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Search · four archives
17 papers · ranked by Valyu relevance
Ahmed N. Sheta, Samaa F. Osman, Abdelfattah A. Eladl, Bishoy E. Sedhom + 1 more
False Data Injection Attacks (FDIAs) represent a significant cybersecurity threat to smart grids (SGs), compromising both system stability and operational reliability. Conventional detection approaches frequently prove inadequate, largely due to challenges such as data imbalance and suboptimal model parameterisation.…
Fan Luo, Siqin Fan, Guolin Shao, Chase Wu
Distributed Denial-of-Service (DDoS) attacks remain the most pervasive and operationally disruptive cyber threat and are routinely weaponized in interstate conflict (e.g., Russia-Ukraine and Stuxnet). Although attack-chain models are standard for Advanced Persistent Threat (APT) analysis, they have seldom been applied…
Xin Li, Chenhan Xiao, Jonathan Cohen, Aviad Elyashar + 2 more
The rapid growth of AI-driven data centers and large-scale energy storage systems is increasing the reliance of power system operation on real-time measurement data and automated decision-making. However, many existing detection methods rely on statistical or data-driven analysis of measurements and can fail when…
Elaheh Yaghoubi, Elnaz Yaghoubi, Mehdi Zareian Jahromi, Mohammad Reza Maghami + 1 more
The integration of renewable energy sources in microgrids (MGs) enhances system efficiency but increases vulnerability to cyberattacks such as false data injection (FDI) attacks. This paper presents a robust data-driven nonlinear model predictive control (NLMPC) framework with the integration with Bayesian Neural…
Xin Li, Chenhan Xiao, Jonathan Cohen, Aviad Elyashar + 2 more
False data injection attacks (FDIAs) introducing small measurement perturbations can still cause large deviations in power system state estimation when the injected signals align with the pseudo-null space of the system model. Existing model- and data-driven detectors may fail to identify such low-magnitude but…
Denys Mishchenko, Irina Oleinikova, Laszlo Erdodi
This paper assesses the resilience of IEC 61850 digital substations under False Data Injection Attacks (FDIAs) targeting the Sampled Values (SV) protocol. The multicast nature of SV, while enabling time-critical automation, exposes substations to cyber intrusions capable of disrupting protection functions and causing…
Basheer Al-Duwairi, Ahmed Shatnawi, Ahmad Al-Hammouri, Mohammad Ababneh
This paper presents network traffic captures from a Supervisory Control and Data Acquisition (SCADA) system installed in a medical waste incinerator. The dataset comprises 14 daily packet capture files (day01.pcap to day14.pcap), collected over a two-week period from a Siemens S7-1500/ET200MP-based SCADA system. The…
Amir Ahmad Ghods, Mohammadreza Doostmohammadian
Distributed target tracking in multi-agent networks plays a critical role in cooperative sensing and autonomous navigation. However, it faces significant challenges in highly dynamic and adversarial setups. This study aims to enhance the resilience of decentralized target tracking algorithms against measurement faults…
Yahya Shahsavari, Reza Nourmohammadi, Sara Rouhani, Kaiwen Zhang
Modern vehicular networks face an expanding attack surface across internal Electronic Control Units (ECUs) and external Vehicle-to-Everything (V2X) communication. Federated Learning (FL) has emerged as a decentralized paradigm to deploy Intrusion Detection Systems (IDS) without compromising data privacy. However, the…
Qinxuan Shi, Toro Dama Caleb, Sicong Shao, Naima Kaabouch + 1 more
The aviation sector relies on cooperative surveillance systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) and Remote Identification (RID) to enhance safety and efficiency. However, their open, unencrypted communication protocols make them vulnerable to various cyberattacks. This survey examines the…
Neha, Bhatia, Tarunpreet
— Intrusion Detection Systems (IDS) are critical components in safeguarding 5G/6G networks from both internal and external cyber threats. While traditional IDS approaches rely heavily on signature-based methods, they struggle to detect novel and evolving attacks. This paper presents an advanced IDS framework that…
Shiwen Ni, Qianning Wang, Chi Wei, Xiaomin Ni + 6 more
Genomic foundation models are increasingly used to interpret and design DNA sequences, yet their susceptibility to training-data manipulation remains poorly understood. Here we systematically evaluate backdoor poisoning across three model families, seven parameter scales ranging from 50 million to 7 billion, and 18…
Hongjian Zhou, Xinyu Zou, Jinge Wu, Sean Wu + 18 more
Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increasingly use them for health advice. We show this assumption is fragile: when misleading context is injected into questions that LLMs…
Steven Golob, Patrick McKeever, Sikha Pentyala, Martine De Cock + 1 more
Single-cell RNA sequencing (scRNA-seq) data is subject to strict access control due to its sensitive nature, motivating the use of synthetic data generation (SDG) for privacy-preserving data sharing. We present the first adversarial privacy attack that performs meaningfully above random guessing against…
Emanuel Casmiry, Neema Mduma, Ramadhani Sinde
In the face of increasing cyberattacks, Structured Query Language (SQL) injection remains one of the most common and damaging types of web threats, accounting for over 20% of global cyberattack costs. However, due to its dynamic and variable nature, the current detection methods often suffer from high false positive…
Hakime Öztürk, Tejumade Afonja, Joonas Jälkö, Ruta Binkyte + 18 more
The synthesis of anonymized data derived from real-world cohorts offers a promising strategy for regulatory-compliant and privacy-preserving biological data sharing, potentially facilitating model development that can improve predictive performance. However, the extent to which generative models can preserve biological…
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High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…