With the rapid development of technology, significant progress has been observed regarding the Internet and interconnected devices, increasing the risk of cyberattacks targeting these platforms. These attacks take diverse and sophisticated forms and pose a serious threat to companies, potentially causing substantial financial losses and service disruptions. In response, the pressing need exists to develop robust defense strategies. This research focuses on analyzing attacks on information systems, specifically concentrating on network forensics using machine learning techniques. The initial phase involves executing various attack scenarios in a virtual environment, recording network packets, and extracting relevant features to create a dataset. A classification framework is then created that includes machine learning algorithms such as random forest, support vector machine (SVM), and Naïve Bayes. Comparing the performance of these algorithms on the study’s dataset has revealed the random forest algorithm to achieve the highest accuracy rate at 94.8%, with Naive Bayes having the lowest at 78.9
Machine learning cyberthreat network forensics classification algorithms intrusion detection system
Primary Language | English |
---|---|
Subjects | Software Engineering (Other) |
Journal Section | Research Article |
Authors | |
Publication Date | June 28, 2024 |
Submission Date | February 28, 2024 |
Acceptance Date | May 9, 2024 |
Published in Issue | Year 2024 Volume: 8 Issue: 1 |