Malware Analysis and Detection using Machine Learning

Author(s)	Fauzia Rehman Aziz ur Rehman, Prof. Dr. Sarika Jadhav
Country	India
Abstract	The rapid rise of Android applications has resulted in an increase in the number of malicious applications. This has provoked concerns regarding user privacy and data and device security. One primary method of addressing the problematic Android malwares is through their detection. However, due to a number of reasons, it is not an easy task. One of the primary reasons is due the dimensions of network flows and the variances in behaviors of malicious applications. This research, therefore, uses a ML-method and Network flow in aids of Android malware detection. The data set contains a total of 4000 instances of network traffic data. Comprehensive data sets include source and destination IP and Port numbers, Protocol, Time in the flow, Number of packets, Number of inter-arrival times, flow flags, and Statistical count of the mean, standard deviation, variance, and a count of the flags.
Keywords	Android APK, malware detection, Machine learning, random forests, software security
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-11

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International Journal For Multidisciplinary Research