Pervasive growth of Internet of Things (IoT) is visible across the globe. The 2016 Dyn
cyberattack exposed the critical fault-lines among smart networks. Security of IoT has …

In the last decade, huge growth is recorded globally in computer networks and Internet of
Things (IoT) networks due to the exponential data generation, approximately zettabyte to a …

Abstract Machine Learning (ML)-based Network Intrusion Detection Systems (NIDSs) have
become a promising tool to protect networks against cyberattacks. A wide range of datasets …

Feature selection plays a vital role in building machine learning models. Irrelevant features
in data affect the accuracy of the model and increase the training time needed to build the …

Abstract Network Intrusion Detection Systems (NIDSs) are important tools for the protection
of computer networks against increasingly frequent and sophisticated cyber attacks …

A robust intrusion detection system plays a very important role in network security. In the
face of complex network data and diverse intrusion methods, traditional machine learning …

Efficiently detecting network intrusions requires the gathering of sensitive information. This
means that one has to collect large amounts of network transactions including high details of …

Deep learning (DL) is gaining significant prevalence in every field of study due to its
domination in training large data sets. However, several applications are utilizing machine …

Intrusions in computer networks have increased significantly in the last decade, due in part
to a profitable underground cyber-crime economy and the availability of sophisticated tools …

Network security is becoming increasingly important in our daily lives—not only for
organizations but also for individuals. Intrusion detection systems have been widely used to …