Recently, the huge amounts of data and its incremental increase have changed the
importance of information security and data analysis systems for Big Data. Intrusion …

This paper successfully tackles the problem of processing a vast amount of security related
data for the task of network intrusion detection. It employs Apache Spark, as a big data …

Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows
up in various shapes and patterns, therefore it is not easy to detect and solve with previous …

Semi-supervised machine learning can be used for obtaining subsets of unlabeled or
partially labeled dataset based on the applicable metrics of dissimilarity. At later stage, the …

The emergence of Internet connectivity has led to a significant increase in the volume and
complexity of cyber attacks. Abnormal behavior detection systems are valuable tools for …

The security pitfalls of IoT devices make it easy for the attackers to exploit the IoT devices
and make them a part of a botnet. Once hundreds of thousands of IoT devices are …

Intrusion Detection System is a well-known term in the domain of Network and Information
Security. It's one of the important components of the Network and Information Security …

Artificial intelligence algorithms have a leading role in the field of cybersecurity and attack
detection, being able to present better results in some scenarios than classic intrusion …

The number of Internet-of-Things (IoT) devices has significantly expanded as a result of the
growing reliance on the Internet and the associated rise in connectivity demand. According …

Abstract Machine learning (ML) algorithms are often used to design effective intrusion
detection (ID) systems for appropriate mitigation and effective detection of malicious cyber …