Real-Time Detection of Global Cyberthreat Based on Darknet by Estimating Anomalous Synchronization Using Graphical Lasso

Chansu HAN  Jumpei SHIMAMURA  Takeshi TAKAHASHI  Daisuke INOUE  Jun'ichi TAKEUCHI  Koji NAKAO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.10   pp.2113-2124
Publication Date: 2020/10/01
Publicized: 2020/06/25
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDP7076
Type of Manuscript: PAPER
Category: Information Network
Keyword: 
cyberthreat,  malware,  darknet,  network security,  synchronization,  outlier detection,  real-time detection,  

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Summary: 
With the rapid evolution and increase of cyberthreats in recent years, it is necessary to detect and understand it promptly and precisely to reduce the impact of cyberthreats. A darknet, which is an unused IP address space, has a high signal-to-noise ratio, so it is easier to understand the global tendency of malicious traffic in cyberspace than other observation networks. In this paper, we aim to capture global cyberthreats in real time. Since multiple hosts infected with similar malware tend to perform similar behavior, we propose a system that estimates a degree of synchronizations from the patterns of packet transmission time among the source hosts observed in unit time of the darknet and detects anomalies in real time. In our evaluation, we perform our proof-of-concept implementation of the proposed engine to demonstrate its feasibility and effectiveness, and we detect cyberthreats with an accuracy of 97.14%. This work is the first practical trial that detects cyberthreats from in-the-wild darknet traffic regardless of new types and variants in real time, and it quantitatively evaluates the result.