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Malware Detection: Harmony of Fuzzy and Firefly (FF-MD)
Vahide Nida Kiliç and Esra Saraç Eşsiz

Android Operating System an (OS) is open-source, easy to use, and user-friendly mobile OS. In this way, it is very preferred. As a result, it becomes the target of malicious people. Applications installed on the Android OS from the Google Play Store or by third-party application providers, also known as Android Package Files (APKs), may contain malicious software. So far, a variety of analyzes and detections have been made to detect such malware. While detecting malware, good results have been obtained with various methods, but malicious people have developed methods of hiding themselves against these methods. We propose a new feature selection method based on Firefly Optimization Algorithm (FOA) with the Fuzzy Set-Based (FSB) weighting method. The proposed method performs better than traditional feature selection methods with fewer features. The experimental results of this study proved that FOA is an acceptable optimization algorithm for feature selection to detect malware in terms of classification performance and classification runtime. In addition, experimental evaluation of TFIDF and FSB weighting methods indicates the effectiveness of the FSB weighting with a full feature set.

Keywords: Malware detection, android operating system, fuzzy set-based weighting, firefly optimization algorithm, feature selection, data mining

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