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

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.