Enhancing Cybersecurity through Bayesian Node Profiling and Attack Classification

Due to the epidemic, the majority of users and businesses turned to the internet, necessitating the necessity to preserve the populace and safeguard their data. However, after being attacked, the expense of data protection runs into the millions of dollars. The phrase "Protection is better than cure" is true. The paper deals with profiling the node for safeguarding against the cyberattack. There is a lot of research on network nodes. Here, we address the requirement to profile the node before utilizing machine learning to separate the data. In order to scan the nodes for risks and save the nature of threat as a database, node profiling is being investigated. The data is then classified using a machine learning algorithm utilizing the database. This research focuses on the application of machine learning methods, specifically Gaussian Naive Bayes and Decision Trees, for the segmentation of cyberattacks in streaming data. Given the continuous nature of cyberattack data, Gaussian Naive Bayes is introduced as a suitable approach. The research methodology involves the development and comparison of these methods in classifying detected attacks. The Bayesian method is employed to classify detected attacks, emphasizing the use of Gaussian Naive Bayes due to its adaptability to streaming data. Decision Trees are also discussed and used for comparison in the results section. The research explores the theoretical foundations of these methods and their practical implementation in the context of cyberattack classification. After classification, the paper delves into the crucial task of identifying intrusions in the streaming data. The effectiveness of intrusion detection is highlighted, emphasizing the importance of minimizing false negatives and false positives in a real-world cybersecurity setting. The implementation and results section presents empirical findings based on the application of Gaussian Naive Bayes and Decision Trees to a dataset. Precision, recall, and accuracy metrics are used to evaluate the performance of these methods. The research concludes by discussing the implications of the findings and suggests that Gaussian Naive Bayes is a suitable choice for streaming data due to its adaptability and efficiency. It also emphasizes the need for continuous monitoring and detection of cyberattacks to enhance overall cybersecurity. The paper provides insights into the practical applicability of these methods and suggests future work in the field of intrusion detection.