In today's digitally connected world, safeguarding information is paramount for organizations. With the increasing complexity of digital systems and the rising number of cyber threats, information security and cybersecurity risk management has become a necessary function for today’s business. Auditing is in place to assess and mitigate risks of information security. However, the audit process can be intimidating and complex. It involves a broad spectrum of methods that analyze existing controls in a variety of information security and cybersecurity areas. To reduce risks and enhance compliance with standards, the plan has corrective actions for all controls in the checklist. Although most ISMSs can co-exist, most organizations have a single ISMS with their respective tools and processes. This setup may limit flexibility and diversity, making the overall result of audit assessments less effective. Moreover, using outdated tools and processes makes it difficult to provide answers to questions about cybersecurity management and may cause gaps in fulfilling requirements. This gap makes organizations susceptible to threats and possible non-compliance with regulations. In general, SMEs have different auditing requirements from large-scale businesses, and existing ISMSs do not support them comprehensively at the controlling level. In short, existing ISMSs are not suitable for auditing SMEs. To overcome these issues, we have developed an end-to-end system that encompasses all aspects of cybersecurity auditing within one platform: the ISO27K1 Toolkit, designed to meet user preferences. Its advanced web interface has improved automated information security auditing. The system is flexible and dynamic, supporting multiple auditing methods and the integration of predefined control checklists and corresponding mitigation actions. This paper describes the development of the ISO27K1 Toolkit, designed based on ISO 27001:2022 controls and future-proofed for compatibility with the NIST-CSF and PCI DSS standards. The ISO27K1 Toolkit automates critical security assessment tasks, improving user experience and supporting audit processes while promoting compliance, efficiency, and proactive risk management. Large-scale business organizations and SMEs can take advantage of the relational database of the ISO 27K1 Toolkit, which maintains the latest auditing and intervention data. The project was implemented among 35 SMEs and focused on information security audits and system assessments according to ISO 27001:2022. This document presents the system architecture and design, as well as the results of global testing.

Underwater image restoration is significant for various applications such as ecological evaluation, exploration, searching and rescue operations, and autonomous vehicle navigation. In underwater environments, spatial images are frequently degraded as a result of light scattering, absorption, sensor noise, and reduced contrast. This research proposes a whole framework with deep learning that simultaneously performs restoration and enhancement. At a single stage, solving the problems of underwater image degradation in a holistic approach. The core of the proposed approach in this study is a Denoising Convolutional Neural Network (DnCNN) architecture. Where the suppression of noise takes place with extreme focus on significant detail by an advanced non-local attention mechanism. For the further natural color restoration, multi-color space transformations RGB, LAB, and HSV. Which come into play for enhancing the contrast adjustment and color correction of contrast and a correction of colors enabling effective correction of deep-sea views. The framework takes advantage of both synthetically modified images and actual underwater images for model training which offers enhanced generalization for various settings. For the evaluation of the developed approach, two datasets of underwater images, EUVP and LSUI, were used. For the evaluation of the developed approach, two datasets of underwater images, EUVP and LSUI, were used. For the EUVP dataset, this model produces a PSNR of 30.77 dB, an SSIM of 0.892, RMSE of 0.065, and NIQE of 3.52. It produces a PSNR of 29.90 dB, SSIM of 0.881, RMSE of 0.071, and NIQE of 3.82 for the LSUI dataset. As mentioned earlier these results outperform the baseline DnCNN model and show consistent performance with varying underwater conditions. Accompanying the performance results, the model achieves high fidelity results while being lightweight, real-time processing.

Introduction/Importance of Study: A novel innovative technique known methodical approach is referring as cloud computing (CC), which allows users to store data on remote servers that are accessible through the internet. This method makes it simple to move and retrieve vital and personal data storage. As a result, the demand for it is rising daily. This can be used to store a variety of data, including multimedia content, paperwork-based files, and financial transactions. Furthermore, by lowering operating and maintenance expenses, CC lessens the reliance of the services on local storage. Novelty statement: Current systems apply only one key size with which all data is encrypted without concerning the level of privacy of the data. This results in higher processing costs and longer processing times. Furthermore, none of these methods improves secrecy and only achieves a low accuracy rate in data classification. Material and Method: This study presents a cloud computing strategy for data sensitivity that is based on automated data classification. The model suggested in this study utilizes Random Forest (RF), Naïve Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM) classifiers to achieve automated feature extraction. This methodology is designed to operate effectively across three sensitivity levels: basic, confidential, and highly confidential. Results and Discussion: The experiments were performed on the Reuters-21578 dataset, which consists of 21,578 documents. The simulation results demonstrated that the three proposed models achieved accuracy rates of 97%, 96%, and 95%, respectively. These findings indicate that SVM, RF, and KNN outperform NB in classification performance. Concluding Remarks: Additionally, the suggested study offers helpful recommendations for researchers and cloud service providers (like Dropbox and Google Drive).