| Title | DynaMalDroid: Dynamic Analysis-Based Detection Framework for Android Malware Using Machine Learning Techniques |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Haidros Rahima Manzil, Hashida, Naik S, Manohar |
| Conference Name | 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES) |
| Date Published | dec |
| Keywords | Android malware, Deep Learning, dynamic analysis, feature extraction, Human Behavior, Knowledge engineering, Malware, malware analysis, Metrics, Operating systems, privacy, pubcrawl, resilience, Resiliency, Support vector machines, system calls, Technological innovation, vulnerabilities |
| Abstract | Android malware is continuously evolving at an alarming rate due to the growing vulnerabilities. This demands more effective malware detection methods. This paper presents DynaMalDroid, a dynamic analysis-based framework to detect malicious applications in the Android platform. The proposed framework contains three modules: dynamic analysis, feature engineering, and detection. We utilized the well-known CICMalDroid2020 dataset, and the system calls of apps are extracted through dynamic analysis. We trained our proposed model to recognize malware by selecting features obtained through the feature engineering module. Further, with these selected features, the detection module applies different Machine Learning classifiers like Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Naive-Bayes, K-Nearest Neighbour, and AdaBoost, to recognize whether an application is malicious or not. The experiments have shown that several classifiers have demonstrated excellent performance and have an accuracy of up to 99%. The models with Support Vector Machine and AdaBoost classifiers have provided better detection accuracy of 99.3% and 99.5%, respectively. |
| DOI | 10.1109/ICKECS56523.2022.10060106 |
| Citation Key | haidros_rahima_manzil_dynamaldroid_2022 |
DynaMalDroid: Dynamic Analysis-Based Detection Framework for Android Malware Using Machine Learning Techniques