In recent decades, wireless communication has made a remarkable impact on a diverse range of fields. Research labs, financial markets, media, and weather forecasting have all adopted high-performance computing to accomplish parallel processing, live streaming, reliable information technology, and predictive analytics. In some computationally intensive applications, such as meteorology or deep neural network training, fast network interface cards (NICs) are used to boost network performance and data-path speed. More specifically, in many Internet of Things (IoT) applications, bio-inspired communication approaches are used for self-organising and self- configuration to drive the network without disruption. In large-scale networks, present wireless communication employs key enabling technologies like massive multiple-input and multiple-output (MIMO), millimetre-wave spectrum, network ultra-densification, and scalable IoT to perform duplex communications, high bandwidth allocation, seamless coverage, and network slicing.

Although it has a variety of advantages in diversified data-driven fields, wireless communication is vulnerable to ransomware, wireless network sniffing, others gaining control of the router, rogue access points, wireless zero-configuration, and wireless hijacking by cybercriminals and eavesdroppers across the network. To enhance the security of such data-driven networks, several natural and bio-inspired algorithms have been modelled to perform secure, high-performance computing in computationally intensive applications. For instance, swarm intelligence for wireless communication is a bio-inspired approach that consists of a comprehensive set of machine learning algorithms used to classify the optimal parameters, which in turn detect cyber-attacks in wireless networks. Honeybee-based bio-inspired algorithms with foraging methods are widely used for their self-organising features in distributed networks to detect and alleviate attacks in a system. Bio-inspired epidemic algorithms have been designed to detect malicious behaviour in communication paths by camouflaging adversary activities through transmissive attacks, and in military communications, algorithms based on bio-inspired radio frequency (RF) steganography have been modelled to prevent eavesdroppers from hearing information by communicating through encrypted chirp radar signals. Thus, nature and bio-inspired algorithms are beneficial in enhancing security and network performance in high performance distributed computing environments.

This Special Issue aims to address challenges and security risks that have not yet been considered in existing nature and bio-inspired approaches. It may also outline research and technology for nature and bio-inspired secured wireless communication to perform parallel processing and distributed computing in computationally intensive applications.

Potential topics include but are not limited to the following:

• Key enabling technologies for secure wireless communication
• Privacy, safety, and cyberattacks in wireless communication
• Security challenges and mitigation approaches in high performance distributed computing environments
• Cyberattack detection and prevention systems for computationally intensive applications
• Parallel and distributed computing techniques for secure wireless communication
• Bio-inspired protocols and mathematical models for cybersecurity in 5G wireless communication
• Particle swarm optimisation for secure bio-inspired computing
• Evolutionary algorithms for nature and bio-inspired computing in high performance distributed computing environments
• Bio-inspired security and networking algorithms