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


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.

Publication DOI: https://doi.org/10.1145/3419103
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Internet Technology, {VOL 21, ISS 4, November 2021} http://doi.acm.org/10.1145/3419103.
Uncontrolled Keywords: Tactile Internet,Multi-agent systems,Artificial immune systems,rookits,flow-based analysis
Publication ISSN: 1557-6051
Full Text Link:
Related URLs: https://dl.acm. ... 10.1145/3419103 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-11
Published Online Date: 2021-07-16
Accepted Date: 2020-08-01
Authors: Marques, Rafael Salema
Epiphaniou, Gregory
Al-Khateeb, Haider (ORCID Profile 0000-0001-8944-123X)
Maple, Carsten
Hammoudeh, Mohammad
De Castro, Paulo Andre Lima
Dehghantanha, Ali
Choo, Kim Kwang Raymond


Export / Share Citation


Additional statistics for this record