Android malware category detection using a novel feature vector‑based machine learning model  被引量:1

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作  者:Hashida Haidros Rahima Manzil S.Manohar Naik 

机构地区:[1]Department of Computer Science,Central University of Kerala,Kasaragod 671316,Kerala,India

出  处:《Cybersecurity》2023年第3期74-84,共11页网络空间安全科学与技术(英文)

摘  要:Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones.Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones.The researchers have conducted numerous studies on the detection of Android malware,but the majority of the works are based on the detection of generic Android malware.The detection based on malware categories will provide more insights about the malicious patterns of the malware.Therefore,this paper presents a detection solution for different Android malware categories,including adware,banking,SMS malware,and riskware.In this paper,a novel Huffman encoding-based feature vector generation technique is proposed.The experiments have proved that this novel approach significantly improves the efficiency of the detection model.This method makes use of system call frequencies as features to extract malware’s dynamic behavior patterns.The proposed model was evaluated using machine learning and deep learning methods.The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%.

关 键 词:Android malware Dynamic analysis Malware category Huffman codin 

分 类 号:TP309[自动化与计算机技术—计算机系统结构] TP181[自动化与计算机技术—计算机科学与技术]

 

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