Machine learning is of rising importance in cybersecurity. The primary objective of applying
machine learning in cybersecurity is to make the process of malware detection more …

This survey paper describes a focused literature survey of machine learning (ML) and data
mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial …

Industrial networks are complex and diverse. Among existing intrusion prevention systems
available, several of them have problems such as low detection accuracy rate, high false …

The comprehensive integration of instrumentation, communication, and control into physical
systems has led to the study of Cyber-Physical Systems (CPSs), a field that has recently …

With the arrival of the Internet of Things (IoT) many devices such as sensors, nowadays can
communicate with each other and share data easily. However, the IoT paradigm is prone to …

With increasing Internet connectivity and traffic volume, recent intrusion incidents have
reemphasized the importance of network intrusion detection systems for combating …

To prevent and defend networks from the occurrence of attacks, it is highly essential that we
have a broad knowledge of existing tools and systems available in the public domain. Based …

Vehicular ad hoc network (VANET) is an enabling technology in modern transportation
systems for providing safety and valuable information, and yet vulnerable to a number of …

Current intrusion detection systems (IDS) examine all data features to detect intrusion or
misuse patterns. Some of the features may be redundant or contribute little (if anything) to …

With the rapid rise in the ubiquity and sophistication of Internet technology and the
accompanying growth in the number of network attacks, network intrusion detection has …