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10.1109/ARES.2013.32guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
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The Big Four - What We Did Wrong in Advanced Persistent Threat Detection?

Published: 02 September 2013 Publication History

Abstract

As both the number and the complexity of cyber-attacks continuously increase, it is becoming evident that current security mechanisms have limited success in detecting sophisticated threats. Stuxnet, Duqu, Flame and Red October have troubled the security community due to their severe complexity and their ability to evade detection - in some cases for several years. The significant technical and financial resources needed for orchestrating such complex attacks are a clear indication that perpetrators are well organized and, likely, working under a state umbrella. In this paper we perform a technical analysis of these advanced persistent threats, highlighting particular characteristics and identifying common patterns and techniques. We also focus on the issues that enabled the malware authors to evade detection from a wide range of security solutions and propose technical countermeasures for strengthening our defenses against similar threats.

Cited By

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  • (2024)If It Looks Like a Rootkit and Deceives Like a Rootkit: A Critical Examination of Kernel-Level Anti-Cheat SystemsProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3670433(1-11)Online publication date: 30-Jul-2024
  • (2024)Identifying Authorship in Malicious Binaries: Features, Challenges & DatasetsACM Computing Surveys10.1145/365397356:8(1-36)Online publication date: 26-Mar-2024
  • (2022)Two statistical traffic features for certain APT group identificationJournal of Information Security and Applications10.1016/j.jisa.2022.10320767:COnline publication date: 1-Jun-2022
  • Show More Cited By
  1. The Big Four - What We Did Wrong in Advanced Persistent Threat Detection?

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    Information & Contributors

    Information

    Published In

    cover image Guide Proceedings
    ARES '13: Proceedings of the 2013 International Conference on Availability, Reliability and Security
    September 2013
    846 pages
    ISBN:9780769550084

    Publisher

    IEEE Computer Society

    United States

    Publication History

    Published: 02 September 2013

    Author Tags

    1. Advanced Persistent Threat
    2. Duqu
    3. Exploitation
    4. Flame
    5. Red October
    6. Stuxnet
    7. Zero Day

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    • (2024)If It Looks Like a Rootkit and Deceives Like a Rootkit: A Critical Examination of Kernel-Level Anti-Cheat SystemsProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3670433(1-11)Online publication date: 30-Jul-2024
    • (2024)Identifying Authorship in Malicious Binaries: Features, Challenges & DatasetsACM Computing Surveys10.1145/365397356:8(1-36)Online publication date: 26-Mar-2024
    • (2022)Two statistical traffic features for certain APT group identificationJournal of Information Security and Applications10.1016/j.jisa.2022.10320767:COnline publication date: 1-Jun-2022
    • (2022)APT attacks on industrial control systemsInternational Journal of Critical Infrastructure Protection10.1016/j.ijcip.2022.10052137:COnline publication date: 1-Jul-2022
    • (2022)APT beaconing detectionComputers and Security10.1016/j.cose.2022.102875122:COnline publication date: 1-Nov-2022
    • (2021)Out of Many We are OneProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452784(261-273)Online publication date: 9-Jun-2021
    • (2020)Operational design for advanced persistent threatsProceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings10.1145/3417990.3420044(1-10)Online publication date: 16-Oct-2020
    • (2019)Discussing the Feasibility of Acoustic Sensors for Side Channel-aided Industrial Intrusion DetectionProceedings of the Third Central European Cybersecurity Conference10.1145/3360664.3360667(1-4)Online publication date: 14-Nov-2019
    • (2019)Malware Detection on Highly Imbalanced Data through Sequence ModelingProceedings of the 12th ACM Workshop on Artificial Intelligence and Security10.1145/3338501.3357374(37-48)Online publication date: 11-Nov-2019
    • (2018)The challenge of detecting sophisticated attacksProceedings of the 13th International Conference on Availability, Reliability and Security10.1145/3230833.3233280(1-9)Online publication date: 27-Aug-2018
    • Show More Cited By

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