Intrusion Detection System for IoT-based Healthcare Intrusions with Lion-Salp-Swarm-Optimization Algorithm: Metaheuristic-Enabled Hybrid Intelligent Approach

Q1 Mathematics
Engineered Science Pub Date : 2023-01-01 DOI:10.30919/es933
Nidhi Goswami, Sahil Raj, D. Thakral, J. L. Arias-Gonzáles, Judith Flores-Albornoz, Edwin Asnate-Salazar, Dhiraj Kapila, Sanjay Yadav, Surendra Kumar
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引用次数: 0

Abstract

The Internet of Things (IoT) makes IoT devices more vulnerable to cyberattacks, especially Distributed Denial of Service (DDoS), raising privacy and security issues. Application-layer DDoS: zombie machines submit queries to the affected server. IDS cannot detect these requests because TCP connections are valid. In this research, we propose the Lion-Salp-Swarm-Optimization Algorithm (LSSOA), which utilizes the freely available IoT-Flock software to create a dataset containing both legitimate and malicious traffic. We employ metaheuristic algorithms like Lion optimization, Whale optimization, Spider-Monkey optimization, and Salp Swarm optimization to detect online attacks and defend the Internet of Medical Things (IoMT) environment. Our framework accurately detects attacks while reducing false positives by overcoming intrusion detection restrictions. Our metaheuristic algorithm outperforms others. Our method is useful for Internet of Things-based enterprises. Overall, the LSSOA framework provides a powerful tool to detect and prevent cyber-attacks in the IoMT environment, demonstrating the potential benefits of novel intrusion detection techniques. Our study emphasizes the importance of enhancing IoT security, particularly in critical industries like healthcare, and highlights the need for continuous efforts to develop effective and innovative approaches to address emerging cyber threats
基于物联网的医疗保健入侵检测系统的狮子- salp -群优化算法:元启发式混合智能方法
物联网(IoT)使物联网设备更容易受到网络攻击,尤其是分布式拒绝服务(DDoS),从而引发了隐私和安全问题。应用层DDoS:僵尸机器向受影响的服务器提交查询。由于TCP连接是有效的,IDS无法检测到这些请求。在本研究中,我们提出了狮子- salp - swarm - optimization Algorithm (LSSOA),该算法利用免费的IoT-Flock软件来创建包含合法和恶意流量的数据集。我们采用诸如狮子优化、鲸鱼优化、蜘蛛猴优化和Salp Swarm优化等元启发式算法来检测在线攻击并防御医疗物联网(IoMT)环境。我们的框架通过克服入侵检测限制,准确地检测攻击,同时减少误报。我们的元启发式算法优于其他算法。我们的方法对基于物联网的企业很有用。总的来说,LSSOA框架提供了一个强大的工具来检测和防止IoMT环境中的网络攻击,展示了新型入侵检测技术的潜在好处。我们的研究强调了加强物联网安全的重要性,特别是在医疗保健等关键行业,并强调需要不断努力开发有效和创新的方法来应对新出现的网络威胁
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Engineered Science
Engineered Science Mathematics-Applied Mathematics
CiteScore
14.90
自引率
0.00%
发文量
83
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