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Improving intrusion detectors by crook-sourcing

Authors:

Frederico Araujo,

Gbadebo Ayoade,

Khaled Al-Naami,

Kevin W. Hamlen,

Latifur KhanAuthors Info & Claims

ACSAC '19: Proceedings of the 35th Annual Computer Security Applications Conference

Pages 245 - 256

https://doi.org/10.1145/3359789.3359822

Published: 09 December 2019 Publication History

Abstract

Conventional cyber defenses typically respond to detected attacks by rejecting them as quickly and decisively as possible; but aborted attacks are missed learning opportunities for intrusion detection. A method of reimagining cyber attacks as free sources of live training data for machine learning-based intrusion detection systems (IDSes) is proposed and evaluated. Rather than aborting attacks against legitimate services, adversarial interactions are selectively prolonged to maximize the defender's harvest of useful threat intelligence. Enhancing web services with deceptive attack-responses in this way is shown to be a powerful and practical strategy for improved detection, addressing several perennial challenges for machine learning-based IDS in the literature, including scarcity of training data, the high labeling burden for (semi-)supervised learning, encryption opacity, and concept differences between honeypot attacks and those against genuine services. By reconceptualizing software security patches as feature extraction engines, the approach conscripts attackers as free penetration testers, and coordinates multiple levels of the software stack to achieve fast, automatic, and accurate labeling of live web data streams.

Prototype implementations are showcased for two feature set models to extract security-relevant network- and system-level features from servers hosting enterprise-grade web applications. The evaluation demonstrates that the extracted data can be fed back into a network-level IDS for exceptionally accurate, yet lightweight attack detection.

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Cited By

Akbar KWang YAyoade GGao YSinghal AKhan LThuraisingham BJee K(2023)Advanced Persistent Threat Detection Using Data Provenance and Metric LearningIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.322178920:5(3957-3969)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TDSC.2022.3221789
Apruzzese GLaskov PSchneider J(2023)SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP57164.2023.00042(592-614)Online publication date: Jul-2023
https://doi.org/10.1109/EuroSP57164.2023.00042
Torre DMesadieu FChennamaneni A(2023)Deep learning techniques to detect cybersecurity attacks: a systematic mapping studyEmpirical Software Engineering10.1007/s10664-023-10302-128:3Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1007/s10664-023-10302-1
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Index Terms

Improving intrusion detectors by crook-sourcing
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
  2. Software and application security
    1. Software security engineering
    2. Web application security

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ACSAC '19: Proceedings of the 35th Annual Computer Security Applications Conference

December 2019

821 pages

ISBN:9781450376280

DOI:10.1145/3359789

Conference Chair:
David Balenson
SRI International

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 December 2019

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ACSAC '19

ACSAC '19: 2019 Annual Computer Security Applications Conference

December 9 - 13, 2019

Puerto Rico, San Juan, USA

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ACSAC '19 Paper Acceptance Rate 60 of 266 submissions, 23%;

Overall Acceptance Rate 104 of 497 submissions, 21%

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Cited By

Akbar KWang YAyoade GGao YSinghal AKhan LThuraisingham BJee K(2023)Advanced Persistent Threat Detection Using Data Provenance and Metric LearningIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.322178920:5(3957-3969)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TDSC.2022.3221789
Apruzzese GLaskov PSchneider J(2023)SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP57164.2023.00042(592-614)Online publication date: Jul-2023
https://doi.org/10.1109/EuroSP57164.2023.00042
Torre DMesadieu FChennamaneni A(2023)Deep learning techniques to detect cybersecurity attacks: a systematic mapping studyEmpirical Software Engineering10.1007/s10664-023-10302-128:3Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1007/s10664-023-10302-1
Khoa NDo Hoang HNgo-Khanh KDuy PPham V(2023)SDN-Based Cyber Deception Deployment for Proactive Defense Strategy Using Honey of Things and Cyber Threat IntelligenceIntelligence of Things: Technologies and Applications10.1007/978-3-031-46749-3_26(269-278)Online publication date: 20-Oct-2023
https://doi.org/10.1007/978-3-031-46749-3_26
Bhardwaj SDave M(2023) RAKSHAM : Responsive approach to Knock‐off scavenging hackers and attack mitigation Transactions on Emerging Telecommunications Technologies10.1002/ett.490435:1Online publication date: 2-Dec-2023
https://doi.org/10.1002/ett.4904
Araujo FAyoade GAl-Naami KGao YHamlen KKhan L(2021)Crook-sourced intrusion detection as a serviceJournal of Information Security and Applications10.1016/j.jisa.2021.10288061:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.jisa.2021.102880
Andresini GAppice AMalerba D(2021)Autoencoder-based deep metric learning for network intrusion detectionInformation Sciences10.1016/j.ins.2021.05.016569(706-727)Online publication date: Aug-2021
https://doi.org/10.1016/j.ins.2021.05.016
Memos VPsannis K(2020)AI-Powered Honeypots for Enhanced IoT Botnet Detection2020 3rd World Symposium on Communication Engineering (WSCE)10.1109/WSCE51339.2020.9275581(64-68)Online publication date: 9-Oct-2020
https://doi.org/10.1109/WSCE51339.2020.9275581
Ayoade GAkbar KSahoo PGao YAgarwal AJee KKhan LSinghal A(2020)Evolving Advanced Persistent Threat Detection using Provenance Graph and Metric Learning2020 IEEE Conference on Communications and Network Security (CNS)10.1109/CNS48642.2020.9162264(1-9)Online publication date: Jun-2020
https://doi.org/10.1109/CNS48642.2020.9162264

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