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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13558],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-805708","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-security-privacy-cryptography","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-6-6","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"IEEE","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/ieeexplore.ieee.org\/document\/9415092","label_id":"243109","label":0}],"msr_related_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/12\/MaliciousDNS_ICASSP2021.pdf","id":"910932","title":"maliciousdns_icassp2021","label_id":"243118","label":0}],"msr_attachments":[{"id":910932,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2023\/01\/MaliciousDNS_ICASSP2021.pdf"}],"msr-author-ordering":[{"type":"text","value":"Jinyuan Jia","user_id":0,"rest_url":false},{"type":"text","value":"Zheng Dong","user_id":0,"rest_url":false},{"type":"text","value":"Jie Li","user_id":0,"rest_url":false},{"type":"edited_text","value":"Jack W. 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