Special Issue “AI for Cybersecurity: Robust Models for Authentication, Threat and Anomaly Detection”-Reference-Cited by-同舟云学术

Special Issue “AI for Cybersecurity: Robust Models for Authentication, Threat and Anomaly Detection”

Published:2023-07-07 Issue:7 Volume:16 Page:327
ISSN:1999-4893
Container-title:Algorithms
language:en
Short-container-title:Algorithms

Author:

Bergadano Francesco¹^ORCID,Giacinto Giorgio²^ORCID

Affiliation:

1. Department of Computer Science, University of Torino, Via Pessinetto 12, 10149 Torino, Italy

2. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123 Cagliari, Italy

Abstract

Cybersecurity models include provisions for legitimate user and agent authentication, as well as algorithms for detecting external threats, such as intruders and malicious software [...]

Publisher

MDPI AG

Subject

Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science

Link

https://www.mdpi.com/1999-4893/16/7/327/pdf

Reference10 articles.

1. Tushkanova, O., Levshun, D., Branitskiy, A., Fedorchenko, E., Novikova, E., and Kotenko, I. (2023). Detection of Cyberattacks and Anomalies in Cyber-Physical Systems: Approaches, Data Sources, Evaluation. Algorithms, 16.

2. Lysenko, S., Bobrovnikova, K., Kharchenko, V., and Savenko, O. (2022). IoT Multi-Vector Cyberattack Detection Based on Machine Learning Algorithms: Traffic Features Analysis, Experiments, and Efficiency. Algorithms, 15.

3. Li, X.J., Ma, M., and Sun, Y. (2023). An Adaptive Deep Learning Neural Network Model to Enhance Machine-Learning-Based Classifiers for Intrusion Detection in Smart Grids. Algorithms, 16.

4. Almalki, S.A., Abdel-Rahim, A., and Sheldon, F.T. (2022). Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks. Algorithms, 15.

5. Mijalkovic, J., and Spognardi, A. (2022). Reducing the False Negative Rate in Deep Learning Based Network Intrusion Detection Systems. Algorithms, 15.