Optimized Security and Design-Based System Enhancement for Cyber-Physical Infrastructure

To cope complexity and multidisciplinary of CPS architecture, this document suggests to use design-based optimization solution to improve its security. CPS consist of elements of dissimilar nature dispersed in physical entities, hardware, and networks to form a problem in maintaining consistent quality and dependability of the system. To overcome this, the developed model combines swarm intelligence in the form of Darwinian Particle Swarm Optimization (DPSO) and a Decision Tree (DT) constructed with stacked auto-encoder. This dual level of resolving complexity increases security since both levels are efficient in terms of system complication as well as the number of computations required. The model thus preprocesses the data and selects features from the similar NSL-KDD dataset that is globally accredited as a cybersecurity benchmark. It equally and systematically improves CPS security through a process of evaluating metrics like reliability, recall, F-score and error rate. From the proposed optimization model, it is evident that there is even better optimization opportunities offered in CPS by the avoidance of redundancy, a straightforward method of feature selection and increased security in measures. By such developments, the study shows a feasible, efficient method of establishing security foundation within a large, integrated CPS network, which has a wide applicability where immediate threat identification and system safety is critical.