This paper provides a survey of prediction, and forecasting methods used in cyber security.
Four main tasks are discussed first, attack projection and intention recognition, in which …

Organizations that benefit from information technologies are vulnerable to various attacks
and malicious behaviors. Intrusion Detection Systems (IDS) are one of the main lines of …

The effectiveness of machine learning models can be significantly averse to redundant and
irrelevant features present in the large dataset which can cause drastic performance …

With the increasing amount of network throughput and security threat, the study of intrusion
detection systems (IDSs) has received a lot of attention throughout the computer science …

Wireless Sensor Networking is one of the most promising technologies that have
applications ranging from health care to tactical military. Although Wireless Sensor Networks …

The dependency of our society on networked computers has become frightening: In the
economy, all-digital networks have turned from facilitators to drivers; as cyber-physical …

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 …

The distributed and open structure of cloud computing and services becomes an attractive
target for potential cyber-attacks by intruders. The traditional Intrusion Detection and …

Abstract Anomaly based Intrusion Detection Systems (IDS) learn normal and anomalous
behavior by analyzing network traffic in various benchmark datasets. Common challenges …

Owing to the swift growth in cyber attacks, intrusion detection systems (IDSs) have become a
necessity to help safeguard personal and organizational assets. However, with the …