abstract

Detecting Malicious Domains via Graph Inference

Authors:

Pratyusa Manadhata,

Sandeep Yadav,

Prasad Rao,

William HorneAuthors Info & Claims

AISec '14: Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop

Pages 59 - 60

https://doi.org/10.1145/2666652.2666659

Published: 07 November 2014 Publication History

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Abstract

No abstract available.

References

[1]

J. Pearl. Reverend Bayes on inference engines: a distributed hierarchical approach. In in Proceedings of the National Conference on Artificial Intelligence, 1982.

Google Scholar

[2]

J. Yedida, W. Freeman, and Y. Weiss. Understanding Belief Propagation and its Generalizations. Exploring Artificial Intelligence in the New Millennium, 2003.

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Cited By

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Zhang SHuang LZhang ZXu WYang WLiu L(2025)RMD-Graph: Adversarial Attacks Resisting Malicious Domain Detection Based on Dual DenoisingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352079837:3(1394-1410)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3520798
Chiang PTsai S(2024)Detection of Malicious Domains With Concept Drift Using Ensemble LearningIEEE Transactions on Network and Service Management10.1109/TNSM.2024.343551621:6(6796-6809)Online publication date: Dec-2024
https://doi.org/10.1109/TNSM.2024.3435516
Nazzal MKhalil IKhreishah APhan NMa Y(2024)Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00006(1236-1254)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00006
Show More Cited By

Index Terms

Detecting Malicious Domains via Graph Inference
1. Security and privacy
  1. Network security
2. Social and professional topics
  1. Computing / technology policy
    1. Computer crime

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Information & Contributors

Information

Published In

AISec '14: Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop

November 2014

134 pages

ISBN:9781450331531

DOI:10.1145/2666652

General Chair:
Gail-Joon Ahn
Arizona State University, USA
,
Program Chairs:
Christos Dimitrakakis
Chalmers University of Technology, Sweden
,
Aikaterini Mitrokotsa
Chalmers University of Technology, Sweden
,
Benjamin I.P. Rubinstein
University of Melbourne, Australia

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2014

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Conference

CCS'14

Sponsor:

SIGSAC

CCS'14: 2014 ACM SIGSAC Conference on Computer and Communications Security

November 7, 2014

Arizona, Scottsdale, USA

Acceptance Rates

AISec '14 Paper Acceptance Rate 12 of 24 submissions, 50%;

Overall Acceptance Rate 94 of 231 submissions, 41%

Upcoming Conference

CCS '25

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cited By

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Zhang SHuang LZhang ZXu WYang WLiu L(2025)RMD-Graph: Adversarial Attacks Resisting Malicious Domain Detection Based on Dual DenoisingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352079837:3(1394-1410)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3520798
Chiang PTsai S(2024)Detection of Malicious Domains With Concept Drift Using Ensemble LearningIEEE Transactions on Network and Service Management10.1109/TNSM.2024.343551621:6(6796-6809)Online publication date: Dec-2024
https://doi.org/10.1109/TNSM.2024.3435516
Nazzal MKhalil IKhreishah APhan NMa Y(2024)Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00006(1236-1254)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00006
Fang XMa XWang CNiu YMa LGao TYang R(2024)Detecting and Exploring Malicious Websites through Multi-Message Passing Heterogeneous Neural Network2024 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC61673.2024.10733612(1-7)Online publication date: 26-Jun-2024
https://doi.org/10.1109/ISCC61673.2024.10733612
Wang YXiao RSun JJin S(2024)MC-Det: Multi-channel representation fusion for malicious domain name detectionComputer Networks10.1016/j.comnet.2024.110847255(110847)Online publication date: Dec-2024
https://doi.org/10.1016/j.comnet.2024.110847
Xu XWang HLal AGunter CLi B(2023)EDoG: Adversarial Edge Detection For Graph Neural Networks2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)10.1109/SaTML54575.2023.00027(291-305)Online publication date: Feb-2023
https://doi.org/10.1109/SaTML54575.2023.00027
T. N. NPramod D(2023)Insider Intrusion Detection Techniques: A State-of-the-Art ReviewJournal of Computer Information Systems10.1080/08874417.2023.217533764:1(106-123)Online publication date: 14-Feb-2023
https://doi.org/10.1080/08874417.2023.2175337
QIN HYANG JLI XMA HLUO ZGUO Q(2022)Research on DNS anomaly detection technology based on multiple featuresJournal of Shenzhen University Science and Engineering10.3724/SP.J.1249.2020.9903637:Z1(36-43)Online publication date: 14-Oct-2022
https://doi.org/10.3724/SP.J.1249.2020.99036
Kim TPark NHong JKim SYin HStavrou ACremers CShi E(2022)Phishing URL DetectionProceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security10.1145/3548606.3560615(1769-1782)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3548606.3560615
AlSabah MNabeel MBoshmaf YChoo E(2022)Content-Agnostic Detection of Phishing Domains using Certificate Transparency and Passive DNSProceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3545948.3545958(446-459)Online publication date: 26-Oct-2022
https://dl.acm.org/doi/10.1145/3545948.3545958
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Following Passive DNS Traces to Detect Stealthy Malicious Domains Via Graph Inference