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Investigations on Bio-Inspired Algorithm for Network Intrusion Detection – A Review


Affiliations
1 School of Computer Science and Engineering, VIT University, Chennai, Tamil Nadu, India
 

A network is a collection of interconnected devices that can share information and resources, exchange files, and enable electronic communications. IDS is an important part of Network Security to secure a network. An Intrusion Detection System (IDS) is a fundamental building block in network security. A wide variety of techniques have been proposed and implemented to improve the performance and accuracy of intrusion detection models. It is used by many MNC companies such as Wipro, TCS, and L&T and they are having their IDS in the organization system. CNN (Convolutional Neural Network, Machine Learning, Data mining, Deep learning models such as SVM (Support Vector Machine) are performing well above the benchmark to prevent the systems from all kinds of attacks. Recently, bio-inspired optimization algorithms are metaheuristics that mimic the nature of solving optimization problems. Bio-inspired algorithms are gaining the moment that brings a revolution in computer science. This paper investigates the feature selection techniques of bio-inspired algorithms-driven Intrusion Detection Systems. This paper categorises these SI approaches based on their applicability in improving various aspects of an intrusion detection process. Furthermore, the paper discusses the capabilities and characteristics of various datasets used in experimentation. The main goal is to assist researchers in evaluating the capabilities and limitations of SI algorithms in identifying security threats and challenges in designing and implementing an IDS for the detection of cyber-attacks across multiple domains. The survey identifies existing issues and provides recommendations for how to effectively address them.

Keywords

IDS, Deep Learning, Optimization, Classification, Feature Selection, Bio-Inspired Algorithm.
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  • Investigations on Bio-Inspired Algorithm for Network Intrusion Detection – A Review

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Authors

Jeyavim Sherin R C
School of Computer Science and Engineering, VIT University, Chennai, Tamil Nadu, India
Parkavi K
School of Computer Science and Engineering, VIT University, Chennai, Tamil Nadu, India

Abstract


A network is a collection of interconnected devices that can share information and resources, exchange files, and enable electronic communications. IDS is an important part of Network Security to secure a network. An Intrusion Detection System (IDS) is a fundamental building block in network security. A wide variety of techniques have been proposed and implemented to improve the performance and accuracy of intrusion detection models. It is used by many MNC companies such as Wipro, TCS, and L&T and they are having their IDS in the organization system. CNN (Convolutional Neural Network, Machine Learning, Data mining, Deep learning models such as SVM (Support Vector Machine) are performing well above the benchmark to prevent the systems from all kinds of attacks. Recently, bio-inspired optimization algorithms are metaheuristics that mimic the nature of solving optimization problems. Bio-inspired algorithms are gaining the moment that brings a revolution in computer science. This paper investigates the feature selection techniques of bio-inspired algorithms-driven Intrusion Detection Systems. This paper categorises these SI approaches based on their applicability in improving various aspects of an intrusion detection process. Furthermore, the paper discusses the capabilities and characteristics of various datasets used in experimentation. The main goal is to assist researchers in evaluating the capabilities and limitations of SI algorithms in identifying security threats and challenges in designing and implementing an IDS for the detection of cyber-attacks across multiple domains. The survey identifies existing issues and provides recommendations for how to effectively address them.

Keywords


IDS, Deep Learning, Optimization, Classification, Feature Selection, Bio-Inspired Algorithm.

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F214503