Mobile Spyware Identification and Categorization: A Systematic Review

Abstract

Smartphones have revolutionized the way we live, work, and interact with the world. They have become indispensable companions, seamlessly integrating into our daily routines. However, with this pervasive usage comes a growing security concern. Mobile phones are increasingly becoming targets of cyber-attacks, with more than 26,000 attacks happening daily. Among these threats, spyware is one of the most prevalent and insidious threat. Researchers have explored various techniques for identifying and categorizing mobile spyware to address this issue. These efforts are crucial for enhancing the security of our mobile devices and protecting our sensitive data from prying eyes. In this paper, we have conducted a comprehensive survey of the existing techniques and summarized their strengths and limitations. Our analysis encompasses a range of approaches, from signature-based detection to machine learning-based classification. We also explore the latest advancements in behavioral analysis and intrusion detection systems. By consolidating this knowledge, we provide a valuable reference point for future research on mobile spyware detection and prevention. In conclusion, this paper highlights mobile security’s critical role in our digital lives. It underscores the importance of ongoing research and innovation in mobile security to safeguard our personal information and prevent cyber-attacks.