![]() We also detail our process for collecting real-world representative Slow HTTP DoS attack traffic from a live network environment to create our datasets. In this work, we evaluate the use of data sampling to produce varying class distributions in order to counteract the effects of severely imbalanced Slow HTTP DoS big datasets. ![]() Machine learning techniques can be used to aid in detection, but the large level of imbalance between normal (majority) and attack (minority) instances can lead to inaccurate detection results. ![]() The issue, is that the number of legitimate traffic instances can far outnumber the amount of attack instances, making detection problematic. Successful mitigation of this attack type requires network analysts to evaluate large quantities of network traffic to identify and block intrusive traffic. Slow HTTP Denial of Service attacks are one such attack variant, which targets the HTTP protocol and can imitate legitimate user traffic in order to deny resources from a service. In recent years, attackers have begun to focus their attack efforts on the application layer, allowing them to produce attacks that can exploit known issues within specific application protocols. Attackers are consistently shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation strategies. The integrity of modern network communications is constantly being challenged by more sophisticated intrusion techniques.
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