Due to their massive success in various domains, deep learning techniques are increasingly used to design network intrusion detection solutions that detect and mitigate unknown and …
With the increasing stringent QoS constraints (eg, latency, bandwidth, jitter) imposed by novel applications (eg, e-Health, autonomous vehicles, smart cities, etc.), as well as the …
S Wang, RKL Ko, G Bai, N Dong… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
Cyber-physical systems (CPS) are increasingly relying on machine learning (ML) techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …
Intrusion detection systems (IDS) are a very vital part of network security, as they can be used to protect the network from illegal intrusions and communications. To detect malicious …
The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultrareliability, low latency, and high throughput …
Abstract Intrusion Detection Systems, specifically Network Anomaly Detection Systems (NADSs) are vital tools in network security. The NADSs are affected by data imbalance …
Z Liu, J Hu, Y Liu, K Roy, X Yuan, J Xu - IEEE Access, 2023 - ieeexplore.ieee.org
Adversarial attacks have threatened the credibility of machine learning models and cast doubts over the integrity of data. The attacks have created much harm in the fields of …
Adversarial attacks have been extensively studied in the domain of deep image classification, but their impacts on other domains such as Machine and Deep Learning …
HA Alatwi, C Morisset - … Conference on Big Data (Big Data), 2022 - ieeexplore.ieee.org
Network Intrusion Detection Systems (NIDS) monitor networking environments for suspicious events that could compromise the availability, integrity, or confidentiality of the …