A Machine Learning Approach to Android Malware Detection

By Justin Sahs, Latifur R. Khan. European Intelligence and Security Informatics Conference, 2012

371 citations recorded by the source corpus.

How researchers see this paper

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Machine learning aided Android malware classification (2017, methodology): “Sahs and Khan used Androguard to obtain permissions and control flow graphs of Android apps and created a SVM-based machine learning model to classify Android malware [21].”
MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis (2015, background): “[39] also include features from the control flow”
Motivating the Rules of the Game for Adversarial Example Research (2018, background): “…security has a long history and our critique does not apply to some existing work, for example, on spam detection [73] (this uses an l 0 -constrained attack model but provides a realistic justiﬁcation for it), malware detection [74], and securing network intrusion detection systems [75, 76].”
Contextual Weisfeiler-Lehman graph kernel for malware detection (2016, background): “Recently, three approaches [3], [6] and [9], have successfully demonstrated using these general purpose graph kernels for malware detection.”
Efficient Detection of Zero-day Android Malware Using Normalized Bernoulli Naive Bayes (2015, result): “In [12], only permissions from AndroidManifest.”