR: A novel feature representation approach, namely the cluster center and nearest neighbor (CANN) approach, which shows that the CANN classifier not only performs better than or similar to k-NN and support vector machines trained and tested by the original feature representation in terms of classification accuracy, detection rates, and false alarms.
Journal ArticleDOI
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection
TL;DR: A detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with variousMachine learning techniques in detecting intrusive activities and future directions are provided for attack detection using machinelearning techniques.
Journal ArticleDOI
Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system
TL;DR: A multi-level hybrid intrusion detection model that uses support vector machine and extreme learning machine to improve the efficiency of detecting known and unknown attacks and a modified K-means algorithm is proposed to build a high-quality training dataset that contributes significantly to improving the performance of classifiers.
Journal ArticleDOI
Internet of Things: A survey on machine learning-based intrusion detection approaches
Kelton A. P. Costa,João Paulo Papa,Celso O. Lisboa,Roberto Munoz,Victor Hugo C. de Albuquerque +4 more
TL;DR: Recent and in-depth research of relevant works that deal with several intelligent techniques and their applied intrusion detection architectures in computer networks with emphasis on the Internet of Things and machine learning are aimed at.
References
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The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Fast training of support vector machines using sequential minimal optimization, advances in kernel methods
TL;DR: SMO breaks this large quadratic programming problem into a series of smallest possible QP problems, which avoids using a time-consuming numerical QP optimization as an inner loop and hence SMO is fastest for linear SVMs and sparse data sets.
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Fast training of support vector machines using sequential minimal optimization
TL;DR: In this article, the authors proposed a new algorithm for training Support Vector Machines (SVM) called SMO (Sequential Minimal Optimization), which breaks this large QP problem into a series of smallest possible QP problems.