Core‐level cybersecurity assurance using cloud‐based adaptive machine learning techniques for manufacturing industry

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Abstract
Cybersecurity is the domain that ensures safeness in both individual system and overall network systems. The classification and learning approaches used in different machine learning (ML) techniques improve the protection of the cyber systems against various attacks. Techniques such as support vector machine (SVM), neural networks (NN), principle component analysis (PCA), and reinforcement learning (RL) are used against various cyber threats. Applying these techniques at the front‐end services (either online or offline) makes less effect than back end process‐level services of any computer system. The proposed work analyzes the benefits of implementing customized ML and deep learning (DL) techniques on the core of the operating system than application level services, which in effect increases the speed and correctness of attack detection. The core (kernel) of the operating system has the capability to extract all internal attributes of process and file systems. The kernel space security activities can be improved by proposed work where the process level attributes classified using ML and DL techniques. The cloud service helps in sharing of the kernel abilities of the system ensuring core level security. The following work uses recurrent NN (RNN), SVM, PCA, and RL for analyzing the system data collected using Process Explorer. This technique finds application in manufacturing domain where the systems are protected from the various attacks to secure the data of the manufacturing company.

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