Papers Cyber Attack Detection
“Cyber Attack Detection” 태그가 달린 논문 41편 · 필터 해제
Enhancing IoT Cyber Attack Detection in the Presence of Highly Imbalanced Data
Due to the rapid growth in the number of Internet of Things (IoT) networks, the cyber risk has increased exponentially, and therefore, we have to develop effective IDS that can work well with highly imbalanced datasets. …
Cyber Attack Detectionfeature selectionZero Dynamics Attack Detection and Isolation in Cyber-Physical Systems with Event-triggered Communication
This paper investigates the problem of Zero Dynamics (ZD) cyber-attack detection and isolation in Cyber-Physical Systems (CPS). By utilizing the notion of auxiliary systems with event-based communications, we will develo…
Cyber Attack DetectionCRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid
The modern power grids are integrated with digital technologies and automation systems. The inclusion of digital technologies has made the smart grids vulnerable to cyber-attacks. Cyberattacks on smart grids can compromi…
Cyber Attack DetectionIntrusion DetectionAdaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models
The rapid expansion of the industrial Internet of things (IIoT) has introduced new challenges in securing critical infrastructures against sophisticated cyberthreats. This study presents the development and evaluation of…
Binary ClassificationClassificationCyber Attack DetectionIntrusion Detection+1Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches
This paper explores the detection and localization of cyber-attacks on time-series measurements data in power systems, focusing on comparing conventional machine learning (ML) like k-means, deep learning method like auto…
Anomaly DetectionCyber Attack DetectionGraph Neural NetworkTime Series+1LogSHIELD: A Graph-based Real-time Anomaly Detection Framework using Frequency Analysis
Anomaly-based cyber threat detection using deep learning is on a constant growth in popularity for novel cyber-attack detection and forensics. A robust, efficient, and real-time threat detector in a large-scale operation…
Anomaly DetectionCyber Attack DetectionGraph EmbeddingGraph Neural NetworkA Life-long Learning Intrusion Detection System for 6G-Enabled IoV
The introduction of 6G technology into the Internet of Vehicles (IoV) promises to revolutionize connectivity with ultra-high data rates and seamless network coverage. However, this technological leap also brings signific…
class-incremental learningClass Incremental LearningContinual LearningCyber Attack Detection+4Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids
Modern power grids are undergoing significant changes driven by information and communication technologies (ICTs), and evolving into smart grids with higher efficiency and lower operation cost. Using ICTs, however, comes…
Cyber Attack DetectionSelf-Supervised LearningBlack-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective
The rapid advancement of artificial intelligence within the realm of cybersecurity raises significant security concerns. The vulnerability of deep learning models in adversarial attacks is one of the major issues. In adv…
Cyber Attack DetectionDeep LearningReliable Feature Selection for Adversarially Robust Cyber-Attack Detection
The growing cybersecurity threats make it essential to use high-quality data to train Machine Learning (ML) models for network traffic analysis, without noisy or missing data. By selecting the most relevant features for …
Computational EfficiencyCyber Attack Detectionfeature selectionAn Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids
Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermit…
Cyber Attack DetectionAn Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey
In this research, we analyzed the suitability of each of the current state-of-the-art machine learning models for various cyberattack detection from the past 5 years with a major emphasis on the most recent works for com…
Cyber Attack DetectionAn Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection
As cyber-attacks become more sophisticated, improving the robustness of Machine Learning (ML) models must be a priority for enterprises of all sizes. To reliably compare the robustness of different ML models for cyber-at…
Adversarial RobustnessCyber Attack DetectionIntrusion DetectionNetwork Intrusion DetectionA Cyber-Physical Architecture for Microgrids based on Deep learning and LORA Technology
This paper proposes a cyber-physical architecture for the secured social operation of isolated hybrid microgrids (HMGs). On the physical side of the proposed architecture, an optimal scheduling scheme considering various…
Cyber Attack Detectionenergy managementSchedulingDeep Learning-Based Cyber-Attack Detection Model for Smart Grids
In this paper, a novel artificial intelligence-based cyber-attack detection model for smart grids is developed to stop data integrity cyber-attacks (DIAs) on the received load data by supervisory control and data acquisi…
Cyber Attack DetectionDeep LearningLoad ForecastingregressionA Deep Multi-Modal Cyber-Attack Detection in Industrial Control Systems
The growing number of cyber-attacks against Industrial Control Systems (ICS) in recent years has elevated security concerns due to the potential catastrophic impact. Considering the complex nature of ICS, detecting a cyb…
Cyber Attack DetectionA Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids
This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. Capturing the topological information of th…
Cyber Attack DetectionGraph Neural NetworkNode ClassificationSmart Grid PredictionHierarchical Cyber-Attack Detection in Large-Scale Interconnected Systems
In this paper we present a hierarchical scheme to detect cyber-attacks in a hierarchical control architecture for large-scale interconnected systems (LSS). We consider the LSS as a network of physically coupled subsystem…
Cyber Attack DetectionCyber-resilient Automatic Generation Control for Systems of AC Microgrids
In this paper we propose a co-design of the secondary frequency regulation in systems of AC microgrids and its cyber securty solutions. We term the secondary frequency regulator a Micro-Automatic Generation Control (Micr…
Cyber Attack DetectionComplex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI
The effective representation, precessing, analysis, and visualization of large-scale structured data over graphs are gaining a lot of attention. So far most of the literature has focused on real-valued signals. However, …
Cyber Attack Detection