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RAFeL - Robust and Data-Aware Federated Learning-inspired Malware Detection in Internet-of-Things (IoT) Networks

Authors:

Houman Homayoun,

Setareh Rafatirad,

Sai Manoj P DAuthors Info & Claims

GLSVLSI '22: Proceedings of the Great Lakes Symposium on VLSI 2022

Pages 153 - 157

https://doi.org/10.1145/3526241.3530378

Published: 06 June 2022 Publication History

Abstract

Federated Learning (FL) is a decentralized machine learning in which the training data is distributed on the Internet-of-Things (IoT) devices and learns a shared global model by aggregating local updates. However, the training data can be poisoned and manipulated by malicious adversaries, contaminating locally computed updates. To prevent this, detecting malicious IoT devices is very important. Since the local updates are large because of the high volume of data, minimizing the communication overhead is also necessary. This paper proposes a "RAFeL" framework, comprising of two techniques to tackle the above issues, (1) a robust defense technique and (2) a "Performance-aware bit-wise encoding" technique. "Robust and Active Protection with Intelligent Defense (RAPID)" is a defense system that detects malicious IoT devices and restricts the participation of the contaminated local updates computed by these malicious devices. To minimize communication cost, "Performance-aware bit-wise encoding" selects the appropriate encoding scheme for individual split bits based on their significance and effect on FL performance. The results illustrate that the proposed framework shows a 1.2-1.8x higher compression rate than lossy and lossless encoding techniques and has an average accuracy drop of 3% to 10% even with a fraction of malicious devices.

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Cited By

Hassan RMeng XBasu KDinakarrao S(2023)Circuit Topology-Aware Vaccination-Based Hardware Trojan DetectionIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.323444042:9(2852-2862)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TCAD.2023.3234440
Tang JXu ZYe LPeng THe RHu XTian W(2023)PE-FedAvg: A Privacy-Enhanced Federated Learning for Distributed Android Malware Detection2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00094(474-481)Online publication date: 21-Dec-2023
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00094
Shukla SDhavlle AD SHomayoun HRafatirad S(2022)Iron-Dome: Securing IoT Networked Systems at Runtime by Network and Device Characteristics to Confine Malware Epidemics2022 IEEE 40th International Conference on Computer Design (ICCD)10.1109/ICCD56317.2022.00046(259-262)Online publication date: Oct-2022
https://doi.org/10.1109/ICCD56317.2022.00046

Index Terms

RAFeL - Robust and Data-Aware Federated Learning-inspired Malware Detection in Internet-of-Things (IoT) Networks
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Malware and its mitigation

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Information & Contributors

Information

Published In

cover image ACM Conferences

GLSVLSI '22: Proceedings of the Great Lakes Symposium on VLSI 2022

June 2022

560 pages

ISBN:9781450393225

DOI:10.1145/3526241

General Chairs:
Ioannis Savidis
Drexel University, USA
,
Avesta Sasan
University of California, Davis, USA
,
Program Chairs:
Himanshu Thapliyal
University of Tennessee, Knoxville, USA
,
Ronald F. DeMara
University of Central Florida, USA

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGDA: ACM Special Interest Group on Design Automation

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Association for Computing Machinery

New York, NY, United States

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Published: 06 June 2022

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GLSVLSI '22: Great Lakes Symposium on VLSI 2022

June 6 - 8, 2022

CA, Irvine, USA

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Cited By

Hassan RMeng XBasu KDinakarrao S(2023)Circuit Topology-Aware Vaccination-Based Hardware Trojan DetectionIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.323444042:9(2852-2862)Online publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1109/TCAD.2023.3234440
Tang JXu ZYe LPeng THe RHu XTian W(2023)PE-FedAvg: A Privacy-Enhanced Federated Learning for Distributed Android Malware Detection2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00094(474-481)Online publication date: 21-Dec-2023
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00094
Shukla SDhavlle AD SHomayoun HRafatirad S(2022)Iron-Dome: Securing IoT Networked Systems at Runtime by Network and Device Characteristics to Confine Malware Epidemics2022 IEEE 40th International Conference on Computer Design (ICCD)10.1109/ICCD56317.2022.00046(259-262)Online publication date: Oct-2022
https://doi.org/10.1109/ICCD56317.2022.00046

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