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In this paper, we introduce an Intrusion Detection system (IDS) based Hybrid Evolutionary Neural Network (HENN). A brief overview of IDS, genetic algorithm, and related detection techniques are discussed. The system architecture is also introduced. Factors affecting the genetic algorithm are addressed in detail. Unlike other implementations of IDS, Input features, network structure and connection...
In recent years, intrusion detection has emerged as an important technique for network security. Machine learning techniques have been applied to the field of intrusion detection. They can learn normal and anomalous patterns from training data and via Feature selection improving classification by searching for the subset of features which best classifies the training data to detect attacks on computer...
An enhanced version of an algorithm to provide anomaly based intrusion detection alerts for cyber security state awareness is detailed. A unique aspect is the training of an error back-propagation neural network with intrusion detection rule features to provide a recognition basis. Ethernet network packet details are subsequently provided to the trained network to produce a classification. This leverages...
Resiliency and security in control systems such as SCADA and nuclear plant's in today's world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly...
41 higher-level derived features were presented by Stolfo et al that help in distinguishing normal connections from attacks. Numerous researchers employed these features to study the utilization of machine learning for intrusion detection and reported detection rates up to 91% with false positive rates less than 1%. Unfortunately, with these 41 derived features as inputs, IDS systems take long time...
The data mining techniques used for extracting patterns that represent abnormal network behavior for intrusion detection is an important research area in network security.Based on the new proposed theoretical model of recognition space and further division method, this paper introduces a novel improvement of neural network classification: further division of recognition space(FDRS).Then studied the...
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