Hybrid extreme learning machine-based approach for IDS in smart Ad Hoc networks

EURASIP Journal on Wireless Communications and Networking

Research

Bijian Liu¹ 

[1] Fujian Vocational College of Agriculture, Fuzhou, Fujian, China;

关键词: Extreme learning machine;    Intrusion detection system;    Detection accuracy;    Detection time;    Multi-node ad hoc network;    And RMSE;   

DOI  :  10.1186/s13638-023-02297-6

 received in 2023-06-23, accepted in 2023-08-22,  发布年份 2023

来源: Springer

【 摘 要 】

In recent years, intrusion detection systems (IDSs) have increasingly come to be regarded as a significant method due to their potential to develop into a key component that is necessary for the safety of computer networks. This work focuses on the usage of extreme learning machines, which are also known as ELMs, with the purpose of spotting prospective intrusions and assaults. The proposed method combines the self-adaptive differential evolution method for optimising network input weights and hidden node biases and multi-node probabilistic approach with the extreme learning machine for deriving network output weights. This body of work presents an innovative method of learning that can be put into practice in order to determine whether or not an incursion has taken place in the system that is the focus of the investigation that is being carried out by this body of work. A hybrid extreme learning machine is used in the execution of this strategy. When there is one thousand times more traffic on a network, the ability of regular IDS systems to detect malicious network intrusions is lowered by a factor of one hundred. This is because there are less opportunities to detect the intrusions. This is due to the fact that there are less probabilities to identify potential dangers. This paper lays the groundwork for a novel methodology for identifying malicious network breaches. The findings of the simulation demonstrated that putting into practice the approach that was proposed resulted in an improvement in the accuracy of the scenario's classification while it was being investigated. The implementation of the method seems to have produced the desired results.

【 授权许可】

CC BY   
© Springer Nature Switzerland AG 2023

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