Problem: The integration of Artificial Intelligence (AI) into cybersecurity, while enhancing threat detection, is hampered by the “black box” nature of complex models, eroding trust, accountability, and regulatory compliance. Explainable AI (XAI) aims to resolve this opacity but introduces a critical new vulnerability: the adversarial exploitation of model explanations themselves. Gap: Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector. There is a pressing need to systematically analyze the trade-offs between interpretability and security, evaluate defense mechanisms, and outline a path for developing robust, next-generation XAI frameworks. Solution: This review provides a systematic examination of XAI techniques (e.g., SHAP, LIME, Grad-CAM) and their applications in intrusion detection, malware analysis, and fraud prevention. It critically evaluates the security risks posed by XAI, including model inversion and explanation-guided evasion attacks, and assesses corresponding defense strategies such as adversarially robust training, differential privacy, and secure-XAI deployment patterns. Contribution: The primary contributions of this work are: (1) a comparative analysis of XAI methods tailored for cybersecurity contexts; (2) an identification of the critical trade-off between model interpretability and security robustness; (3) a synthesis of defense mechanisms to mitigate XAI-specific vulnerabilities; and (4) a forward-looking perspective proposing future research directions, including quantum-safe XAI, hybrid neuro-symbolic models, and the integration of XAI into Zero Trust Architectures. This review serves as a foundational resource for developing transparent, trustworthy, and resilient AI-driven cybersecurity systems.

Wireless technologies and the Internet of Things (IoT) are being extensively utilized for advanced development in traditional communication systems. This evolution lowers the cost of the extensive use of sensors, changing the way devices interact and communicate in dynamic and uncertain situations. Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system. Therefore, it leads to the design of effective and trusted routing to support sustainable smart cities. This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model, which combines big data analytics and analysis rules to evaluate user feedback. The sentiment analysis is utilized to assess the perception of network performance, allowing the classification of device behavior as positive, neutral, or negative. By integrating sentiment-driven insights, the IoT network adjusts the system configurations to enhance the performance using network behaviour in terms of latency, reliability, fault tolerance, and sentiment score. Accordingly to the analysis, the proposed model categorizes the behavior of devices as positive, neutral, or negative, facilitating real-time monitoring for crucial applications. Experimental results revealed a significant improvement in the proposed model for threat prevention and network efficiency, demonstrating its resilience for real-time IoT applications.

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification, which affect the testing process. Therefore, it is difficult to identify all faults in software. As requirement changes continuously, it increases the irrelevancy and redundancy during testing. Due to these challenges; fault detection capability decreases and there arises a need to improve the testing process, which is based on changes in requirements specification. In this research, we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment. The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis. Then compute the similarity of requirements through case-based reasoning, which predicted the requirements for reuse and restricted to error-based requirements. Afterward, the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate. Furthermore, the proposed model was evaluated by conducting experiments. The results showed that requirement redundancy and irrelevancy improved due to semantic analysis, which correctly predicted the requirements, increasing the fault detection rate and resulting in high user satisfaction. The predicted requirements are mapped into test cases, increasing the fault detection rate after changes to achieve higher user satisfaction. Therefore, the model improves the redundancy and irrelevancy of requirements by more than 90% compared to other clustering methods and the analytical hierarchical process, achieving an 80% fault detection rate at an earlier stage. Hence, it provides guidelines for practitioners and researchers in the modern era. In the future, we will provide the working prototype of this model for proof of concept.