基于群体的网络安全渗透测试:智能家居和物联网网络的安全意识

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information (Switzerland) Pub Date : 2023-09-30 DOI:10.3390/info14100536
Thomas Schiller, Bruce Caulkins, Annie S. Wu, Sean Mondesire
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引用次数: 0

摘要

物联网(IoT)设备在当今的计算机网络中很常见。这些设备可能具有强大的计算能力,但容易被网络安全利用。为了弥补这些日益增长的安全弱点,这项工作提出了一种新的人工智能方法,通过使用自主的、基于群体的网络安全渗透测试,使这些物联网网络更加安全。在这项工作中,将引入的粒子群优化(PSO)渗透测试技术与传统的线性和基于队列的方法进行比较,以发现智能家居和物联网网络中的漏洞。为了评估PSO方法的有效性,使用网络模拟器来模拟两种规模的智能家庭网络:小型家庭网络和大型商业规模网络。实验结果表明,基于群算法的漏洞检测速度明显快于线性算法。所提出的研究结果支持这样一种情况,即网络中的自主和基于群体的渗透测试可用于在未来呈现更安全的物联网网络。这种方法可能会影响拥有智能家庭网络的私人家庭、工业物联网(IIoT)内的设置和军事环境。
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Security Awareness in Smart Homes and Internet of Things Networks through Swarm-Based Cybersecurity Penetration Testing
Internet of Things (IoT) devices are common in today’s computer networks. These devices can be computationally powerful, yet prone to cybersecurity exploitation. To remedy these growing security weaknesses, this work proposes a new artificial intelligence method that makes these IoT networks safer through the use of autonomous, swarm-based cybersecurity penetration testing. In this work, the introduced Particle Swarm Optimization (PSO) penetration testing technique is compared against traditional linear and queue-based approaches to find vulnerabilities in smart homes and IoT networks. To evaluate the effectiveness of the PSO approach, a network simulator is used to simulate smart home networks of two scales: a small, home network and a large, commercial-sized network. These experiments demonstrate that the swarm-based algorithms detect vulnerabilities significantly faster than the linear algorithms. The presented findings support the case that autonomous and swarm-based penetration testing in a network could be used to render more secure IoT networks in the future. This approach can affect private households with smart home networks, settings within the Industrial Internet of Things (IIoT), and military environments.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
期刊最新文献
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