Taher, F.; AlFandi, O.; Al-kfairy, M.; Al Hamadi, H.; Alrabaee, S. DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection. Appl. Sci.2023, 13, 7720.
Taher, F.; AlFandi, O.; Al-kfairy, M.; Al Hamadi, H.; Alrabaee, S. DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection. Appl. Sci. 2023, 13, 7720.
Taher, F.; AlFandi, O.; Al-kfairy, M.; Al Hamadi, H.; Alrabaee, S. DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection. Appl. Sci.2023, 13, 7720.
Taher, F.; AlFandi, O.; Al-kfairy, M.; Al Hamadi, H.; Alrabaee, S. DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection. Appl. Sci. 2023, 13, 7720.
Abstract
Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffec-tive. Although prior writers have proposed numerous high-quality approaches, static and dy-namic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tai-lored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves ap-plying a newly proposed detection model to classify android apps; this model uses a neural net-work optimized with an improved version of HHO. Application of binary and multi-class classi-fication is used, with binary classification for benign and malware apps and multi-class classifica-tion for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classifi-cation. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.