Industrial Control Systems (ICS) are increasingly targeted by distributed denial-of-service (DDoS) attacks, posing significant risks to system availability and reliability. This research proposes a novel defensive framework for ICS networks based on Software-Defined Networking (SDN). The main objectives are to enhance resilience against DDoS attacks and maintain critical system functions. Our framework combines automated traffic control (ATC) to filter and bypass malicious traffic dynamically, and Moving Target Defense (MTD) techniques such as proactive IP shuffling and network redundancy to protect critical nodes. Experimental results show that the proposed approach effectively reduces CPU load, improves round-trip time (RTT), and lowers packet drop rate (PDR) during DDoS scenarios. These findings demonstrate that integrating SDN-based ATC and MTD strategies can significantly strengthen ICS security and ensure system availability, providing a robust solution for critical infrastructure protection.

In recent years, ransomware attacks have exploded globally, and it has become one of the most significant cyber threats to digital infrastructure. Such attacks have been targeting ranging from individuals to critical infrastructure or large organizations such as large commercial companies, energy facilities, medical centers and government departments. Ransomware attackers use sophisticated encryption techniques to hijack victims’ files in exchange for a large ransom to release encrypted data. Sophisticated encryption techniques make it almost impossible for victims to recover data without the secret key in the event of such an attack. To protect systems from ransomware threats, malicious activities had better be detected earlier, preferably before they engage in the harmful behavior. Numerous studies have focused on ransomware threats and attempted to provide detection and prevention solutions for ransomware attacks, but none of the surveys explored the early detection of ransomware and highlighted challenges and issues with existing solutions. This survey fills this gap and provides a state-of-the-art overview of research on the ransomware early detections. Moreover, we investigate the latest ransomware surveys and give an overview of the categories of ransomware from different perspectives, the evolution and attack process of ransomware, and provide datasets used for ransomware detection. Finally, the possible future research directions are discussed.

In recent years, Industrial Control Systems (ICSs) have faced increasing vulnerability to cyber attacks due to their integration with the Internet. Despite efforts to enhance cybersecurity, reconnaissance attacks remain a significant threat, prompting the need for innovative defensive strategies. This paper introduces a novel approach to strengthen the defensive capabilities of ICS networks against reconnaissance attacks using machine learning-driven cyber deception techniques. Leveraging Conditional Generative Adversarial Networks (CGANs), the proposed framework dynamically generates defensive network topologies to network shuffling and implement deception strategies, prioritizing system availability. Extensive simulations demonstrate the superior efficacy of the proposed framework in enhancing cybersecurity while minimizing computational overhead. By effectively mitigating reconnaissance attacks, this solution reinforces the resilience of ICS networks, safeguarding critical industrial infrastructure from evolving cyber threats. These findings underscore the significance of adopting machine learning-based cyber deception as a pragmatic security measure for protecting ICS networks in real-world industrial contexts.

The convergence of information technology (IT) and operation technology (OT) has made Industrial Control Systems (ICS) a popular target for cyberattacks in recent years. Unlike traditional networks, enhancing availability is the ICS network's top priority rather than confidentiality in the CIA scheme. We propose a bio-inspired adaptive defense framework based on dissimilar redundancy, diversity, and adaptive defense strategies to achieve this aim. The proposed mechanism mixed optimal network shuffling and cyber deception techniques to maximise the time attackers spend on the decoys. Besides, to provide an extra layer of protection for system availability, we introduce dual heterogeneous subnets in the proposed framework that could be regenerated once compromised. We evaluate the performance of the proposed defense framework in a typical industrial manufacturing network using an SDN-based platform and test the defense framework in various scenarios. Compared with previous research, the simulation shows a considerable improvement in defense performance in the adaptive defense mode.

The advent of the digital age and the internet has recently seen a corresponding increase in security concerns. Intrusion detection systems are one of the crucial factors to consider in today’s digital world. This paper proposes a metaheuristic algorithm based on quantum differential evolution with multiple strategies. This algorithm proposes a new differential mutation strategy approach to improve the search capability and convergence speed. Then, a quantum rotation gate is used to perform the secondary evolution of the population. Finally, various benchmark functions are chosen to demonstrate the optimization ability of the algorithm. The experimental results show that the model outperforms differential evolution and quantum differential evolution. And has better optimization capability, efficiency and stability. This evolutionary technique plays an important role in identifying network intrusions and security attacks.