Related papers: A Comparative Study of AI-based Intrusion Detectio…
Today by growing network systems, security is a key feature of each network infrastructure. Network Intrusion Detection Systems (IDS) provide defense model for all security threats which are harmful to any network. The IDS could detect and…
Intrusion Detection Systems (IDS) are critical components in safeguarding 5G/6G networks from both internal and external cyber threats. While traditional IDS approaches rely heavily on signature-based methods, they struggle to detect novel…
The Internet of Things (IoT) has significantly expanded the digital landscape, interconnecting an unprecedented array of devices, from home appliances to industrial equipment. This growth enhances functionality, e.g., automation, remote…
Intrusion detection systems (IDS) help detect unauthorized activities or intrusions that may compromise the confidentiality, integrity or availability of a resource. This paper presents a general overview of IDSs, the way they are…
As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as…
Due to an exponential increase in the number of cyber-attacks, the need for improved Intrusion Detection Systems (IDS) is apparent than ever. In this regard, Machine Learning (ML) techniques are playing a pivotal role in the early…
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…
Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and…
Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed to the ability of machine learning algorithms to identify…
In recent years, as intrusion attacks on IoT networks have grown exponentially, there is an immediate need for sophisticated intrusion detection systems (IDSs). A vast majority of current IDSs are data-driven, which means that one of the…
The application of Artificial Intelligence (AI) and Machine Learning (ML) to cybersecurity challenges has gained traction in industry and academia, partially as a result of widespread malware attacks on critical systems such as cloud…
UAVs have the potential to revolutionize urban management and provide valuable services to citizens. They can be deployed across diverse applications, including traffic monitoring, disaster response, environmental monitoring, and numerous…
The evolution of the traditional power grid into the "smart grid" has resulted in a fundamental shift in energy management, which allows the integration of renewable energy sources with modern communication technology. However, this…
Network intrusions have become a significant threat in recent years as a result of the increased demand of computer networks for critical systems. Intrusion detection system (IDS) has been widely deployed as a defense measure for computer…
Unmanned Aerial Vehicles (UAVs) are becoming more dependent on mission success than ever. Due to their increase in demand, addressing security vulnerabilities to both UAVs and the Flying Ad-hoc Networks (FANET) they form is more important…
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.…
Intrusion detection is a long standing and crucial problem in security. A system capable of detecting intrusions automatically is on great demand in enterprise security solutions. Existing solutions rely heavily on hand-crafted rules…
Machine learning, statistical-based, and knowledge-based methods are often used to implement an Anomaly-based Intrusion Detection System which is software that helps in detecting malicious and undesired activities in the network primarily…
Recent Intrusion Detection System (IDS) research has increasingly moved towards the adoption of machine learning methods. However, most of these systems rely on supervised learning approaches, necessitating a fully labeled training set. In…
As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing central- ized control and flexible resource management. But it is also confronted with various security challenges and potential…