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EVOLIoT: A Self-Supervised Contrastive Learning Framework for Detecting and Characterizing Evolving IoT Malware Variants

Authors:

Elias Bou-Harb,

Nizar Bouguila,

Chadi AssiAuthors Info & Claims

ASIA CCS '22: Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security

Pages 452 - 466

https://doi.org/10.1145/3488932.3517393

Published: 30 May 2022 Publication History

Abstract

Recent years have witnessed the emergence of new and more sophisticated malware targeting the Internet of Things. Moreover, the public release of the source code of popular malware families such as Mirai has spawned diverse variants, making it harder to disambiguate their ownership, lineage, and correct label. Such a rapidly evolving landscape makes it also harder to deploy and generalize effective learning models against retired, updated, and/or new threat campaigns. In this paper, we present EVOLIoT, a novel approach aiming at combating "concept drift" and the limitations of inter-family IoT malware classification by detecting drifting IoT malware families and understanding their diverse evolutionary trajectories. We introduce a robust and effective contrastive method that learns and compares semantically meaningful representations of IoT malware binaries and codes without the need for expensive target labels. We find that the evolution of IoT binaries can be used as an augmentation strategy to learn effective representations to contrast (dis)similar variant pairs. We discuss the impact and findings of our analysis and present several evaluation studies to highlight the tangled relationships of IoT malware, as well as the efficiency of our contrastively learned feature vectors in preserving semantics and reducing out-of-vocabulary size in cross-architecture IoT malware binaries.

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Cited By

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
Yang SZheng XLi JXu JWang XNgai EBaeza-Yates RBonchi F(2024)ReCDA: Concept Drift Adaptation with Representation Enhancement for Network Intrusion DetectionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672007(3818-3828)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672007
Rudd EKrisiloff DCoull SOlszewski DRaff EHolt J(2024)Efficient Malware Analysis Using Metric EmbeddingsDigital Threats: Research and Practice10.1145/36156695:1(1-20)Online publication date: 21-Mar-2024
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Index Terms

EVOLIoT: A Self-Supervised Contrastive Learning Framework for Detecting and Characterizing Evolving IoT Malware Variants
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Malware and its mitigation

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cover image ACM Conferences

ASIA CCS '22: Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security

May 2022

1291 pages

ISBN:9781450391405

DOI:10.1145/3488932

General Chairs:
Yuji Suga
Internet Initiative Japan Inc., Japan
,
Kouichi Sakurai
Kyushu University, Japan
,
Program Chairs:
Xuhua Ding
Singapore Management University, Singapore
,
Kazue Sako
Waseda University, Japan

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ASIA CCS '22: ACM Asia Conference on Computer and Communications Security

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https://dl.acm.org/doi/10.1145/3678890.3678901
Yang SZheng XLi JXu JWang XNgai EBaeza-Yates RBonchi F(2024)ReCDA: Concept Drift Adaptation with Representation Enhancement for Network Intrusion DetectionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672007(3818-3828)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672007
Rudd EKrisiloff DCoull SOlszewski DRaff EHolt J(2024)Efficient Malware Analysis Using Metric EmbeddingsDigital Threats: Research and Practice10.1145/36156695:1(1-20)Online publication date: 21-Mar-2024
https://dl.acm.org/doi/10.1145/3615669
Razaulla SFachkha CGawanmeh AMarkarian CFung BAssi C(2024)Tracing the Ransomware Bloodline: Investigation and Detection of Drifting Virlock Variants2024 6th International Conference on Computer Communication and the Internet (ICCCI)10.1109/ICCCI62159.2024.10674108(1-6)Online publication date: 14-Jun-2024
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Rahat MBanerjee VBloom GZhuang Y(2024)Cimalir: Cross-Platform IoT Malware Clustering using Intermediate Representation2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)10.1109/CCWC60891.2024.10427663(0460-0466)Online publication date: 8-Jan-2024
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