This paper proposes anatomy and main functionalities of a distributed framework for supporting adaptive ensemble-based intrusion detection. We start from open issues and limitations of actual state-of-the-art proposals, and we derive a suitable architecture that, based on actual, emerging research trends, finally defines an innovative ensemble-based network intrusion detection system that combines following requirements: distribution, cooperativeness, scalability, multi-scale network traffic analysis, feature selection and extraction. These requirements are recognized by our study as first-class research challenges for next-generation intrusion detection systems
Due to the extensive use of computer networks, new risks have arisen, and improving the speed and ac...
With the increasing requirements of fast response and privacy protection, how to detect network intr...
Dottorato di Ricerca in Information and Communication Engineering For Pervasive Intelligent Environm...
This paper describes a distributed framework for supporting adaptive ensemble-based intrusion detect...
Intrusion detection systems are used for monitoring the network data, analyze them and find the intr...
The IT infrastructure of today needs to be ready to defend against massive cyber-attacks which often...
The security of computer networks plays a strategic role in modern computer systems. In order to en...
Background: Building an effective Intrusion detection system in a multi-attack classification enviro...
Undoubtedly, the advancements in Machine Learning (ML) and especially ensemble learning models enabl...
Several studies have used machine learning algorithms to develop intrusion systems (IDS), which diff...
Proper security solutions in the cyber world are crucial for enforcing network security by providing...
Of late, Network Security Research is taking center stage given the vulnerability of computing ecosy...
The heterogeneity and complexity of modern networks and services urge the requirement for flexible a...
Intrusion Detection Systems (IDS) have been developed to solve the problem of detecting the attacks ...
AbstractThe master thesis focuses on ensemble approaches applied to intrusion detection systems (IDS...
Due to the extensive use of computer networks, new risks have arisen, and improving the speed and ac...
With the increasing requirements of fast response and privacy protection, how to detect network intr...
Dottorato di Ricerca in Information and Communication Engineering For Pervasive Intelligent Environm...
This paper describes a distributed framework for supporting adaptive ensemble-based intrusion detect...
Intrusion detection systems are used for monitoring the network data, analyze them and find the intr...
The IT infrastructure of today needs to be ready to defend against massive cyber-attacks which often...
The security of computer networks plays a strategic role in modern computer systems. In order to en...
Background: Building an effective Intrusion detection system in a multi-attack classification enviro...
Undoubtedly, the advancements in Machine Learning (ML) and especially ensemble learning models enabl...
Several studies have used machine learning algorithms to develop intrusion systems (IDS), which diff...
Proper security solutions in the cyber world are crucial for enforcing network security by providing...
Of late, Network Security Research is taking center stage given the vulnerability of computing ecosy...
The heterogeneity and complexity of modern networks and services urge the requirement for flexible a...
Intrusion Detection Systems (IDS) have been developed to solve the problem of detecting the attacks ...
AbstractThe master thesis focuses on ensemble approaches applied to intrusion detection systems (IDS...
Due to the extensive use of computer networks, new risks have arisen, and improving the speed and ac...
With the increasing requirements of fast response and privacy protection, how to detect network intr...
Dottorato di Ricerca in Information and Communication Engineering For Pervasive Intelligent Environm...