Real-time detection of malware activities by analyzing darknet traffic using graphical lasso

Recent malware evolutions have rendered cyberspace less secure, and we are currently witnessing an increasing number of severe security incidents. To minimize the impact of malware activities, it is important to detect them promptly and precisely. We have been working on this issue by monitoring traffic coming into unused IP address spaces, i.e., the darknet. On our darknet, Internet-wide scans from malware are observed as if they are coordinated or working cooperatively. Based on this observation, our earlier method monitored network traffic arriving at our darknet, estimated the degree of cooperation between each pair of the source hosts, and detected significant changes in cooperation among source hosts as a sign of newly activated malware activities. However, this method does not work in real time, and thus, it is impractical. In this study, we extend our earlier work and propose an online processing algorithm, making it possible to detect malware activities in real time. In our evaluation, we measure the detection performance of the proposed method with our proof-of-concept implementation to demonstrate its feasibility and effectiveness in terms of detecting the rise of new malware activities in real time.