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The growing scale and sophistication of cyberattacks pose critical challenges to network security, particularly in detecting diverse intrusion types within imbalanced datasets. Traditional intrusion detection systems (IDS) often struggle to…

Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vulnerability of the electricity grid to false data injection…

Existing defence mechanisms have demonstrated significant effectiveness in mitigating rule-based Denial-of-Service (DoS) attacks, leveraging predefined signatures and static heuristics to identify and block malicious traffic. However, the…

Software Defined Networks (SDN) face many security challenges today. A great deal of research has been done within the field of Intrusion Detection Systems (IDS) in these networks. Yet, numerous approaches still rely on deep learning…

The exponential expansion of IoT and 5G-Advanced applications has enlarged the attack surface for DDoS, malware, and zero-day intrusions. We propose an intrusion detection system that fuses a convolutional neural network (CNN), a…

Intrusion Detection Systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. It is not surprising that the same technology is being applied to secure Internet of Things…

Attackers demonstrated the use of remote access to the in-vehicle network of connected vehicles to launch cyber-attacks and remotely take control of these vehicles. Machine-learning-based Intrusion Detection Systems (IDSs) techniques have…

Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intra-vehicle networks to implement various functionalities and perform actions. Modern vehicles are…

Anomaly-based Intrusion Detection System (IDS) has been a hot research topic because of its ability to detect new threats rather than only memorized signatures threats of signature-based IDS. Especially after the availability of advanced…

The popularity of Software Defined Networks (SDNs) has grown in recent years, mainly because of their ability to simplify network management and improve network flexibility. However, this also makes them vulnerable to various types of cyber…

The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most…

Distributed Denial of Service (DDoS) attacks remain a persistent threat to the availability of Internet services, edge networks, and cyber-physical infrastructure. Although recent AI-security work has increasingly focused on foundation…

Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for…

Intrusion Detection System (IDS) is one of the security measures being used as an additional defence mechanism to prevent the security breaches on web. It has been well known methodology for detecting network-based attacks but still…

Deep neural networks (DNN) can achieve high performance when applied to In-Distribution (ID) data which come from the same distribution as the training set. When presented with anomaly inputs not from the ID, the outputs of a DNN should be…

The proliferation of Internet-of-things (IoT) infrastructures and the widespread adoption of traffic encryption present significant challenges, particularly in environments characterized by dynamic traffic patterns, constrained…

Security of information passing through the Internet is threatened by today's most advanced malware ranging from orchestrated botnets to simpler polymorphic worms. These threats, as examples of zero-day attacks, are able to change their…

Focus on Deep Neural Network based malicious and normal computer Network Traffic classification. (such as attacks, phishing, any other illegal activity and normal traffic identification). In this paper, the main idea is to review, existed…

Deep learning-based fine-grained network intrusion detection systems (NIDS) enable different attacks to be responded to in a fast and targeted manner with the help of large-scale labels. However, the cost of labeling causes insufficient…

Intrusion detection system (IDS) is a piece of hardware or software that looks for malicious activity or policy violations in a network. It looks for malicious activity or security flaws on a network or system. IDS protects hosts or…