A Survey of Machine Learning Algorithms for Detecting Malware in IoT Firmware
It addresses security vulnerabilities in IoT devices, which are prone to attacks due to infrequent firmware updates, but the approach is incremental as it uses existing methods on new data.
This paper tackles the problem of detecting malware in IoT firmware by applying various machine learning algorithms, reporting that Gradient Boosting, Logistic Regression, and Random Forest classifiers performed best in classification tasks.
This work explores the use of machine learning techniques on an Internet-of-Things firmware dataset to detect malicious attempts to infect edge devices or subsequently corrupt an entire network. Firmware updates are uncommon in IoT devices; hence, they abound with vulnerabilities. Attacks against such devices can go unnoticed, and users can become a weak point in security. Malware can cause DDoS attacks and even spy on sensitive areas like peoples' homes. To help mitigate this threat, this paper employs a number of machine learning algorithms to classify IoT firmware and the best performing models are reported. In a general comparison, the top three algorithms are Gradient Boosting, Logistic Regression, and Random Forest classifiers. Deep learning approaches including Convolutional and Fully Connected Neural Networks with both experimental and proven successful architectures are also explored.