An efficient malware detection approach with feature weighting based on Harris Hawks optimization

This paper introduces and tests a novel machine learning approach to detect Android malware. The proposed approach is composed of Support Vector Machine (SVM) classifier and Harris Hawks Optimization (HHO) algorithm. More specifically, the role of HHO algorithm is to optimize SVM classifier hyperpar...

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Bibliographic Details
Published in:Cluster computing 2022-08, Vol.25 (4), p.2369-2387
Main Authors: Alzubi, Omar A., Alzubi, Jafar A., Al-Zoubi, Ala’ M., Hassonah, Mohammad A., Kose, Utku
Format: Article
Language:English
Subjects:
Algorithms
Boolean
Classifiers
Computer Communication Networks
Computer Science
Datasets
Heuristic methods
Machine learning
Malware
Operating Systems
Optimization
Processor Architectures
Ransomware
Smartphones
Social networks
Support vector machines
Weighting
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Summary:This paper introduces and tests a novel machine learning approach to detect Android malware. The proposed approach is composed of Support Vector Machine (SVM) classifier and Harris Hawks Optimization (HHO) algorithm. More specifically, the role of HHO algorithm is to optimize SVM classifier hyperparameters while the SVM performs the classification of malware based on the best-chosen model, as well as producing the optimal solution for weighting the features. The effectiveness of the proposed approach and the ability to increase detection performance are demonstrated by scientific testing using CICMalAnal2017 sampled datasets. We test our method and its robustness on five sampled datasets and achieved the best results in most datasets and measures when compared with other approaches. We also illustrate the ability of the proposed approach to measure the significance of each feature. In addition, we provide deep analysis of possible relationships between weighted features and the type of malware attack. The results show that the proposed approach outperforms the other metaheuristic algorithms and state-of-art classifiers.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-021-03459-1