Abstract
In recent years, malware attacks against data and information is considered as a serious cyber threat in the industries and organizations. Cyber criminals attempt to attack and gain access to computer networks or systems of many organizations especially in the healthcare industry by malicious software or malware to breach or manipulate sensitive data, or to make illegal financial transactions. Healthcare organizations nowadays preserve huge sensitive data into virtual and cloud environments. As a result, targeted attacks on healthcare data have become more common in recent years. Hence, protecting the medical data is a big concern in the healthcare industry. This paper proposes an effective approach for malware detection and classification using machine learning techniques. The proposed scheme can uncover targeted attacks and stop spear phishing attacks on healthcare records by detecting advanced malware and attacker behavior and deliver custom sandbox analysis to identify malware. In this work, we employ dynamic features in order to achieve high accuracy in malware detection. Experimental results support the superior performance and effectiveness of the proposed method over similar approaches.
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Chowdhury, M., Jahan, S., Islam, R., Gao, J. (2018). Malware Detection for Healthcare Data Security. In: Beyah, R., Chang, B., Li, Y., Zhu, S. (eds) Security and Privacy in Communication Networks. SecureComm 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-030-01704-0_22
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