Recent Papers

The significance of cyber security has experienced substantial growth in addressing common network communication issues. To mitigate the risk of unauthorized access to resources, services, and networks, recent research is largely focused on the field of network security. Cyber-Physical Systems (CPS) encompass the integration of several components such as sensing, computation, regulation, and networking into tangible infrastructure and equipment. This integration facilitates their connectivity to the Internet and enables seamless communication among these components. Given the increasing threat posed by cyber-related dangers, it is imperative to develop robust and accurate systems. The primary objectives of this study are to examine different faults and respective security measures to safeguard against potential threats and provide support to many businesses and internet users. This will ultimately enhance the overall security of CPS. The objectives are to offer a blend of adaptability and efficiency to enhance security measures pertaining to CPS and establish a robust foundation for an advanced cloud-based Intrusion Detection System (IDS). The proposed models are being created with the intention of fulfilling two objectives for this purpose. The proposed CPS model utilizes a dual mutation-based genetic approach to detect and eliminate faults in the smart manufacturing process. The identified faults are then classified using Ada-boost and Enhanced Support Vector Machine (E-SVM) classifiers. These classifiers contribute to the system's control and monitoring capabilities, specifically in safeguarding against unauthorized network access. The results of the comparison indicate that the suggested model outperformed other Current approaches, demonstrating a higher level of accuracy at 95.18%. The second proposed Approach ology places emphasis on safeguarding user privacy through the implementation of measures that prevent unauthorized access to user information. The Current approach demonstrates a maximum reduction of 14.89% in key generation time, 16.67% in encryption time, and 12.5% in decryption time.

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