Abstract
In recent years, different types of threats and attacks continue to increase in the internet world. It is important to detect anomalies in the time series quickly and accurately for web traffic data measured by the number of online visitors. Different methodologies and data classification techniques are used to detect abnormal traffic in network data. This problem is generally evaluated by classifying the signal windows by feature extraction. In this study, a method based on the Negative Selection Algorithm (NSA) of Artificial Immune Systems for the detection of abnormal web traffic on the network is proposed and a user-friendly application software is developed. For web traffic, the real data in the Yahoo Webscope S5 dataset is used and the data is split into windows using the window shift method. In the experimental studies, the detection of abnormal traffic data in the web traffic data is realized by monitoring the changes in the number of activated detectors in the NSA structure. On the application software developed in the study, it is observed that abnormal conditions in the web traffic data are detected with low error rates with NSA.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
