Development of a machine learning algorithm for predicting cyber attacks



Abstract:

With the increase in cyber attacks globally, there is a growing need for effective cybersecurity measures to protect organizations and individuals from potential threats. One way to enhance cybersecurity is through the development of machine learning algorithms that can predict and prevent cyber attacks before they occur. This thesis aims to develop a machine learning algorithm for predicting cyber attacks by analyzing patterns and trends in historical data.

Chapter 1: Introduction
This chapter provides an overview of the research topic, rationale for the study, research questions, and scope of the study. It also includes the significance of the study, objectives of the study, study hypothesis, and limitations of the study.

Chapter 2: Literature Review
This chapter reviews existing literature on cyber attacks, machine learning algorithms, and predictive analytics in cybersecurity. It discusses the current state of research in predicting cyber attacks and identifies gaps in the literature that the study aims to address.

Chapter 3: Research Methodology
This chapter outlines the research design, data collection methods, machine learning algorithms used, and evaluation metrics for assessing the performance of the algorithm. It also discusses the dataset used for training and testing the algorithm.

Chapter 4: Discussion of Findings
This chapter presents the results of the study, including the performance of the machine learning algorithm in predicting cyber attacks. It discusses the implications of the findings and compares the algorithm’s performance with existing methods in cybersecurity.

Chapter 5: Summary, Recommendation, and Conclusion
This chapter summarizes the key findings of the study, provides recommendations for future research, and concludes the thesis with implications for cybersecurity practice. It suggests ways in which the developed algorithm can be implemented in real-world scenarios to enhance cybersecurity measures.

In conclusion, the development of a machine learning algorithm for predicting cyber attacks has the potential to revolutionize cybersecurity practices and provide organizations with proactive measures to prevent potential threats. By analyzing historical data and trends, this algorithm can predict and prevent cyber attacks before they cause significant damage. Further research is needed to refine and optimize the algorithm for real-world applications.


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