This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.

The growing use of Wireless Sensor Networks (WSNs) in Internet of Things (IoT) applications requires innovation in energy-efficient and secure communication protocols to improve network functionality and lifetime. Legacy routing protocols struggle to achieve an equilibrium between energy usage, security, and information accuracy, resulting in excessive node failure rates and transmission errors. Compared to previous methods, this candidate RK-EFCRP is the first to combine a rank-based dynamic key update protocol with the hierarchy clustering hierarchical structure of the hybrid RK-tree, and can comprehensively optimize both routing and cryptography security, within the same lightweight structure. To solve these problems, this paper presents an enhanced Rank-Based Key Management for Energy-Efficient Cluster-Based Routing Protocol (RK-EFCRP) which implements a new hierarchical clustering method with Adaptive Cluster Head (CH) selection and a novel rank-based key assignment scheme to enhance the system’s stability, security, and energy efficiency. The effectiveness of RK-EFCRP is compared with the last mile safety routing for low power WSN (Safety-RPL) protocol, Hybrid Blockchain Enabled Energy Efficient Routing (HBEER), Secure and Energy Efficient Hierarchical Routing (SEHR), and Secure and Energy Aware Multi-Hop Hierarchical Routing (SEAMHR) protocol using energy efficiency, security strength, latency, and packet delivery ratio (PDR), and overall network lifetime as key performance indicators (KPIs). Experimental results demonstrate that RK-EFCRP significantly reduces energy consumption to 6.13 J, outperforming other protocols like SEAMHR (11.20 J). Additionally, RK- EFCRP enhances security strength to 97.5%, surpassing SEAMHR (85%) and HBEER (78%) as well. Moreover, unlike existing protocols that operate between 1.5 and 2.3 ms, RK-EFCRP reduces latency to 0.12 ms. The protocol has an improved PDR of 97.8%, surpassing Safety-RPL (91.5%) and the other models. Lastly, network lifetime is improved and extended to 3200 rounds compared to SEAMHR's (2100 rounds). From these results, it is evident that RK-EFCRP optimizes the balance of energy efficiency, security, and data reliability makes RK-EFCRP performing well for WSN based IoT applications. Rank based key allocation along with dynamic CH selection provides effective solutions to security holes and cuts down failure transmission while boosting overall network performance. This research facilitates the next steps towards intelligent and secure WSN based IoT systems with flexible and robust network structures.