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As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the Internet of Things. Besides, the…

The ability to centrally control network infrastructure using a programmable middleware has made Software-Defined Networking (SDN) ideal for emerging applications, such as immersive environments. However, such flexibility introduces new…

Smart grids are replacing conventional power grids due to rising electricity use, failing infrastructure, and reliability problems. Two-way communication, demand-side administration, and real-time pricing make smart grids (SGs) dependent on…

Context: Automated software defect prediction (SDP) methods are increasingly applied, often with the use of machine learning (ML) techniques. Yet, the existing ML-based approaches require manually extracted features, which are cumbersome,…

Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a…

As more devices connect to the internet, it becomes crucial to address their limitations and basic security needs. While much research focuses on utilizing ML and DL to tackle security challenges, there is often a tendency to overlook the…

There have been numerous works on network intrusion detection and prevention systems, but work on application layer intrusion detection and prevention is rare and not very mature. Intrusion detection and prevention at both network and…

Due to their massive success in various domains, deep learning techniques are increasingly used to design network intrusion detection solutions that detect and mitigate unknown and known attacks with high accuracy detection rates and…

Cybersecurity of Industrial Control Systems (ICS) is drawing significant concerns as data communication increasingly leverages wireless networks. A lot of data-driven methods were developed for detecting cyberattacks, but few are focused on…

While Deep Neural Networks (DNNs) excel in many tasks, the huge training resources they require become an obstacle for practitioners to develop their own models. It has become common to collect data from the Internet or hire a third party…

In this paper, we propose a novel hybrid deep learning architecture that synergistically combines Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and multi-head attention mechanisms to significantly enhance cybersecurity…

The increasing reliance on software in various applications has made the problem of software vulnerability detection more critical. Software vulnerabilities can lead to security breaches, data theft, and other negative outcomes. Traditional…

In our current society, the inter-connectivity of devices provides easy access for netizens to utilize cyberspace technology for illegal activities. The deep web platform is a consummative ecosystem shielded by boundaries of trust,…

Software defined network (SDN) provides technical support for network construction in smart cities, However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection methods have complex algorithms…

Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. Machine learning has emerged as a popular approach for intrusion detection due to its ability to analyze and detect patterns in large…

Software-defined networking (SDN) has shifted network management by decoupling the data and control planes. This enables programmatic control via software applications using open APIs. SDN's programmability has fueled its popularity but may…

Intrusion detection is a critical component of cybersecurity, responsible for identifying unauthorized access or anomalous behavior in computer networks. This paper presents a comprehensive study on intrusion detection in networks using…

As Artificial Intelligence (AI) technologies continue to gain traction in the modern-day world, they ultimately pose an immediate threat to current cybersecurity systems via exploitative methods. Prompt engineering is a relatively new field…

Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without requiring clients to share their private local data. The DNN…

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…