An Assessment of the Role of Artificial Intelligence Models in Predicting Cyberattacks: A Case Study of Government IT Systems in Gombe State
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1.1 Background of the Study
With the growing reliance on digital technologies, government institutions in Nigeria are increasingly becoming targets of cyberattacks. Predicting these attacks before they occur has become a critical aspect of cybersecurity. Artificial Intelligence (AI) models, especially those employing machine learning algorithms, have shown immense potential in predicting cyberattacks by analyzing patterns in network traffic, user behavior, and system vulnerabilities (Alabi et al., 2024). By leveraging these models, government agencies can proactively protect sensitive data and systems.
Gombe State, located in northeastern Nigeria, has recently been exploring AI applications to enhance its cybersecurity infrastructure. The role of AI models in predicting cyberattacks on government IT systems within the state is a focal point of this study. This research will assess how AI is currently being used to predict cyberattacks on these systems and evaluate the effectiveness of such models in preventing potential security breaches.
1.2 Statement of the Problem
Despite significant advancements in cybersecurity, many government institutions in Nigeria still struggle with proactively identifying cyber threats before they cause damage. Traditional cybersecurity measures are reactive, addressing threats only after they manifest, which leads to delays in response and higher risk exposure. AI-driven predictive models offer an opportunity to shift from reactive to proactive cybersecurity, yet the implementation and effectiveness of such systems in Nigeria’s government IT systems remain underexplored. This study aims to assess the use and potential of AI models in predicting cyberattacks within Gombe State’s government IT systems.
1.3 Objectives of the Study
1. To evaluate the effectiveness of AI-driven predictive models in forecasting cyberattacks on government IT systems in Gombe State.
2. To assess the accuracy of AI models in detecting emerging cyber threats and vulnerabilities.
3. To identify the challenges and limitations in implementing AI predictive models for cybersecurity in government systems.
1.4 Research Questions
1. How effective are AI-driven predictive models in forecasting cyberattacks on government IT systems in Gombe State?
2. What is the accuracy of AI models in identifying potential vulnerabilities and threats within these systems?
3. What challenges and limitations do government agencies face in adopting AI-driven predictive models for cybersecurity?
1.5 Research Hypothesis
1. AI-driven predictive models are significantly more accurate in forecasting cyberattacks on government IT systems than traditional methods.
2. The use of AI models in cybersecurity leads to earlier detection of cyber threats and vulnerabilities within government IT systems.
3. Challenges such as limited data, technical expertise, and infrastructure hinder the full adoption of AI predictive models in government cybersecurity efforts.
1.6 Significance of the Study
This research is significant as it highlights the potential of AI in transforming cybersecurity practices within Nigerian government institutions. By adopting AI-driven predictive models, government agencies can better protect their digital assets, ensure the safety of citizens’ data, and prevent the disruption of critical services. The study’s findings will provide valuable insights for policymakers and IT professionals looking to enhance cybersecurity through AI.
1.7 Scope and Limitations of the Study
The study will focus on government IT systems in Gombe State, Nigeria, and will not extend to private institutions or other regions. Limitations include challenges in accessing data on cybersecurity incidents and AI model performance within government agencies.
1.8 Operational Definition of Terms
1. Predictive Models: AI algorithms that analyze data to forecast future events, such as cyberattacks, based on historical patterns.
2. Cybersecurity: The practice of protecting computer systems, networks, and data from unauthorized access or attacks.
3. Vulnerabilities: Weaknesses or flaws in a system that could be exploited by attackers to gain unauthorized access or cause damage.
4. Government IT Systems: Information technology infrastructure and software used by government agencies to store, process, and manage public sector data.
5. Machine Learning: A subset of AI that enables systems to learn from data and improve their performance without explicit programming.
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