Search Results for author:
Found 5 papers, 0 papers with 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.
Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System
In this research work, we aim to cover important aspects related to NIDS, adversarial attacks and its defence mechanism to increase the robustness of the ML and DL based NIDS.
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks
As a defence method, Adversarial Training is used to increase the robustness of the NIDS model.
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model
The overall accuracy and F score of OPT_Model (when trained in unsupervised way) are 0. 90 and 0. 76, respectively.
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)
If the internal working of the ML and DL based models is understandable, then it can further help to improve its performance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
