Android Malware Detection Using Parallel Machine Learning Classifiers
Mobile malware has continued to grow at an alarming rate despite on-going efforts towards mitigating the problem. This has been particularly noticeable on Android due to its being an open platform that has subsequently overtaken other platforms in the share of the mobile smart devices market. Hence, incentivizing a new wave of emerging Android malware sophisticated enough to evade most common detection methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.
Code (0)
등록된 구현이 없습니다.
Tasks
Android Malware DetectionBIG-bench Machine LearningGeneral ClassificationMalware DetectionSimilar Papers 제목 키워드 기반
Android Malware Detection Using Machine Learning on Image Patterns
Android platform has been targeted by cyber-criminals due to the increase number of Android users in 2017. More than 8,000 Android malware were identified everyday making it is difficult for the malware analyst to detect…
Android Malware DetectionMalware DetectionAdversarial Patterns: Building Robust Android Malware Classifiers
Machine learning models are increasingly being adopted across various fields, such as medicine, business, autonomous vehicles, and cybersecurity, to analyze vast amounts of data, detect patterns, and make predictions or …
Autonomous VehiclesBIG-bench Machine LearningMalware DetectionAndroid Malware Characterization using Metadata and Machine Learning Techniques
Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses in…
BIG-bench Machine LearningIoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense
Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to …
Graph Neural NetworkAdversarial DefenseMalware DetectionDetection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering
Today anti-malware community is facing challenges due to the ever-increasing sophistication and volume of malware attacks developed by adversaries. Traditional malware detection mechanisms are not able to cope-up with ne…
Android Malware DetectionBIG-bench Machine LearningClusteringMalware Detection