arXiv subjects

Aryan Pasikhani

Publications and source records attributed to Aryan Pasikhani.

4 recordsLinked to original sources

arXiv2026

Automated Stealthy Wear-Out Attack on Digital Twins With Deep Reinforcement Learning

Digital Twins (DTs) have emerged as pivotal enablers of Industry 4.0, offering transformative capabilities such as real-time monitoring, advanced simulation, and precise control of physical assets. By bridging the physical and virtual domains, DTs facilitate seamless integration of data-driven decision-making and operational optimisation. However, this seamless interaction significantly expands the attack surface of industrial systems, creating vulnerabilities that adversaries can exploit. This paper introduces a novel and stealthy wear-out attack leveraging Deep Reinforcement Learning (DRL) to target DT-enabled infrastructures. The adversary strategically and covertly manipulates control signals, inducing increased torque on a specific joint to accelerate wear and tear while evading detection by a state-of-the-art anomaly detection system. Extensive benchmarking of reinforcement learning algorithms - including Twin Delayed Deep Deterministic Policy Gradient (TD3), Soft Actor-Critic (SAC), Proximal Policy Optimisation (PPO), and Advantage Actor-Critic (A2C) - revealed that SAC consistently outperformed its counterparts in terms of sample efficiency, stability, and overall attack effectiveness. We evaluate the proposed adversary in an industrial setting using the UR10e robotic arm. Results demonstrate the adversary's ability to significantly elevate torque levels on the targeted joint, leading to accelerated degradation and increased maintenance costs, all while operating stealthily and avoiding detection. Our findings highlight the substantial risks posed by DRL-driven adversaries to DT-enabled environments and emphasise the critical need for robust defence mechanisms to protect critical industrial systems.

cs.CR↗

arXiv2026

Baiting AI: Deceptive Adversary Against AI-Protected Industrial Infrastructures

This paper explores a new cyber-attack vector targeting Industrial Control Systems (ICS), particularly focusing on water treatment facilities. Developing a new multi-agent Deep Reinforcement Learning (DRL) approach, adversaries craft stealthy, strategically timed, wear-out attacks designed to subtly degrade product quality and reduce the lifespan of field actuators. This sophisticated method leverages DRL methodology not only to execute precise and detrimental impacts on targeted infrastructure but also to evade detection by contemporary AI-driven defence systems. By developing and implementing tailored policies, the attackers ensure their hostile actions blend seamlessly with normal operational patterns, circumventing integrated security measures. Our research reveals the robustness of this attack strategy, shedding light on the potential for DRL models to be manipulated for adversarial purposes. Our research has been validated through testing and analysis in an industry-level setup. For reproducibility and further study, all related materials, including datasets and documentation, are publicly accessible.

cs.CR↗

arXiv2025

Towards Irreversible Machine Unlearning for Diffusion Models

Diffusion models are renowned for their state-of-the-art performance in generating synthetic images. However, concerns related to safety, privacy, and copyright highlight the need for machine unlearning, which can make diffusion models forget specific training data and prevent the generation of sensitive or unwanted content. Current machine unlearning methods for diffusion models are primarily designed for conditional diffusion models and focus on unlearning specific data classes or features. Among these methods, finetuning-based machine unlearning methods are recognized for their efficiency and effectiveness, which update the parameters of pre-trained diffusion models by minimizing carefully designed loss functions. However, in this paper, we propose a novel attack named Diffusion Model Relearning Attack (DiMRA), which can reverse the finetuning-based machine unlearning methods, posing a significant vulnerability of this kind of technique. Without prior knowledge of the unlearning elements, DiMRA optimizes the unlearned diffusion model on an auxiliary dataset to reverse the unlearning, enabling the model to regenerate previously unlearned elements. To mitigate this vulnerability, we propose a novel machine unlearning method for diffusion models, termed as Diffusion Model Unlearning by Memorization (DiMUM). Unlike traditional methods that focus on forgetting, DiMUM memorizes alternative data or features to replace targeted unlearning data or features in order to prevent generating such elements. In our experiments, we demonstrate the effectiveness of DiMRA in reversing state-of-the-art finetuning-based machine unlearning methods for diffusion models, highlighting the need for more robust solutions. We extensively evaluate DiMUM, demonstrating its superior ability to preserve the generative performance of diffusion models while enhancing robustness against DiMRA.

cs.LG↗

arXiv2024

VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication

In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, good data is not a free lunch and is always hard to access due to privacy regulations like the General Data Protection Regulation (GDPR). A potential solution is to release a synthetic dataset with a similar distribution to that of the private dataset. Nevertheless, in some scenarios, it has been found that the attributes needed to train an AI model belong to different parties, and they cannot share the raw data for synthetic data publication due to privacy regulations. In PETS 2023, Xue et al. proposed the first generative adversary network-based model, VertiGAN, for vertically partitioned data publication. However, after thoroughly investigating, we found that VertiGAN is less effective in preserving the correlation among the attributes of different parties. This article proposes a Vertical Federated Learning-based Generative Adversarial Network, VFLGAN, for vertically partitioned data publication to address the above issues. Our experimental results show that compared with VertiGAN, VFLGAN significantly improves the quality of synthetic data. Taking the MNIST dataset as an example, the quality of the synthetic dataset generated by VFLGAN is 3.2 times better than that generated by VertiGAN w.r.t. the Fréchet Distance. We also designed a more efficient and effective Gaussian mechanism for the proposed VFLGAN to provide the synthetic dataset with a differential privacy guarantee. On the other hand, differential privacy only gives the upper bound of the worst-case privacy guarantee. This article also proposes a practical auditing scheme that applies membership inference attacks to estimate privacy leakage through the synthetic dataset.

cs.LG↗