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This work explores the use of machine learning techniques on anInternet-of-Things firmware dataset to detect malicious attempts to infect edgedevices or subsequently corrupt an entire network. Firmware updates areuncommon in IoT devices; hence, they abound with vulnerabilities. Attacksagainst such devices can go unnoticed, and users can become a weak point insecurity. Malware can cause DDoS attacks and even spy on sensitive areas likepeoples' homes. To help mitigate this threat, this paper employs a number ofmachine learning algorithms to classify IoT firmware and the best performingmodels are reported. In a general comparison, the top three algorithms areGradient Boosting, Logistic Regression, and Random Forest classifiers. Deeplearning approaches including Convolutional and Fully Connected Neural Networkswith both experimental and proven successful architectures are also explored.

A Survey of Machine Learning Algorithms for Detecting Malware in IoT Firmware