Real-time detection of global cyberthreat based on darknet by estimating anomalous synchronization using graphical lasso

Chansu Han, Jumpei Shimamura, Takeshi Takahashi, Daisuke Inoue, Junichi Takeuchi, Koji Nakao

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)2113-2124
Number of pages12
JournalIEICE Transactions on Information and Systems
VolumeE103D
Issue number10
DOIs
Publication statusPublished - Oct 1 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Real-time detection of global cyberthreat based on darknet by estimating anomalous synchronization using graphical lasso'. Together they form a unique fingerprint.

Cite this