Cyber Attack Detection
6 papers with code • 0 benchmarks • 0 datasets
Cybersecurity attacks prediction using deep learning
Benchmarks
These leaderboards are used to track progress in Cyber Attack Detection
Latest papers with no code
Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective
It indicates that the adversarial perturbation input generated through the surrogate model has a similar impact on the target model in producing the incorrect classification.
Reliable Feature Selection for Adversarially Robust Cyber-Attack Detection
Two different feature sets were selected and were used to train multiple ML models with regular and adversarial training.
An 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.
An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey
Of all the proposed methods, machine learning had been the most effective method in securing a system with capabilities ranging from early detection of software vulnerabilities to real-time detection of ongoing compromise in a system.
An 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.
A Cyber-Physical Architecture for Microgrids based on Deep learning and LORA Technology
Additionally, since the cyber layer of smart grids is highly vulnerable to cyber-attacks, t1his paper proposes a deep-learning-based cyber-attack detection model (CADM) based on bidirectional long short-term memory (BLSTM) and sequential hypothesis testing (SHT) to detect false data injection attacks (FDIA) on the smart meters within AMI.
Deep 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 acquisition (SCADA).
A 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.
Hierarchical Cyber-Attack Detection in Large-Scale Interconnected Systems
We present a scheme to detect attacks that occur at the local level, with malicious agents capable of affecting the local control.
Cyber-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.
