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The integration of communication networks and the Internet of Things (IoT) in Industrial Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing devastating outcomes. Traditional Intrusion Detection Systems…
With the ubiquitous nature of information technology solutions that facilitate communication in the modern world, cyber attacks are increasing in volume and becoming more sophisticated in nature. From classic network-based Denial of Service…
Network security applications, including intrusion detection systems of deep neural networks, are increasing rapidly to make detection task of anomaly activities more accurate and robust. With the rapid increase of using DNN and the volume…
With the increasing number of intrusions in system and network infrastructures, Intrusion Detection Systems (IDS) have become an active area of research to develop reliable and effective solutions to detect and counter them. The use of…
Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanism that used to sense and classify any abnormal actions. Therefore, the…
The current paper addresses relevant network security vulnerabilities introduced by network devices within the emerging paradigm of Internet of Things (IoT) as well as the urgent need to mitigate the negative effects of some types of…
The network security analyzers use intrusion detection systems (IDSes) to distinguish malicious traffic from benign ones. The deep learning-based IDSes are proposed to auto-extract high-level features and eliminate the time-consuming and…
Deep Learning Systems (DLSs) are increasingly deployed in real-time applications, including those in resourceconstrained environments such as mobile and IoT devices. To address efficiency challenges, Dynamic Deep Learning Systems (DDLSs)…
The rapid development and expansion of World Wide Web and network systems have changed the computing world in the last decade and also equipped the intruders and hackers with new facilities for their destructive purposes. The cost of…
The rapid expansion of the Industrial Internet of Things (IIoT) has significantly advanced digital technologies and interconnected industrial systems, creating substantial opportunities for growth. However, this growth has also heightened…
Extreme events with potential deadly outcomes, such as those organized by terror groups, are highly unpredictable in nature and an imminent threat to society. In particular, quantifying the likelihood of a terror attack occurring in an…
Anomaly detection systems aim to detect and report attacks or unexpected behavior in networked systems. Previous work has shown that anomalies have an impact on system performance, and that performance signatures can be effectively used for…
The rapid expansion of the Internet of Things and the emergence of edge computing-based applications has led to a new wave of cyber-attacks, with intensity and complexity that has never been seen before. Historically most research has…
Denial of service attacks are especially pertinent to the internet of things as devices have less computing power, memory and security mechanisms to defend against them. The task of mitigating these attacks must therefore be redirected from…
Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task.…
An application of software known as an Intrusion Detection System (IDS) employs machine algorithms to identify network intrusions. Selective logging, safeguarding privacy, reputation-based defense against numerous attacks, and dynamic…
With the growth of adversarial attacks against machine learning models, several concerns have emerged about potential vulnerabilities in designing deep neural network-based intrusion detection systems (IDS). In this paper, we study the…
The increase in network attacks has necessitated the development of robust and efficient intrusion detection systems (IDS) capable of identifying malicious activities in real-time. In the last five years, deep learning algorithms have…
Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore,…
This paper presents a simple yet efficient method for an anomaly-based Intrusion Detection System (IDS). In reality, IDSs can be defined as a one-class classification system, where the normal traffic is the target class. The high diversity…