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Defending the Defender: Adversarial Learning Based Defending Strategy for Learning Based Security Methods in

Zakir Ahmad Sheikh1, Yashwant Singh1, Pradeep Kumar Singh2

  • 1Department of Computer Science and Information Technology, Central University of Jammu, Rahya Suchani, Bagla, Jammu 181143, India.

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Summary
This summary is machine-generated.

Cyber-Physical Systems (CPS) face complex security threats. This research proposes adversarial learning-based defenses to enhance CPS security and resilience against sophisticated cyber-attacks, improving detection capabilities.

Keywords:
CPS securityGenerative Adversarial Networksadversarial attackscyber attackscyber securityevasion attackspoisonous attacks

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Cyber-Physical Systems (CPS) are increasingly vulnerable to sophisticated cyber-attacks due to their interconnected nature and remote accessibility.
  • Traditional signature-based security methods struggle to detect novel and complex zero-day attacks, necessitating advanced solutions.
  • Machine learning models show promise for attack detection but are themselves susceptible to adversarial manipulations like poisoning and evasion.

Purpose of the Study:

  • To propose an adversarial learning-based defense strategy to enhance the security and resilience of Cyber-Physical Systems.
  • To address the limitations of traditional and standard machine learning techniques in detecting advanced and zero-day attacks.
  • To ensure the confidentiality, integrity, and availability of CPS in the face of evolving security threats.

Main Methods:

  • Developed and implemented machine learning-based intelligent attack detection strategies.
  • Proposed an adversarial learning-based defense mechanism to counter adversarial attacks.
  • Evaluated the strategy using Random Forest (RF), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) models.
  • Utilized the ToN_IoT Network dataset and a Generative Adversarial Network (GAN)-generated adversarial dataset for evaluation.

Main Results:

  • Demonstrated the effectiveness of machine learning models in detecting known and unknown (zero-day) attacks.
  • Showcased the proposed adversarial learning strategy's capability to enhance CPS resilience against adversarial attacks.
  • Validated the performance of RF, ANN, and LSTM models on both standard and adversarial datasets.

Conclusions:

  • Adversarial learning-based defense strategies are crucial for robustly securing Cyber-Physical Systems against complex and evolving threats.
  • The proposed approach significantly improves the detection of sophisticated attacks and enhances system resilience.
  • Machine learning, particularly with adversarial training, offers a powerful paradigm for next-generation CPS security.