Abstract The ever-growing expansion of smart cities and the Internet of Things (IoT) offer a promising solution to many contemporary urban challenges. However, this digital transformation also results in cyber-security loopholes which can be exploited by malicious hackers to wreak substantial digital and physical damage. Malware is the primary tool of cyber-criminals for attacking digital systems. In this paper, a multi-view ensemble threat hunting model based on Sparse Representation based Classifier (SRC) is proposed to use in IoT systems that are finding domain space in the advent of Smart Cities. An ensemble of SRCs is considered where every individual SRC classifies malware by Opcode, Bytecode and system call views of several standard IoT and Ransomware datasets. The final decision is made through weighted majority voting. SRC is employed to alleviate the complexity of the base classifiers. Experimental results verify the efficiency and robustness of the proposed model in different balanced and imbalanced environments. The proposed model outperforms all base classifiers and several well-known works in current literature.

中文翻译：

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基于集合稀疏表示的智能城市安全网络威胁搜寻

摘要 智慧城市和物联网 (IoT) 的不断扩展为许多当代城市挑战提供了有前景的解决方案。然而，这种数字化转型也会导致网络安全漏洞，恶意黑客可以利用这些漏洞造成大量的数字和物理破坏。恶意软件是网络犯罪分子攻击数字系统的主要工具。在本文中，提出了一种基于稀疏表示的分类器 (SRC) 的多视图集成威胁狩猎模型，用于在智能城市出现时寻找域空间的物联网系统。考虑了一组 SRC，其中每个单独的 SRC 通过操作码、字节码和几个标准物联网和勒索软件数据集的系统调用视图对恶意软件进行分类。最终决定通过加权多数投票做出。SRC 用于减轻基分类器的复杂性。实验结果验证了所提出模型在不同平衡和不平衡环境下的效率和鲁棒性。所提出的模型优于当前文献中的所有基分类器和一些知名作品。