Malicious network attacks are a serious source of concern, and machine learning
techniques are widely used to build Attack Detectors with off-line training with real attack and …

Traditional intrusion detection systems (IDS) detect attacks by comparing current behavior to
signatures of known attacks. One main drawback is the inability of detecting new attacks …

Abstract Network Security Management is not only becoming difficult but also becoming
impossible as size of networks grow. Attacks grow beyond the current ability of security …

Denial of service attacks are harmful cyberattacks that diminish Internet resources and
services. Hence, detecting these cyberattacks is a topic of great interest in cybersecurity …

Network-based intrusion detection is traditionally bound to deep packet inspection, ie
searching for known signatures of attacks in the packet payload. With the rise of connected …

Intentional attacks1 that cause country wide blackouts, gas and water systems malfunction
are actions that can be carried out by a nation to impact on another nation in a mean of war …

Detecting and classifying anomalous behaviors in computer networks remains a formidable
challenge. This work outlines a machine learning technique that uses deep neural networks …

Cybersecurity is a critical issue in today's internet world. Classical security systems, such as
firewalls based on signature detection, cannot detect today's sophisticated zero-day attacks …

The use of machine learning techniques to improve network design is gaining popularity.
When these techniques are applied to security problems, a fundamental problem arises; …

The continuous increase in the number of attacks on computer networks has raised serious
concerns regarding the importance of establishing a methodology that can learn and adapt …