Abstract
The next revolution of smart industry relies on the emergence of Industrial Internet of Things (I) and 5G/6G technology. The proprieties of such sophisticated communication technologies will change our perspective of information and communication by enabling seamless connectivity and bring closer entities, data, and ‘things’. Terahertz-based 6G networks promise the best speed and reliability, but they will face new man-in-the-middle attacks. In such critical and high-sensitive environments, security of data and privacy of information still a big challenge. Without privacy-preserving considerations, the configuration state may be attacked or modified, thus causing security problems and damage to data. In this article, motivated by the need to secure 6G IoT networks, an ant colony optimization (ACO) approach is presented by adopting multiple objectives as well as using transaction deletion to secure confidential and sensitive information. Each ant in the population is represented as a set of possible deletion transactions for hiding sensitive information. We utilize the use of a pre-large concept to assist in the reduction of multiple database scans in the evaluation progress. We then also adopt external solutions to maintain discovered Pareto solutions, thus improving effectiveness to find optimized solutions. Experiments are conducted comparing our methodology to state-of-the-art bio-inspired Particle Swarm Optimization (PSO) as well as Genetic Algorithm (GA). Our strong results clearly show that the designed approach achieves fewer side effects while maintaining low computational cost overall g1.