THE EFFECT OF ARTIFICIAL INTELLIGENCE MODELS IN PREDICTING RANSOMWARE ATTACKS: A CASE STUDY OF FEDERAL MINISTRIES, ABUJA, FCT
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- Table of Content: Available
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- NGN 5000
CHAPTER ONE
1.1 Background of the Study
Ransomware attacks are among the most devastating forms of cybercrime, targeting organizations to encrypt their critical data and demand a ransom for its release. Federal Ministries in Abuja, being custodians of sensitive government data, are particularly vulnerable to ransomware attacks. These attacks disrupt operations, compromise national security, and result in significant financial losses. Traditional cybersecurity measures often fail to provide adequate protection due to the evolving tactics of cybercriminals.
Artificial Intelligence (AI) models have proven effective in predicting ransomware attacks by analyzing patterns, detecting anomalies, and identifying vulnerabilities before an attack occurs. AI systems utilize machine learning algorithms to process vast amounts of data, flagging suspicious activities in real time. This proactive approach reduces response times and minimizes damage.
This study explores the application of AI models in predicting ransomware attacks on Federal Ministries in Abuja, FCT, highlighting their potential to enhance cybersecurity resilience in government institutions.
1.2 Statement of the Problem
Federal Ministries in Abuja face a growing threat of ransomware attacks that compromise sensitive data and disrupt critical functions. Traditional cybersecurity measures often fail to predict these attacks effectively, leaving organizations vulnerable. This study investigates the role of AI models in predicting ransomware attacks and reducing their impact.
1.3 Aim and Objectives of the Study
The aim of this study is to evaluate the effectiveness of AI models in predicting ransomware attacks in Federal Ministries in Abuja. The specific objectives are:
- To assess the accuracy of AI models in predicting ransomware attack patterns.
- To identify challenges in implementing AI-based ransomware prediction systems.
- To determine the impact of AI-driven predictions on cybersecurity outcomes in Federal Ministries.
1.4 Research Questions
- How effective are AI models in predicting ransomware attacks in Federal Ministries?
- What challenges do Federal Ministries face in adopting AI-based ransomware prediction systems?
1.5 Research Hypotheses
- AI models significantly improve the prediction of ransomware attacks in Federal Ministries.
- The implementation of AI-driven prediction systems enhances cybersecurity resilience.
- Resource constraints hinder the adoption of AI models for ransomware prediction.
1.6 Significance of the Study
This study provides insights into the potential of AI models to enhance cybersecurity in Federal Ministries, offering recommendations for policymakers and IT administrators. It highlights AI’s critical role in mitigating ransomware threats in government institutions.
1.7 Scope and Limitation of the Study
The study focuses on the use of AI models to predict ransomware attacks in Federal Ministries in Abuja, FCT. It excludes other forms of cyberattacks and private sector organizations. Limitations include access to cybersecurity systems and the availability of attack data.
1.8 Definition of Terms
Artificial Intelligence (AI): Systems capable of simulating human intelligence for analyzing data and predicting outcomes.
Ransomware: A type of malicious software that encrypts data and demands a ransom for its release.
Federal Ministries: Government institutions responsible for policy formulation and administration.
Cybersecurity: Measures and technologies used to protect systems, networks, and data from cyber threats.
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