In the modern world, Machine Learning (ML) touches our day-to-day routine in various ways.
Researchers have been actively working on adding intelligence to Intrusion Detection …

Intrusion detection systems (IDS) identify cyber attacks given a sample of network traffic
collected from real-world computer networks. As a powerful classification tool, deep learning …

Cyberattacks are becoming more and more advanced, making it more difficult to identity
suspicious activities on network traffic. Weaponizing the data in the line between network …

In the previous, not many years, digital assaults have become a significant issue in
cybersecurity. Researchers are taking a shot at the intrusion detection framework from the …

Cybersecurity has become one of the focuses of organisations. The number of cyberattacks
keeps increasing as Internet usage continues to grow. An intrusion detection system (IDS) is …

An intrusion detection system is a security system that aims to detect sabotage and
intrusions on networks to inform experts of the attack and abuse of the network. Different …

In this paper, Big Data and Deep Learning Techniques are integrated to improve the
performance of intrusion detection systems. Three classifiers are used to classify network …

The rapid development in computer network and internet have resulted in increased
corresponding data and network attacks. Many novels and improvement technologies have …

Intrusion Detection is one of the fields network security important for industry 4.0. Applying
deep learning models opened a new scope in this field. But availability of latest data set and …

Recently, many anomaly intrusion detection algorithms have been developed and applied in
network security. These algorithms achieve high detection rate on many classical datasets …