Several works have used machine learning techniques for network-based intrusion
detection over the past few years. While proposed schemes have been able to provide high …

Several works have proposed highly accurate network-based intrusion detection schemes
through machine learning techniques. However, they are unable to address changes in …

Over the last years, several works have proposed highly accurate machine learning (ML)
techniques for network-based intrusion detection systems (NIDS), that are hardly used in …

In recent years, intrusion detection has emerged as an important technique for network
security. Due to the large volumes of security audit data as well as complex and dynamic …

Abstract Machine Learning techniques for network-based intrusion detection are widely
adopted in the scientific literature. Besides being highly variable, network traffic behavior …

The number of internet-connected systems has increased by a huge amount in recent years.
These systems are highly vulnerable and increasingly at risk from cyber-attacks. These …

Intrusion detection is a crucial service in today's data networks, and the search for new fast
and robust algorithms that are capable of detecting and classifying dangerous traffic is …

Abstract Intrusion Detection Systems (IDS) help protect computer networks by identifying
and detecting attempts to obtain unauthorized access to data via computer networks by …

Despite the promising results of machine learning for network-based intrusion detection,
current techniques are not widely deployed in real-world environments. In general …

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has
taken a prominent role in the network security management field, due to the substantial …