2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIFS-5.5
Paper Title DETECTION OF MALICIOUS DNS AND WEB SERVERS USING GRAPH-BASED APPROACHES
Authors Jinyuan Jia, Duke University, United States; Zheng Dong, Jie Li, Microsoft Corporation, United States; Jack W. Stokes, Microsoft Research, United States
SessionIFS-5: Privacy and Information Security
LocationGather.Town
Session Time:Thursday, 10 June, 15:30 - 16:15
Presentation Time:Thursday, 10 June, 15:30 - 16:15
Presentation Poster
Topic Information Forensics and Security: [CYB] Cybersecurity
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The DNS hijacking attack represents a significant threat to users. In this type of attack, a malicious DNS server redirects a victim domain to an attacker-controlled web server. Existing defenses are not scalable and have not been widely deployed. In this work, we propose both unsupervised and semi-supervised defenses based on the available knowledge of the defender. Specifically, our unsupervised defense is a graph-based detection approach employing a new variant of the community detection algorithm. When the IP addresses of several compromised DNS servers are available, we also propose a semi-supervised defense for the detection of compromised or malicious web servers which host the web content. We evaluate our defenses on a real-world attack. The experimental results show that our defenses can successfully identify these malicious web servers and/or DNS server IPs. Moreover, we find that a deep learning-based algorithm, i.e., node2vec, outperforms one which employs belief propagation.