Identifying the Features of the Various Cyber Dataset for Ensuring Cyber Security using Hybrid Optimization Techniques and Machine Learning (ML)

Cyber-attacks are continuously evolving and widespread, necessitating ongoing management adjustments to counter cyber security concerns. Despite the fact that the hardware and software components required for this defences are typically homogeneous and inexpensive, detecting the faults requires highly specialized skills. As a consequence, cyber-security is a complex skill that cannot be acquired simply by purchasing technological components; rather, it requires expert knowledge. It is vital to research and use suitable quality assurance technologies, such as different inspection techniques and test methodologies, in order to safely manage the application processes of cyber security control. As a consequence, optimization and machine learning approaches are employed in our study to find and choose particular aspects of the dataset to assure detecting network intrusions attacks and the development of an efficient Cyber Security system. A hybrid optimization approach was used for feature selection to enhance efficiency;it combines elements of the ABC and Grasshopper optimization techniques (GOA). The assaults are also detected within use of a support vector machine (SVM), as well as the proposed technique is compared to existing algorithms according to performance metrics such as accuracy, sensitivity, & specificity to illustrate its effectiveness.