Detection of Botnet Command and Control Traffic by the Identification of Untrusted Destinations

Burghouwt, Pieter; Spruit, Marcel; Sips, Henk

doi:10.1007/978-3-319-23829-6_13

Cited by 1 publication

(1 citation statement)

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“…Their results indicate that statistical modelling of APT communications can successfully develop deterministic characteristics for detection. Burghouwt et al [11] proposed a novel approach to real-time detection of C2 channels based on trust of trafc destinations. In the approach, a destination can become trusted by transitivity, if its origin can be evaluated by another trusted entity.…”

Section: Flow-based Analysismentioning

confidence: 99%

A Flow-based Multi-agent Data Exfiltration Detection Architecture for Ultra-low Latency Networks

Marques

Epiphaniou

Al-Khateeb

et al. 2021

ACM Trans. Internet Technol.

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Modern network infrastructures host converged applications that demand rapid elasticity of services, increased security, and ultra-fast reaction times. The Tactile Internet promises to facilitate the delivery of these services while enabling new economies of scale for high fidelity of machine-to-machine and human-to-machine interactions. Unavoidably, critical mission systems served by the Tactile Internet manifest high demands not only for high speed and reliable communications but equally, the ability to rapidly identify and mitigate threats and vulnerabilities. This article proposes a novel Multi-Agent Data Exfiltration Detector Architecture (MADEX), inspired by the mechanisms and features present in the human immune system. MADEX seeks to identify data exfiltration activities performed by evasive and stealthy malware that hides malicious traffic from an infected host in low-latency networks. Our approach uses cross-network traffic information collected by agents to effectively identify unknown illicit connections by an operating system subverted. MADEX does not require prior knowledge of the characteristics or behavior of the malicious code or a dedicated access to a knowledge repository. We tested the performance of MADEX in terms of its capacity to handle real-time data and the sensitivity of our algorithm’s classification when exposed to malicious traffic. Experimental evaluation results show that MADEX achieved 99.97% sensitivity, 98.78% accuracy, and an error rate of 1.21% when compared to its best rivals. We created a second version of MADEX, called MADEX level 2, that further improves its overall performance with a slight increase in computational complexity. We argue for the suitability of MADEX level 1 in non-critical environments, while MADEX level 2 can be used to avoid data exfiltration in critical mission systems. To the best of our knowledge, this is the first article in the literature that addresses the detection of rootkits real-time in an agnostic way using an artificial immune system approach while it satisfies strict latency requirements.

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Section: Flow-based Analysismentioning

confidence: 99%