Android Malware Classification Using Machine Learning and Bio-Inspired Optimisation Algorithms

Pye, Jack, Issac, Biju, Rafiq, Husnain and Aslam, Nauman (2020) Android Malware Classification Using Machine Learning and Bio-Inspired Optimisation Algorithms. In: 4th International Workshop on Cyberspace Security (IWCSS 2020): The 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2020), 29 Dec 2020 - 1 Jan 2021, Guangzhou, China. (In Press)

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Abstract

In recent years the number and sophistication of Android malware have increased dramatically. A prototype framework which uses static analysis methods for classification is proposed which employs two feature sets to classify Android malware, permissions declared in the AndroidManifest.xml and Android classes used from the Classes.dex file. The extracted features were then used to train a variety of machine learning algorithms including Random Forest, SGD, SVM and Neural networks. Each machine learning algorithm was subsequently optimised using optimisation algorithms, including the use of bio-inspired optimisation algorithms such as Particle Swarm Optimisation, Artificial Bee Colony optimisation (ABC), Firefly optimisation and Genetic algorithm. The prototype framework was tested and evaluated using three datasets. It achieved a good accuracy of 95.7 percent by using SVM and ABC optimisation for the CICAndMal2019 dataset, 94.9 percent accuracy (with f1- score of 96.7 percent) using Neural network for the KuafuDet dataset and 99.6 percent accuracy using an SGD classifier for the Andro-Dump dataset. The accuracy could be further improved through better feature selection.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Android Malware Detection, Machine Learning, Optimisation, Bio-inspired optimisation
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: John Coen
Date Deposited: 11 Nov 2020 09:35
Last Modified: 11 Nov 2020 14:15
URI: http://nrl.northumbria.ac.uk/id/eprint/44730

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