| Title | An Effective Android Ransomware Detection Through Multi-Factor Feature Filtration and Recurrent Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Bibi, Iram, Akhunzada, Adnan, Malik, Jahanzaib, Ahmed, Ghufran, Raza, Mohsin |
| Conference Name | 2019 UK/ China Emerging Technologies (UCET) |
| Date Published | aug |
| Keywords | Android environment, Android malware, Android malware dataset, Android ransomware detection, composability, Deep Learning, deep learning-based malware detection model, defense mechanisms, digital forensics, feature extraction, feature filtration techniques, feature selection, feature selection algorithms, filtration, forensic analysis, invasive software, learning (artificial intelligence), Long short-term memory, majority voting process, malware analysis, Metrics, mobile computing, multifactor feature filtration, pubcrawl, ransomware, recurrent neural nets, recurrent neural network, Resiliency, security, smart devices, smart phones |
| Abstract | With the increasing diversity of Android malware, the effectiveness of conventional defense mechanisms are at risk. This situation has endorsed a notable interest in the improvement of the exactitude and scalability of malware detection for smart devices. In this study, we have proposed an effective deep learning-based malware detection model for competent and improved ransomware detection in Android environment by looking at the algorithm of Long Short-Term Memory (LSTM). The feature selection has been done using 8 different feature selection algorithms. The 19 important features are selected through simple majority voting process by comparing results of all feature filtration techniques. The proposed algorithm is evaluated using android malware dataset (CI-CAndMal2017) and standard performance parameters. The proposed model outperforms with 97.08% detection accuracy. Based on outstanding performance, we endorse our proposed algorithm to be efficient in malware and forensic analysis. |
| DOI | 10.1109/UCET.2019.8881884 |
| Citation Key | bibi_effective_2019 |
An Effective Android Ransomware Detection Through Multi-Factor Feature Filtration and Recurrent Neural Network