A federated approach to Android malware classification through Perm-Maps

Random Forest Classification for Android Malware

Classification techniques such as Support Vector Machines, K-Nearest Neighbours, Decision Trees, Logistic Regression and Naive Bayes have widely been used in the area of intrusion detection research in the security community. They are predominantly used for behaviour based detection methods (anomaly detection methods). In this paper we exclusively apply the ensemble learning algorithm Random Fo...

Classifying Android Malware through Subgraph Mining

Android Malware Clustering Through Malicious Payload Mining

Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party libraries in Android application development and the widespread use of repackaging in malware development. We design and implement an Android ma...

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Andr...

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely d...