Y Sun, H Esaki, H Ochiai - IEEE Open Journal of the …, 2020 - ieeexplore.ieee.org
Predominant network intrusion detection systems (NIDS) aim to identify malicious traffic patterns based on a handcrafted dataset of rules. Recently, the application of machine …
Abstract Machine Learning techniques for network-based intrusion detection are widely adopted in the scientific literature. Besides being highly variable, network traffic behavior …
Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of …
O Aouedi, K Piamrat, G Muller… - ICC 2022-IEEE …, 2022 - ieeexplore.ieee.org
With the increasing development of 5G/Beyond 5G and network softwarization techniques, we have more flexibility and agility in the network. This can be exploited by Machine …
J Li, X Tong, J Liu, L Cheng - IEEE Systems Journal, 2023 - ieeexplore.ieee.org
Network intrusion detection is used to detect unauthorized activities on a digital network, with which the cybersecurity teams of organizations can then kick-start prevention protocols …
The rise of IoT networks has presented fresh challenges in terms of scalability and security for distributed Network Intrusion Detection Systems (NIDS) due to privacy concerns. While …
In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative Machine Learning (ML). FL does not share local data but ML models, offering applications in …
O Aouedi, K Piamrat, G Muller… - 2022 IEEE 19th Annual …, 2022 - ieeexplore.ieee.org
In this paper, we present FLUIDS, a Federated Learning with semi-sUpervised approach for Intrusion Detection System. FLUIDS formulates the intrusion detection into a semi …
Abstract The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks …