Streamlining HTTP Flooding Attack Detection through Incremental Feature Selection
Applications over the Web primarily rely on the HTTP protocol to transmit web pages to and from systems. There are a variety of application layer protocols, but among all, HTTP is the most targeted because of its versatility and ease of integration with online services. The attackers leverage the fact that by default no detection system blocks any HTTP traffic. Thus, by exploiting such characteristics of the protocol, attacks are launched against web applications. HTTP flooding attacks are one such attack in the application layer of the OSI model. In this paper, a method for the detection of such an attack is proposed. The heart of the detection method is an incremental feature subset selection method based on mutual information and correlation. INFS-MICC helps in identifying a subset of highly relevant and independent feature subset so as to detect HTTP Flooding attacks with best possible classification performance in near-real time.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionSimilar Papers 제목 키워드 기반
Detection and classification of DDoS flooding attacks by machine learning method
This study focuses on a method for detecting and classifying distributed denial of service (DDoS) attacks, such as SYN Flooding, ACK Flooding, HTTP Flooding, and UDP Flooding, using neural networks. Machine learning, par…
Detecting Target-Area Link-Flooding DDoS Attacks using Traffic Analysis and Supervised Learning
A novel class of extreme link-flooding DDoS (Distributed Denial of Service) attacks is designed to cut off entire geographical areas such as cities and even countries from the Internet by simultaneously targeting a selec…
BIG-bench Machine LearningAttention Meets UAVs: A Comprehensive Evaluation of DDoS Detection in Low-Cost UAVs
This paper explores the critical issue of enhancing cybersecurity measures for low-cost, Wi-Fi-based Unmanned Aerial Vehicles (UAVs) against Distributed Denial of Service (DDoS) attacks. In the current work, we have expl…
Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing
Network slicing manages network resources as virtual resource blocks (RBs) for the 5G Radio Access Network (RAN). Each communication request comes with quality of experience (QoE) requirements such as throughput and late…
BIG-bench Machine LearningReinforcement Learning (RL)Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network
The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN introduce many security threats and attacks. An effective Intrusion Detection System (IDS) should be used to detect attacks. Detecting such an …
BIG-bench Machine LearningIntrusion DetectionScheduling