Linear SVM-Based Android Malware Detection for Reliable IoT Services

• Published in: Journal of Applied Mathematics Vol. 2014; no. 2014; pp. 568 - 577-559
• Main Authors: Choi, Mi-Jung, Kim, Myung-Sup, Kim, Hwan-Hee, Ham, Hyo-Sik
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2014; Hindawi Publishing Corporation; John Wiley & Sons, Inc; Wiley
• Subjects: Analysis; Classifiers; Internet; Internet of Things; Machine learning; Malware; Mathematical analysis; Mobile communication systems; Mobile devices; Safety and security measures; Smartphones; Spyware; Support vector machines; United States
• ISSN: 1110-757X; 1687-0042
• DOI: 10.1155/2014/594501

Current many Internet of Things (IoT) services are monitored and controlled through smartphone applications. By combining IoT with smartphones, many convenient IoT services have been provided to users. However, there are adverse underlying effects in such services including invasion of privacy and information leakage. In most cases, mobile devices have become cluttered with important personal user information as various services and contents are provided through them. Accordingly, attackers are expanding the scope of their attacks beyond the existing PC and Internet environment into mobile devices. In this paper, we apply a linear support vector machine (SVM) to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.