Effective Android Malware Detection using Emotional Reactions to Sudden Events

Android devices are providing about 70% of the web traffic. Therefore, the security of the android devices is one of the major factors impacting the web security. Autonomous detection of the malware infecting Android devices using machine learning methods can act as a scalable solution for security provision on smartphones. This study aims to introduce an innovative approach for detecting mobile phone malware by leveraging users' emotional reactions and interactions with their devices during sudden and unpredictable events. Traditional mobile malware detection methods that rely on permissions and API calls have extensively been researched, yet they often overlook human elements such as emotions and their potential implications in this context. The methodology proposed in this research involves capturing users' reactive behaviors to unexpected events using Natural Language Processing (NLP), analyzing their interactive patterns with mobile phones through clustering techniques, and employing machine learning algorithms and classification methods for malware detection. The experimental results show that the proposed method can provide an accuracy of more than 96% which provides an efficient tool for Android and web security.