Higher education institutions (HEIs) are increasingly becoming vulnerable to cyberattacks as they adopt digital technologies to support their administrative, research and academic activities. These institutions, which typically operate in open and decentralized environments, face serious challenges as a result of the growing complexity of cyberattacks such as phishing, ransomware and data breaches. This systematic review synthesizes existing literature on cybersecurity in HEIs, identifying key challenges, emerging solutions and current trends. The review analyses the adoption of advanced technologies such as zero trust architectures (ZTAs), artificial intelligence (AI)-driven security and cloud-based systems. Furthermore, it investigates the underlying causes of cybersecurity vulnerabilities, including fragmented security procedures, lack of proper awareness about cybersecurity among users and associated technology gaps. The review also examines how governance frameworks, institutional policies and the incorporation of state-of-the-art security technologies can significantly mitigate these threats. Findings reveal that considerable progress has been made by some institutions in implementing security measures. However, comprehensive cybersecurity plans that integrate technological solutions with a robust institutional culture of cybersecurity awareness are still critically needed. The review concludes by highlighting the need for HEIs to collaborate and foster institution-wide partnership to strengthen cybersecurity measures. Finally, an in-depth study into the strategies and best practices for handling emerging cyberthreats in the HEIs is recommended.

Enterprise networking optimization has become crucial recently due to increasing demand for a secure, adaptable, reliable, and interoperable network infrastructure. Novel techniques to optimize network security and toimprove scalability and efficiency are constantly being developed by network enablers, particularly in more challenging multi-cloud and edge scenarios. This paper, therefore, presents a comprehensive review of the traditional and most recent developments in enterprise networking. We structure the paper with particular emphasis on the adoption of state of-the-art technologies, such as software-defined wide area network(SD-WAN), secure access service edge (SASE) architecture, and network automation, driven by artificial intelligence (AI). The review also identifies various challenges associated with the adoption of the aforementioned technologies. These include operational complexity, cybersecurity threats, and trade-offs between cost-effectiveness and high performance requirements. Furthermore, the paper examines how different organizations are addressing a plethora of challenges by exploiting these technological innovations to drive robust and agile business interconnectivity. The review is concluded with an outline of possible solutions and future prospects, capable of promoting digital transformation and enhancing seamless connectivity within the enterprise networking environment.

The limitations of conventional rule-based security systems have been exposed by the quick evolution of cyber threats, necessitating more proactive, intelligent, and flexible solutions. In cybersecurity, Artificial Intelligence (AI) has emerged as a transformative factor, offering improved threat detection, prediction, and automated response capabilities. This paper explores the advantages of using AI in strengthening cybersecurity, focusing on its applications in machine learning, Deep Learning, Natural Language Processing, and reinforcement learning. We highlight the improvement brought by AI in terms of real-time incident response, detection accuracy, scalability, and false positive reduction while processing massive datasets. Furthermore, we examine the challenges that accompany the integration of AI into cybersecurity, including adversarial attacks, data quality constraints, interpretability, and ethical implications. The study concludes by identifying potential future directions, such as integration with blockchain and IoT, Explainable AI and the implementation of autonomous security systems. By presenting a comprehensive analysis, this paper underscores exceptional potential of AI to transform cybersecurity into a field that is more robust, adaptive, and predictive.