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

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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.
Original languageEnglish
Title of host publication2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications
Subtitle of host publicationTrustCom 2020
EditorsGuojun Wang, Ryan Ko, Md Zakirul Alam Bhuiyan, Yi Pan
Place of PublicationPiscataway
PublisherIEEE
Pages1777-1882
Number of pages6
ISBN (Print)9781665403924
DOIs
Publication statusPublished - Dec 2020
Event19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2020): 4th International Workshop on Cyberspace Security (IWCSS 2020) - Guangzhou University, Guangzhou, China
Duration: 29 Dec 20201 Jan 2021
http://ieee-trustcom.org/TrustCom2020/

Conference

Conference19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2020)
CountryChina
CityGuangzhou
Period29/12/201/01/21
Internet address

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