Abstract
As cyberattacks become increasingly prevalent globally, there is a need to identify trends in these cyberattacks and take suitable countermeasures quickly. The darknet, an unused IP address space, is relatively conducive to observing and analyzing indiscriminate cyberattacks because of the absence of legitimate communication. Indiscriminate scanning activities by malware to spread their infections often show similar spatiotemporal patterns, and such trends are also observed on the darknet. To address the problem of early detection of malware activities, we focus on anomalous synchronization of spatiotemporal patterns observed in darknet traffic data. Our previous studies proposed algorithms that automatically estimate and detect anomalous spatiotemporal patterns of darknet traffic in real time by employing three independent machine learning methods. In this study, we integrated the previously proposed methods into a single framework, which we refer to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dark-TRACER</i> , and conducted quantitative experiments to evaluate its ability to detect these malware activities. We used darknet traffic data from October 2018 to October 2020 observed in our large-scale darknet sensors (up to /17 subnet scales). The results demonstrate that the weaknesses of the methods complement each other, and the proposed framework achieves an overall 100% recall rate. In addition, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dark-TRACER</i> detects the average of malware activities 153.6 days earlier than when those malware activities are revealed to the public by reputable third-party security research organizations. Finally, we evaluated the cost of human analysis to implement the proposed system and demonstrated that two analysts can perform the daily operations necessary to operate the framework in approximately 7.3 h.
Highlights
In recent years, an increasingly large number of indiscriminate cyberattacks have been observed on the Internet, and it is becoming increasingly costly to analyze these attacks
Even in case of small-scale infection activity of malware, a high degree of synchronicity is expected to occur in the associated spatiotemporal patterns, and early detection of malware activity can be realized by estimating the synchronicity and detecting anomalies. We focused on such synchronization and attempted to detect potential malware activities by estimating the group of source hosts with high synchronization in their spatiotemporal patterns on a large-scale darknet
We previously proposed the following different methods to estimate the synchronization in real time to automatically use the aforementioned algorithms and detect the source host space groups that show abnormal synchronization: Dark-GLASSO [6], [7], Dark-nonnegative matrix factorization (NMF) [8], and Dark-nonnegative Tucker decomposition (NTD) [9]
Summary
School and Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan. Resources (JPJ000254),” which was supported by the Ministry of Internal Affairs and Communications, Japan
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