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L-IDS: A lightweight hardware-assisted IDS for IoT systems to detect ransomware attacks

Published: 09 May 2023 Publication History

Abstract

In recent years, ransomware has evolved to target Internet of things (IoT) devices, such as medical equipment and thermostats. Traditional ransomware detection methods may not be effective for resource-constrained IoT devices as IoT-based ransomware is geared towards impairing functionality rather than accessing data. Therefore, this article proposes L-IDS, a lightweight hardware-assisted intrusion detection system that combines hardware-assisted security, such as Trusted Execution Environment, with machine learning algorithms to detect and mitigate ransomware inside an IoT system with fewer resources. The proposed approach can more effectively protect IoT systems from ransomware attacks and requires less resources than traditional security scanning methods.

References

[1]
[1] IBM Security X-Force. (2021). 2021 X-Force Threat Intelligence Index. Retrieved from https://www.ibm.com/security/data-breach/threat-intelligence.
[2]
[2] Ibrar Yaqoob, Ejaz Ahmed, Muhammad Habib ur Rehman, Abdelmuttlib Ibrahim Abdalla Ahmed, Mohammed Ali Al-garadi, Muhammad Imran, Mohsen Guizani, The rise of ransomware and emerging security challenges in the Internet of Things, Computer Networks, Volume 129, Part 2, 2017, Pages 444-458, ISSN 1389-1286.
[3]
[3] Lemmou, Y., Lanet, J.-L. and Souidi, E.M. (2021), A behavioural in-depth analysis of ransomware infection. IET Inf. Secur, 15: 38-58.
[4]
[4] Y. -L. Wan, J. -C. Chang, R. -J. Chen and S. -J. Wang, "Feature-Selection-Based Ransomware Detection with Machine Learning of Data Analysis," 2018 3rd International Conference on Computer and Communication Systems (ICCCS), Nagoya, Japan, 2018, pp. 85-88.
[5]
[5] Fengwei Zhang and Hongwei Zhang. 2016. SoK: A Study of Using Hardware-assisted Isolated Execution Environments for Security. In Proceedings of the Hardware and Architectural Support for Security and Privacy 2016 (HASP 2016). Association for Computing Machinery, New York, NY, USA, Article 3, 1–8.
[6]
[6] Kiran Maharana, Surajit Mondal, Bhushankumar Nemade, A review: Data pre-processing and data augmentation techniques, Global Transitions Proceedings, Volume 3, Issue 1, 2022, Pages 91-99, ISSN 2666-285X.
[7]
[7] Lee, J.; Lee, K. A Method for Neutralizing Entropy Measurement-Based Ransomware Detection Technologies Using Encoding Algorithms. Entropy 2022, 24, 239.

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Published In

cover image ACM Conferences
IoTDI '23: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation
May 2023
514 pages
ISBN:9798400700378
DOI:10.1145/3576842
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 May 2023

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Author Tags

  1. Hardware-assisted security
  2. Internet of Things
  3. Intrusion Detection System
  4. IoT Ransomware
  5. Machine Learning
  6. Ransomware
  7. Trusted Execution Environment

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