Ağ güvenliği Web trafik verileri Anomali tespiti Yapay bağışıklık sistemleri Negatif seçim algoritması
Bu çalışmanın yazarları, çalışmada deneysel testler için kullanılan Yahoo Webscope S5 Veriseti kullanımına izin verdiği için Yahoo şirketine teşekkürlerini sunmaktadır.
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.
Network security Web traffic data Anomaly detection Artificial immune systems Negative selection algorithm
Primary Language | Turkish |
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Subjects | Engineering |
Journal Section | Articles |
Authors | |
Publication Date | October 31, 2019 |
Published in Issue | Year 2019 Special Issue 2019 |