Ensemble sparse representation-based cyber threat hunting for security of smart cities

摘要: Abstract The ever-growing expansion of smart cities and the Internet 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 wreak substantial physical damage. Malware is primary tool cyber-criminals for attacking systems. In paper, multi-view ensemble threat hunting model based on Sparse Representation Classifier (SRC) proposed use IoT systems that are finding domain space advent Smart Cities. An SRCs considered where every individual SRC classifies malware Opcode, Bytecode system call views several standard Ransomware datasets. final decision made through weighted majority voting. employed alleviate complexity base classifiers. Experimental verify efficiency robustness different balanced imbalanced environments. outperforms all classifiers well-known works current literature.