Ain Shams Engineering Journal · 2024 · 11 citations · 20 references
ComputingComputer ScienceCybersecurityComputer SecurityMachine LearningMalware AnalysisBehavioral AnalysisClassificationEnsemble LearningEnsemble MethodsMalware ClassificationMobile Device SecurityAndroid Malware AnalysisAndroid Malware ClassificationEnsemble Malware ClassificationFeature Extraction For Malware ClassificationMajority VotingMalware DetectionMalware Feature ExtractionMalware Traffic Analysis
This paper introduces the Systematic mAlware detection in android (STAR) technique designed to enhance accuracy in identifying and classifying Android malware, addressing significant concerns regarding device security and data privacy. The STAR method involves comprehensive data collection from diverse datasets, rigorous preprocessing for data quality improvement, and feature extraction using Principal Component Analysis (PCA). Butterfly optimization ensures selection of pertinent features, while ensemble classifiers including Bagging, AdaBoost, and LogitBoost are employed for robust model creation. Final classification is achieved via majority voting. Experimental validation demonstrates that STAR outperforms existing techniques such as ERBE, De-LADY, and MSFDROID, achieving detection rates 4.34 %, 1.41 %, and 2.52 % higher respectively. This innovative approach underscores its potential in mitigating the evolving threat landscape of Android malware, offering a promising avenue for enhancing mobile app security.
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IoMT Malware Detection Approaches: Analysis and Research Challenges
Mohammad Wazid, Ashok Kumar Das, Joel J. P. C. Rodrigues et al. · IEEE Access · 2019 · 189 citations · Full text