Y Qin, M Kondo - 2021 International conference on electrical …, 2021 - ieeexplore.ieee.org
With the increase and diversity of network attacks, machine learning has shown its efficiency in realizing intrusion detection. Federated Learning (FL) has been proposed as a new …
The evolution of cybersecurity is undoubtedly associated and intertwined with the development and improvement of artificial intelligence (AI). As a key tool for realizing more …
MA Ayed, C Talhi - 2021 International Symposium on Networks …, 2021 - ieeexplore.ieee.org
We are attending a severe zero-day cyber attacks. Machine learning based anomaly detection is definitely the most efficient defence in depth approach. It consists to analyzing …
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 …
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 …
As computer networks and interconnected systems continue to gain widespread adoption, ensuring cybersecurity has become a prominent concern for organizations, regardless of …
Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. The development of deep learning …
Intrusion detection through classifying incoming packets is a crucial functionality at the network edge, requiring accuracy, efficiency and scalability at the same time, introducing a …