HTTP Application Layer DDoS Attack Mitigation Using Resources Monitor View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

Mohamed Aly Mohamed , Nashwa Abdelbaki

ABSTRACT

Denial of Service attacks are one of the most annoying day to day challenges for any security expert and IT professional. This is according to the attack nature. It can be run against any kind of network resources, whether exposed to the Internet or internally in a corporate network, regardless of the type of service it provides and its role in the network. There is no one complete solution or unified framework method against this type of attack. The most dangerous type of DoS attack is the DDoS type. The attack flow from many sources at the same time. In the context of mitigation from DDoS attack, the detection mechanisms are the first step in the way. Mechanisms like IPS and firewall are not effective because of the current challenging DDOS attack methods against application layer. The attackers use vulnerability in the application itself to disrupt the service it provides. Current mitigation techniques depend on preventing the attack traffic from reaching web servers. In this paper we study http application layer DDoS attacks against web servers. We propose new approach for mitigation that depends on absorbing the attack effects on the web server and increases server’s resistance against DDoS attacks. More... »

PAGES

213-221

References to SciGraph publications

  • 2015. An Approach of DDOS Attack Detection Using Classifiers in EMERGING RESEARCH IN COMPUTING, INFORMATION, COMMUNICATION AND APPLICATIONS
  • 2012. AIGG Threshold Based HTTP GET Flooding Attack Detection in INFORMATION SECURITY APPLICATIONS
  • 2014-06. ConnectionScore: a statistical technique to resist application-layer DDoS attacks in JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
  • 2016-03. Distributed denial-of-service attacks against HTTP/2 services in CLUSTER COMPUTING
  • 2014. Protective Frameworks and Schemes to Detect and Prevent High Rate DoS/DDoS and Flash Crowd Attacks: A Comprehensive Review in ADVANCED MACHINE LEARNING TECHNOLOGIES AND APPLICATIONS
  • 2016-09. Learning to control a structured-prediction decoder for detection of HTTP-layer DDoS attackers in MACHINE LEARNING
  • 2017-03. DyProSD: a dynamic protocol specific defense for high-rate DDoS flooding attacks in MICROSYSTEM TECHNOLOGIES
  • Book

    TITLE

    Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017

    ISBN

    978-3-319-64860-6
    978-3-319-64861-3

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-64861-3_20

    DOI

    http://dx.doi.org/10.1007/978-3-319-64861-3_20

    DIMENSIONS

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