Convolutional Neural Network-Based Automatic Diagnostic System for AL-DDoS Attacks Detection

Convolutional Neural Network-Based Automatic Diagnostic System for AL-DDoS Attacks Detection

Fargana J. Abdullayeva
Copyright: © 2022 |Pages: 15
DOI: 10.4018/IJCWT.305242
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Abstract

Distributed denial of service (DDoS) attacks are one of the main threats to information security. The purpose of DDoS attacks at the network (IP) and transport (TCP) layers is to consume the network bandwidth and deny service to legitimate users of the target system. Application layer DDoS attacks (AL-DDoS) can be organized against many different applications. Many of these attacks target HTTP, in which case their goal is to deplete the resources of web services. Various schemes have been proposed to detect DDoS attacks on network and transport layers. There are very few works being done to detect AL-DDoS attacks. The development of an intelligent system automatically detecting AL-DDoS attacks in advance is very necessary. In this paper to detect AL-DDoS attacks a deep learning model based on the Convolutional Neural Network is proposed. To simulate the AL-DDoS attack detection process, while in testing of the model on CSE-CIC-IDS2018 DDoS and CSIC 2010 datasets, 0.9974 and 0.9059 accuracy values were obtained, respectively.
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Application Layer Ddos Attacks

The first large-scale DDoS attacks were carried out on big companies such as Yahoo!, eBay, and CNN in early February 2000, and since then the development of mechanisms to combat DDoS attacks was the focus of practitioners and researchers. During this period, a lot of investigations were carried out on this topic. Several important review papers were published analysing the taxonomy of DDoS attacks, attack mechanisms, the existing architectures and methods for protecting against DDoS attacks, their advantages, and disadvantages.

There are several types of DDoS attacks (Singh & De 2017):

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