Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm

The rapid development of information technology leads to increasing the number of devices connected to the Internet. Besides, the amount of network attacks also increased. Accordingly, there is an urgent demand to design a defence system proficient in discovering new kinds of attacks. One of the mos...

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Vydáno v:Journal of ambient intelligence and humanized computing Ročník 11; číslo 9; s. 3735 - 3756
Hlavní autoři: Alamiedy, Taief Alaa, Anbar, Mohammed, Alqattan, Zakaria N. M., Alzubi, Qusay M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020
Springer Nature B.V
Témata:
Accuracy
Algorithms
Artificial Intelligence
Classification
Computational Intelligence
Data encryption
Datasets
Engineering
Feature selection
Internet
Intrusion detection systems
Literature reviews
Machine learning
Methods
Multiple objective analysis
Network security
Optimization
Original Research
Packets (communication)
Performance degradation
Robotics and Automation
Sensors
Support vector machines
System effectiveness
Taxonomy
User Interfaces and Human Computer Interaction
Grey wolf algorithm
Feature selection
Intrusion detection system
Classification
Swarm intelligence
Support vector machine
Multi-objective optimisation
ISSN:1868-5137, 1868-5145
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Shrnutí:The rapid development of information technology leads to increasing the number of devices connected to the Internet. Besides, the amount of network attacks also increased. Accordingly, there is an urgent demand to design a defence system proficient in discovering new kinds of attacks. One of the most effective protection systems is intrusion detection system (IDS). The IDS is an intelligent system that monitors and inspects the network packets to identify the abnormal behavior. In addition, the network packets comprise many attributes and there are many attributes that are irrelevant and repetitive which degrade the performance of the IDS system and overwhelm the system resources. A feature selection technique helps to reduce the computation time and complexity by selecting the optimum subset of features. In this paper, an enhanced anomaly-based IDS model based on multi-objective grey wolf optimisation (GWO) algorithm was proposed. The GWO algorithm was employed as a feature selection mechanism to identify the most relevant features from the dataset that contribute to high classification accuracy. Furthermore, support vector machine was used to estimate the capability of selected features in predicting the attacks accurately. Moreover, 20% of NSL–KDD dataset was used to demonstrate effectiveness of the proposed approach through different attack scenarios. The experimental result revealed that the proposed approach obtains classification accuracy of (93.64%, 91.01%, 57.72%, 53.7%) for DoS, Probe, R2L, and U2R attack respectively. Finally, the proposed approach was compared with other existing approaches and achieves significant result.
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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-019-01569-8