在现实物联网系统中进行恶意软件检测的 AIS:挑战与机遇

Network Pub Date : 2023-11-16 DOI:10.3390/network3040023
Hadeel Alrubayyi, G. Goteng, Mona Jaber
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引用次数: 0

摘要

随着数字世界的扩展,物联网(IoT)设备的数量也在急剧增加。物联网设备的计算能力有限,内存也很小。因此,现有的复杂安全方法并不适合检测物联网网络中的未知恶意软件攻击。面对越来越难以预测的创新型网络攻击,这已成为一个重大问题。在这种情况下,人工免疫系统(AIS)作为一种有效的恶意软件检测机制应运而生,它对计算和内存的要求较低。在这项研究中,我们首先使用多种不同类型恶意软件攻击的数据集验证了最近的人工免疫系统解决方案的恶意软件检测结果。接下来,我们研究了在现实实施场景下有前景的 AIS 解决方案的潜在优势和局限性。我们模仿现实生活中的物联网系统架构设计了一个现实的物联网框架。目的是评估 AIS 解决方案在系统约束条件下的性能。我们证明,AIS 解决方案能在最具挑战性的条件下成功检测未知恶意软件。此外,不同系统架构下的系统结果表明,AIS 解决方案具有在物联网设备之间迁移学习的能力。在网络中设备高度受限的情况下,转移学习是一个关键特征。更重要的是,这项工作突出表明,以前公布的 AIS 性能结果是在模拟环境中获得的,不能照单全收。实际上,在最受限制的设计系统中,AIS 对物联网系统的恶意软件检测准确率为 91%,而模拟实验中报告的准确率为 99%。
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AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
With the expansion of the digital world, the number of Internet of things (IoT) devices is evolving dramatically. IoT devices have limited computational power and a small memory. Consequently, existing and complex security methods are not suitable to detect unknown malware attacks in IoT networks. This has become a major concern in the advent of increasingly unpredictable and innovative cyberattacks. In this context, artificial immune systems (AISs) have emerged as an effective malware detection mechanism with low requirements for computation and memory. In this research, we first validate the malware detection results of a recent AIS solution using multiple datasets with different types of malware attacks. Next, we examine the potential gains and limitations of promising AIS solutions under realistic implementation scenarios. We design a realistic IoT framework mimicking real-life IoT system architectures. The objective is to evaluate the AIS solutions’ performance with regard to the system constraints. We demonstrate that AIS solutions succeed in detecting unknown malware in the most challenging conditions. Furthermore, the systemic results with different system architectures reveal the AIS solutions’ ability to transfer learning between IoT devices. Transfer learning is a pivotal feature in the presence of highly constrained devices in the network. More importantly, this work highlights that previously published AIS performance results, which were obtained in a simulation environment, cannot be taken at face value. In reality, AIS’s malware detection accuracy for IoT systems is 91% in the most restricted designed system compared to the 99% accuracy rate reported in the simulation experiment.
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