Anomaly-based intrusion detection system using Harris Hawks optimisation with a sigmoid neuron network

被引：0

作者：

Narengbam, Lenin ^{[1
]}

Dey, Shouvik ^{[1
]}

机构：

[1] Natl Inst Technol Nagaland, Dept Comp Sci & Engn, Dimapur 797103, India

来源：

INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTER SECURITY | 2024年 / 24卷 / 1-2期

关键词：

intrusion detection system; IDS; neural network; meta-heuristic optimisation; machine learning; CUCKOO SEARCH ALGORITHM; IDS;

D O I：

10.1504/IJICS.2024.140219

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This study introduces an innovative approach, merging Harris Hawks optimisation (HHO) with a sigmoid neuron network (SN), to enhance anomaly-based intrusion detection systems (ADS) performance. The resultant SN-HHO hybrid model aims to elevate detection rates and lower false positive rates (FPRs) within ADS. Evaluation across five datasets - UNSW-NB15, CIDDS-001, NSL-KDD, AWID3, and CICDDoS2019 - reveals heightened accuracy and faster convergence compared to existing methods. This work underscores the potential synergy of meta-heuristic optimisation and artificial neural networks, offering a promising strategy to fortify IDS performance and reliability, thus presenting a novel direction for advancing anomaly detection practices.

引用

页码：5 / 27

页数：24

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