Role of Machine Learning in Enhancing Image Encryption Techniques for Cybersecurity
Keywords:
Hybrid encryption, Traditional cryptographic techniques, Machine learning algorithms, Security, Performance, Cybersecurity, Encryption frameworkAbstract
In the context of cybersecurity, ensuring the privacy of the content of images, filled with potentially sensitive data, is crucial. Kosheen et al. , have used conventional approaches to encrypt images to protect it from unauthorized access; it has been noted that conventional methods have high risk of being defeated by new forms of threats. The following paper aims to understand how to apply machine learning (ML) in strengthening image encryption approaches to improve safety mechanisms. First, we introduce a brief explanation of what image encryption is and explain why traditional approaches are not completely effective. Furthermore, we are going to describe the appeared ML in the field of cybersecurity and correlated fields and analyze the possibilities of using it in image encryption. Explaining the detailed method of optimization of a broad encryption process, this paper outlines the correlation between machine learning algorithms such as neural network and deep learning techniques on the process of data encryption for optimum security and efficiency. Moreover, we introduce the benefits resulting from the combination of ML with encryption such as the increased resistance against attacks and the flexibility to respond to current threats. Yet there are concerns including interpretability of the models and the presence of adversarial examples. Last of all, we introduce the knowledge gaps that should be filled in future studies within the given domain. In conclusion, this paper emphasizes on the relevance of ML as an evolving tool towards the development of image encryption and enhancement of cybersecurity mechanisms in the rapidly evolving digital world.
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