Exploring the Use of AI in Automated Network Security Auditing in Federal University, Wukari, Taraba State

Exploring the Use of AI in Automated Network Security Auditing in Federal University, Wukari, Taraba State

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Background of the Study

With the rapid evolution of cyber threats, traditional network security auditing methods have become insufficient in identifying and mitigating vulnerabilities within university networks. Federal University, Wukari, Taraba State, like many academic institutions, relies on its network infrastructure for research, communication, and administrative operations. However, maintaining a secure network environment requires continuous auditing to detect potential security risks and ensure compliance with security policies.

Automated network security auditing leverages Artificial Intelligence (AI) to enhance the efficiency and accuracy of security assessments. AI-driven security tools can analyze large volumes of network data, detect anomalies, and predict potential threats with minimal human intervention. Recent advancements in AI, such as machine learning algorithms and deep learning models, have improved the ability of security auditing systems to detect and respond to cyber threats in real time (Wang et al., 2024).

AI-based security auditing offers several advantages over traditional manual approaches, including reduced operational costs, faster detection of security breaches, and adaptive threat response capabilities. According to the IEEE Cybersecurity Initiative (2024), AI-driven security auditing systems can proactively identify weaknesses in network configurations and recommend corrective actions before vulnerabilities are exploited.

Despite the benefits, the adoption of AI in network security auditing presents challenges, including data privacy concerns, the complexity of AI model training, and the risk of adversarial attacks. Understanding how AI-based security auditing can be effectively integrated into Federal University, Wukari’s network infrastructure is crucial for enhancing the institution’s cybersecurity posture. This study aims to explore the potential of AI-driven security auditing in improving network security at the university.

Statement of the Problem

Network security breaches in academic institutions have become a growing concern, with increasing incidents of data breaches, unauthorized access, and malware infections affecting university networks. Traditional security auditing methods, which rely heavily on manual inspection and rule-based analysis, are often time-consuming and prone to human error. Federal University, Wukari, faces similar challenges in maintaining a secure and resilient network infrastructure.

One of the key limitations of conventional security auditing approaches is their inability to adapt to evolving cyber threats in real time. Cyber attackers constantly develop sophisticated techniques to bypass security defenses, making it necessary for universities to adopt more advanced security auditing mechanisms. AI-powered network security auditing has the potential to address these challenges by automating vulnerability assessments, detecting anomalies, and providing predictive insights to prevent cyber attacks.

However, the integration of AI-driven security auditing tools into the university’s existing cybersecurity framework requires careful evaluation. There is limited research on the effectiveness of AI in academic network security auditing within the Nigerian university context. This study seeks to fill this gap by investigating the role of AI in automated network security auditing at Federal University, Wukari, and identifying the benefits and challenges associated with its implementation.

Objectives of the Study

1. To evaluate the current network security auditing methods used at Federal University, Wukari.

2. To explore the potential of AI in automating network security auditing processes.

3. To propose an AI-driven security auditing framework for improving network security at the university.

Research Questions

1. What network security auditing methods are currently employed at Federal University, Wukari?

2. How can AI enhance the automation of network security auditing processes?

3. What are the challenges and benefits of implementing AI-based security auditing in the university’s network?

Significance of the Study

This study will contribute to the field of cybersecurity by examining the role of AI in enhancing network security auditing at Federal University, Wukari. The findings will help university IT administrators understand the benefits of AI-driven auditing systems and provide recommendations for their effective implementation. The research will also serve as a reference for policymakers and cybersecurity professionals interested in leveraging AI for network security in academic institutions.

Scope and Limitations of the Study

The study is limited to Federal University, Wukari, Taraba State, and focuses on AI-driven automated network security auditing. It will assess existing security auditing methods, explore AI-based solutions, and propose an implementation framework for the university’s network security. The study does not cover other cybersecurity aspects such as endpoint security or cloud security.

Definitions of Terms

• Automated Network Security Auditing: The use of AI and machine learning techniques to assess network security, detect vulnerabilities, and recommend corrective actions without manual intervention.

• Artificial Intelligence (AI): The simulation of human intelligence in machines, enabling them to analyze data, learn from patterns, and make decisions.

• Adversarial Attacks: Cyber attacks that manipulate AI models by introducing deceptive inputs to mislead security systems.

 

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