Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms
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TLDR
Intelligent feature selection methods and intrusion detection (ISTID) organization in webs based on neuron-genetic algorithms, intelligent software agents, genetic algorithms, particulate swarm intelligence and neural networks, rough-set are proposed.Abstract:
Rapid Internet use growth and applications of diverse military have managed researchers to develop smart systems to help applications and users achieve the facilities through the provision of required service quality in networks. Any smart technologies offer protection in interactions in dispersed locations such as, e-commerce, mobile networking, telecommunications and management of network. Furthermore, this article proposed on intelligent feature selection methods and intrusion detection (ISTID) organization in webs based on neuron-genetic algorithms, intelligent software agents, genetic algorithms, particulate swarm intelligence and neural networks, rough-set. These techniques were useful to identify and prevent network intrusion to provide Internet safety and improve service value and accuracy, performance and efficiency. Furthermore, new algorithms of intelligent rules-based attributes collection algorithm for efficient function and rules-based improved vector support computer, were proposed in this article, along with a survey into the current smart techniques for intrusion detection systems.read more
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References
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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
Machine learning based phishing detection from URLs
TL;DR: A real-time anti-phishing system, which uses seven different classification algorithms and natural language processing (NLP) based features, is proposed and Random Forest algorithm with only NLP based features gives the best performance with the 97.98% accuracy rate for detection of phishing URLs.
Journal ArticleDOI
Survey on SDN based network intrusion detection system using machine learning approaches
TL;DR: This survey evaluated the techniques of deep learning in developing SDN-based Network Intrusion Detection Systems (NIDS) and covered tools that can be used to develop NIDS models in SDN environment.
Journal ArticleDOI
Firefly algorithm based feature selection for network intrusion detection
Selvakumar B,K. Muneeswaran +1 more
TL;DR: The proposed work, deploys filter and wrapper based method with firefly algorithm in the wrapper for selecting the features, and shows that 10 features are sufficient to detect the intrusion showing improved accuracy.
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An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks
Hossam Faris,Ala' M. Al-Zoubi,Ali Asghar Heidari,Ibrahim Aljarah,Majdi Mafarja,Mohammad A. Hassonah,Hamido Fujita +6 more
TL;DR: An intelligent detection system that is based on Genetic Algorithm and Random Weight Network is proposed to deal with email spam detection tasks and can automatically identify the most relevant features of the spam emails.