Evaluation of machine learning classifiers for mobile malware detection

By Fairuz Amalina, Ali Feizollah, Nor Badrul Anuar, Abdullah Bin Gani. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2014

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Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, methodology): “KNN Intrusion/Malware Detection [147],[162]”
IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security? (2018, methodology): “Naive Bayes can be applied by IoT devices in intrusion detection [9], and random forest classifier can be used to detect malware[14].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “This trend is reflected in machine learning (ML) based solutions to problems ranging from radio access technology (RAT) selection [8] to malware detection [9], as well as the development of networked systems that support machine learning practices (e.”
A Survey on Distributed Machine Learning (2019, positive): “– Perceptrons [105] are binary classiers that label input vectors as ‘active’ or ‘inactive’.”
Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, methodology): “Also, an IoT device can utilize K-NNs and random forest classifiers to devise a malware-detection model [29].”