Search Results for author:
Found 8 papers, 0 papers with code
Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling
In this work, we introduce an enhanced approach for DDoS attack detection by leveraging the capabilities of Deep Residual Neural Networks (ResNets) coupled with synthetic oversampling techniques.
Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis
Mitigating Denial-of-Service (DoS) attacks is vital for online service security and availability.
A Decentralized Cooperative Navigation Approach for Visual Homing Networks
Visual homing is a lightweight approach to visual navigation.
Improving Machine Learning Robustness via Adversarial Training
Moreover, in the non-IID data case, the natural accuracy drops from 66. 23% to 57. 82%, and the robust accuracy decreases by 25% and 23. 4% in C&W and Projected Gradient Descent (PGD) attacks, compared to the IID data case, respectively.
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks
Many state-of-the-art ML models have outperformed humans in various tasks such as image classification.
Mahalanobis distance-based robust approaches against false data injection attacks on dynamic power state estimation
Compared to existing approaches, our proposed approaches have three major differences and significant strengths: (1) they defend against the three FDI attacks on dynamic power state estimation rather than static power state estimation, (2) they give a robust estimator that can accurately extract a subset of attack-free sensors for power state estimation, and (3) they adopt the little-known Mahalanobis distance in the consistency check of power sensor measurements, which is different from the Euclidean distance used in all the existing studies on power state estimation.
Active Learning Under Malicious Mislabeling and Poisoning Attacks
To check the performance of the proposed method under an adversarial setting, i. e., malicious mislabeling and data poisoning attacks, we perform an extensive evaluation on the reduced CIFAR-10 dataset, which contains only two classes: airplane and frog.
An Adversarial Attack Defending System for Securing In-Vehicle Networks
In a modern vehicle, there are over seventy Electronics Control Units (ECUs).
