A Minimally Informed Black-Box Fake-Report Attack on DRL Traffic-Signal Control

Deep reinforcement learning DRL traffic-signal controllers may use vehicle-to-infrastructure V2I reports, exposing their perception layer to false-data injection. Existing demonstrations typically assume either white-box access to the controller's weights or full observability of the traffic stat...

A Defense-Aware Explainable AI Framework for Feature-Level Vulnerability Analysis in Optimal Power Flow

Optimal power flow OPF is a foundational analysis in power system operation, and inaccuracies in OPF decision-making can result in significant operational and security consequences. With the increasing adoption of machine-learning-based OPF surrogate models for real-time and large-scale...

AquaSense-ICS: A Hybrid Real-Simulated Cyber-Physical Dataset and Leakage-Aware Benchmark for Smart Water Quality Monitoring and Attack Detection

Machine-learning research on water cyber-physical systems CPS is constrained by datasets that are either real but chemically poor, or simulated but free of sensing noise and attacks. We present AquaSense-ICS, an open dataset and benchmark that co-registers i a 25.4 h recording window 8.0 h of...

Diversifying Moving Target Defense against Adaptive Attacks through Factorized Diffusion

Proactive defense offers a means to enhance power-system cybersecurity through controlled modifications of system configurations. One example is moving target defense MTD, which uses distributed flexible AC transmission system D-FACTS devices to perturb transmission-line reactances against stealt...

Robustness of Local Energy Markets to Cyberattacks: Case Study of False Data Injection

Market clearing in community-based local energy markets relies on power demand and PV forecasts, making it vulnerable to coordinated false data injection FDI attacks. This paper proposes a bilevel optimization framework to identify worst-case bus-level FDI against interconnected multi-community...

Astra Linux – Vulnerability in bind9

BIND 9.11.0 - 9.11.36 9.12.0 - 9.16.26 9.17.0 - 9.18.0 BIND supports the following preview editions: 9.11.4-S1 - 9.11.36-S1 9.16.8-S1 - 9.16.26-S1 Versions of BIND 9 that are earlier than those shown—going back to 9.1.0, including the supported preview editions—are also believed to be affected, b...

Adversarial Training of Linear Models under Stealthy Attacks

Predictive models are widely used in many fields, but are vulnerable to false data injection attacks. To address this, detection schemes and adversarial training have been proposed, but such approaches lack guarantees against stealthy attacks. We therefore propose a detector-based switched model,...

A Hybrid Intrusion Detection System for Electric Vehicle Charging Infrastructure

The integration of Electric Vehicle Charging Stations EVCSs into the smart grid necessitates sophisticated digital infrastructure for their management and coordination, which expands the attack surface and makes both the power grid and EVCSs vulnerable to cyberattacks. This research addresses...

SAMD: A Tool for Identifying False Data Injection Scenarios in AI/ML-Enabled Medical Devices

The growing integration of artificial intelligence AI and machine learning ML in medical systems requires effective measures to address emerging security risks. One such risk is that of adversaries introducing false data through vulnerable system components during inference, causing misdiagnosis...

Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing

In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion. To addres...

Time Stepped Cyber Physical Simulation of DoS, DoD, and FDI Attacks on the IEEE 14 Bus System

Reliable grid operation depends on accurate and timely telemetry, making modern power systems vulnerable to communication layer cyberattacks. This paper evaluates how Denial of Service DoS, Denial of Data DoD, and False Data Injection FDI attacks disrupt the IEEE 14 bus system using a MATLAB only...

Large Language Models for Detecting Cyberattacks on Smart Grid Protective Relays

This paper presents a large language model LLM-based framework for detecting cyberattacks on transformer current differential relays TCDRs, which, if undetected, may trigger false tripping of critical transformers. The proposed approach adapts and fine-tunes compact LLMs such as DistilBERT to...

EUVD-2007-1181

Malware in sbrugna...

A Kolmogorov-Arnold Network for Interpretable Cyberattack Detection in AGC Systems

Automatic Generation Control AGC is essential for power grid stability but remains vulnerable to stealthy cyberattacks, such as False Data Injection Attacks FDIAs, which can disturb the system's stability while evading traditional detection methods. Unlike previous works that relied on blackbox...

False Data-Injection Attack Detection in Cyber-Physical Systems: a Wasserstein Distributionally Robust Reachability Optimization Approach

Cyber-physical system CPS is the foundational backbone of modern critical infrastructures, so ensuring its security and resilience against cyber-attacks is of pivotal importance. This paper addresses the challenge of designing anomaly detectors for CPS under false-data injection FDI attacks and...

Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems

False Data Injection Attacks FDIAs pose a significant threat to smart grid infrastructures, particularly Home Area Networks HANs, where real-time monitoring and control are highly adopted. Owing to the comparatively less stringent security controls and widespread availability of HANs, attackers...

Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems

The increasing digitization of smart grids has improved operational efficiency but also introduced new cybersecurity vulnerabilities, such as False Data Injection Attacks FDIAs targeting Automatic Generation Control AGC systems. While machine learning ML and deep learning DL models have shown...

SDN-Based False Data Detection with Its Mitigation and Machine Learning Robustness for In-Vehicle Networks

As the development of autonomous and connected vehicles advances, the complexity of modern vehicles increases, with numerous Electronic Control Units ECUs integrated into the system. In an in-vehicle network, these ECUs communicate with one another using an standard protocol called Controller Are...

Breaking the Flow and the Bank: Stealthy Cyberattacks on Water Network Hydraulics

As water distribution networks WDNs become increasingly connected with digital infrastructures, they face greater exposure to cyberattacks that threaten their operational integrity. Stealthy False Data Injection Attacks SFDIAs are particularly concerning, as they manipulate sensor data to...

PT-2024-33506 · Elvaco · Elvaco

Name of the Vulnerable Software and Affected Versions: Elvaco affected versions not specified Description: The issue arises from insufficiently protected credentials, potentially allowing an attacker to impersonate Elvaco and send false information. Recommendations: At the moment, there is no...