Journal of Information Technology & Software Engineering
Open Access

Department of Computer Science, Heidelberg University, Baden-Württemberg, Germany

Publications

• Short Communication   
  Machine Learning for Identifying iOS Malware
  Author(s): Lisa Angelina*
  
  Smartphones have transformed into an indispensible component of our daily life. Smartphones are almost completely relied on as a communication tool, a source of information, and a source of pleasure on a social, political, and economic level. Rapid advances in information and cyber security have mandated particular attention to the privacy and security of smartphone data. Spyware detection systems have recently been created as a potential and appealing option for the privacy protection of smartphone users. Because the Android operating system is the most commonly used in the world, it is a major target for various groups interested in attacking smartphone users' privacy. This research presents a unique dataset gathered in a realistic setting using a novel data collecting approach based on a unified activity list.   The data is separated into three categori.. View More»
  DOI: 10.35248/2165-7866.22.12.306

  Abstract HTML PDF