Related papers: Perfectly Undetectable False Data Injection Attack…
Federated Learning (FL) has emerged as a promising privacy-preserving collaborative model training paradigm without sharing raw data. However, recent studies have revealed that private information can still be leaked through shared gradient…
A critical bottleneck limiting imitation learning in robotics is the lack of data. This problem is more severe in mobile manipulation, where collecting demonstrations is harder than in stationary manipulation due to the lack of available…
Man-in-The-Middle (MiTM) attacks present numerous threats to a smart grid. In a MiTM attack, an intruder embeds itself within a conversation between two devices to either eavesdrop or impersonate one of the devices, making it appear to be a…
This paper considers the defense strategy of strategic topology switching for the second-order multi-agent system under zero-dynamics attack (ZDA) whose attack-starting time is allowed to be not the initial time. We first study the…
This paper demonstrates that false data injection (FDI) attacks are extremely limited in their ability to cause physical consequences on $N-1$ reliable power systems operating with real-time contingency analysis (RTCA) and security…
Anomaly detection plays a vital role in the security and safety of cyber-physical control systems, and accurately distinguishing between different anomaly types is crucial for system recovery and mitigation. This study proposes a dual…
Over-the-air federated learning (OTA-FL) improves communication efficiency by exploiting the superposition property of wireless channels, but this same property also creates a critical security vulnerability: the parameter server (PS)…
Zero-day attacks pose severe cybersecurity risks due to their high success rates and stealth. Because signature-based approaches struggle to detect such attacks, building Intrusion Detection Systems (IDSs) for detecting zero-day attacks is…
There are hardly any data sets publicly available that can be used to evaluate intrusion detection algorithms. The biggest threat for industrial applications arises from state-sponsored and criminal groups. Often, formerly unknown exploits…
Transfer learning (TL) has been demonstrated to improve DNN model performance when faced with a scarcity of training samples. However, the suitability of TL as a solution to reduce vulnerability of overfitted DNNs to privacy attacks is…
Deep Neural Networks have proven to be highly accurate at a variety of tasks in recent years. The benefits of Deep Neural Networks have also been embraced in power grids to detect False Data Injection Attacks (FDIA) while conducting…
Machine unlearning is a newly popularized technique for removing specific training data from a trained model, enabling it to comply with data deletion requests. While it protects the rights of users requesting unlearning, it also introduces…
For many IoT domains, Machine Learning and more particularly Deep Learning brings very efficient solutions to handle complex data and perform challenging and mostly critical tasks. However, the deployment of models in a large variety of…
Fully Homomorphic Encryption (FHE) is rapidly emerging as a promising foundation for privacy-preserving cloud services, enabling computation directly on encrypted data. As FHE implementations mature and begin moving toward practical…
Accurate and reliable dynamic state quantities of generators are very important for real-time monitoring and control of the power system. The emergence of cyber attacks has brought new challenges to the state estimation of generators.…
As researchers continue to optimize AI agents for more effective task execution within operating systems, they often overlook a critical security concern: the ability of these agents to detect "impostors" within their environment. Through…
Despite the promise of Federated Learning (FL) for privacy-preserving model training on distributed data, it remains susceptible to backdoor attacks. These attacks manipulate models by embedding triggers (specific input patterns) in the…
As power systems evolve with increased integration of renewable energy sources, they become more complex and vulnerable to both cyber and physical threats. This study validates a centralized Dynamic State Estimation (DSE) algorithm designed…
The possibility of operating in remote environments using teleoperation systems has been considered widely in the control literature. This paper presents a review on the discrete-time teleoperation systems, including issues such as…
This paper introduces a novel two-stage framework for online mitigation of False Data Injection (FDI) signals to improve the resiliency of Networked Control Systems (NCSs) and ensure their safe operation in the presence of malicious…