Global Network Cyberattack Classification Using Naive Bayes Method Time Range 2020 – 2023 - Sandi Mutia, Acep; Irawan, Irawan; Juliane, Christina (ASTONJADRO, 2024)
This study focuses on developing a classification model for cyberattacks on global networks during the time span of 2020 to 2023 using the Naive Bayes method. The main objective of the study is to analyze and classify the frequent severity of cyber, which helps in improving network security and reducing vulnerabilities. The Naive Bayes method was chosen for its efficiency in handling large datasets and its ability to make predictions based on probabilities. Collecting cyberattack data from a variety of reliable and up-to-date sources, the study covers attacks such as ransomware, phishing, DDoS, and other malware. The classification process includes data pre-processing, feature extraction, and finally the application of Naive Bayes algorithms to identify patterns in such attacks. The classification results are then evaluated using the Apply Model and Performance validation methods to assess the effectiveness of the model. The results of this study show that Naive Bayes is able to accurately classify cyberattacks, providing a useful tool for cybersecurity professionals to understand attack trends and respond proactively. The study also suggests areas for further research, including the integration of the Naive Bayes model with other artificial intelligence systems for improved cyberattack detection. The study provides new insights into the application of the Naive Bayes method in cybersecurity and paves the way for improved data-driven cyber defense strategies.