Related papers: A Survey of Network-based Intrusion Detection Data…

Network Intrusion Detection Systems (NIDSs) are important tools for the protection of computer networks against increasingly frequent and sophisticated cyber attacks. Recently, a lot of research effort has been dedicated to the development…

Data-driven cyberthreat detection has become a crucial defense technique in modern cybersecurity. Network defense, supported by Network Intrusion Detection Systems (NIDSs), has also increasingly adopted data-driven approaches, leading to…

The effectiveness of network intrusion detection systems, predominantly based on machine learning, are highly influenced by the dataset they are trained on. Ensuring an accurate reflection of the multifaceted nature of benign and malicious…

Data-driven methods have been widely used in network intrusion detection (NID) systems. However, there are currently a number of challenges derived from how the datasets are being collected. Most attack classes in network intrusion datasets…

The identification of cyberattacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex problem which presents a diverse number of challenges. Many…

An approach for real-time network monitoring in terms of numerical time-dependant functions of protocol parameters is suggested. Applying complex systems theory for information f{l}ow analysis of networks, the information traffic is…

In this paper, we analyze existing feature selection methods to identify the key elements of network traffic data that allow intrusion detection. In addition, we propose a new feature selection method that addresses the challenge of…

A novel approach to analyze statistically the network traffic raw data is proposed. The huge amount of raw data of actual network traffic from the Intrusion Detection System is analyzed to determine if a traffic is a normal or harmful one.…

Machine Learning (ML)-based Network Intrusion Detection Systems (NIDSs) have proven to become a reliable intelligence tool to protect networks against cyberattacks. Network data features has a great impact on the performances of ML-based…

The goal of an Intrusion Detection is inadequate to detect errors and unusual activity on a network or on the hosts belonging to a local network by monitoring network activity. Algorithms for building detection models are broadly classified…

Distributed intrustion detection systems detect attacks on computer systems by analyzing data aggregated from distributed sources. The distributed nature of the data sources allows patterns in the data to be seen that might not be…

With the increasing amount of reliance on digital data and computer networks by corporations and the public in general, the occurrence of cyber attacks has become a great threat to the normal functioning of our society. Intrusion detection…

With the increasing number of network threats it is essential to have a knowledge of existing and new network threats in order to design better intrusion detection systems. In this paper we propose a taxonomy for classifying network attacks…

Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks were studied in this paper. The identification of attacks in computer networks is divided in to two categories of intrusion detection…

Intrusion detection systems (IDSs) are essential elements of IT systems. Their key component is a classification module that continuously evaluates some features of the network traffic and identifies possible threats. Its efficiency is…

In contrast to previous surveys, the present work is not focused on reviewing the datasets used in the network security field. The fact is that many of the available public labeled datasets represent the network behavior just for a…

Intrusion detection systems (IDS) monitor system logs and network traffic to recognize malicious activities in computer networks. Evaluating and comparing IDSs with respect to their detection accuracies is thereby essential for their…

With the increasing dependency of daily life over computer networks, the importance of these networks security becomes prominent. Different intrusion attacks to networks have been designed and the attackers are working on improving them.…

Internet of Things (IoT) networks have become an increasingly attractive target of cyberattacks. Powerful Machine Learning (ML) models have recently been adopted to implement network intrusion detection systems to protect IoT networks. For…

Network intrusion detection is the process of identifying malicious behaviors that target a network and its resources. Current systems implementing intrusion detection processes observe traffic at several data collecting points in the…