A proposed study provides an annotated list of SLR (Systematic literature Review) for use by
dark web (DW) researchers, cyber experts, and security agencies in the investigation and …

Abstract Network Anomaly Detection Systems (NADSs) are gaining a more important role in
most network defense systems for detecting and preventing potential threats. The paper …

Machine learning is adopted in a wide range of domains where it shows its superiority over
traditional rule-based algorithms. These methods are being integrated in cyber detection …

Users and Internet service providers (ISPs) are constantly affected by denial‐of‐service
(DoS) attacks. This cyber threat continues to grow even with the development of new …

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 …

Nowadays, the ever-increasing complication and severity of security attacks on computer
networks have inspired security researchers to incorporate different machine learning …

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 …

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 …

In a contemporary data center, Linux applications often generate a large quantity of real-time
system call traces, which are not suitable for traditional host-based intrusion detection …

An intrusion detection system has become a vital mechanism to detect a wide variety of
malicious activities in the cyber domain. However, this system still faces an important …