research-article

INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection

Authors:

Giuseppina Andresini,

Feargus Pendlebury,

Fabio Pierazzi,

Corrado Loglisci,

Annalisa Appice,

Lorenzo CavallaroAuthors Info & Claims

AISec '21: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security

Pages 111 - 122

https://doi.org/10.1145/3474369.3486864

Published: 15 November 2021 Publication History

Abstract

Despite decades of research in network traffic analysis and incredible advances in artificial intelligence, network intrusion detection systems based on machine learning (ML) have yet to prove their worth. One core obstacle is the existence of concept drift, an issue for all adversary-facing security systems. Additionally, specific challenges set intrusion detection apart from other ML-based security tasks, such as malware detection.

In this work, we offer a new perspective on these challenges. We propose INSOMNIA, a semi-supervised intrusion detector which continuously updates the underlying ML model as network traffic characteristics are affected by concept drift. We use active learning to reduce latency in the model updates, label estimation to reduce labeling overhead, and apply explainable AI to better interpret how the model reacts to the shifting distribution.

To evaluate INSOMNIA, we extend TESSERACT - a framework originally proposed for performing sound time-aware evaluations of ML-based malware detectors - to the network intrusion domain. Our evaluation shows that accounting for drifting scenarios is vital for effective intrusion detection systems.

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Cited By

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https://doi.org/10.26599/BDMA.2023.9020027
Niu ZXue JWang YLei THan WGao X(2024)QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic StreamsChinese Journal of Electronics10.23919/cje.2022.00.36033:3(645-656)Online publication date: May-2024
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Alotaibi FMaffeis S(2024)Mateen: Adaptive Ensemble Learning for Network Anomaly DetectionProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678901(215-234)Online publication date: 30-Sep-2024
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Index Terms

INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection
1. General and reference
  1. Cross-computing tools and techniques
    1. Evaluation
2. Security and privacy
  1. Network security

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cover image ACM Conferences

AISec '21: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security

November 2021

210 pages

ISBN:9781450386579

DOI:10.1145/3474369

Program Chairs:
Nicholas Carlini
Google Brain
,
Ambra Demontis
University of Cagliari
,
Yizheng Chen
University of California, Berkeley

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Published: 15 November 2021

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CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security

November 15, 2021

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Overall Acceptance Rate 94 of 231 submissions, 41%

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https://doi.org/10.26599/BDMA.2023.9020027
Niu ZXue JWang YLei THan WGao X(2024)QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic StreamsChinese Journal of Electronics10.23919/cje.2022.00.36033:3(645-656)Online publication date: May-2024
https://doi.org/10.23919/cje.2022.00.360
Alotaibi FMaffeis S(2024)Mateen: Adaptive Ensemble Learning for Network Anomaly DetectionProceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3678890.3678901(215-234)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3678890.3678901
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Amalapuram SReddy Tamma BChannappayya S(2024)SPIDER: A Semi-Supervised Continual Learning-based Network Intrusion Detection SystemIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621428(571-580)Online publication date: 20-May-2024
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