2019/10/23 by Nikola Milosevic, Nikola Milošević, Milosevic, Nikola +2 · 2 voices
Computer Science · #Advanced Malware Detection Techniques #Anomaly Detection Techniques and Applications #Computers and Society (cs.CY) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection #cs.CR #cs.CY #cs.LG
paper · pdf · doi:10.48550/arxiv.1910.10660
openalex publication_date 2019/10/23 · arxiv published 2019/10/23 · arxiv updated 2019/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the past decade, the cyber-crime related to mobile devices has increased. Mobile devices, especially the ones running on Android operating system are particularly interesting to malware creators, as the users often keep the biggest amount of personal information on their mobile devices, such as their contacts, social media profiles, emails, and bank accounts. Both dynamic and static malware analysis is necessary to prevent and detect malware, as both techniques have their benefits and shortcomings. In this paper, we propose a deep learning technique that relies on LSTM and encoder-decoder neural network architectures for dynamic malware analysis based on CPU, memory and battery usage. The proposed system is able to detect and notify users about anomalies in system that is likely consequence of malware behaviour. The method was implemented as a part of OWASP Seraphimdroids anti-malware mechanism and notifies users about anomalies on their devices. The method proved to perform with an F1-score of 79.2%.