The Slow HTTP Distributed Denial of Service Attack Detection in Cloud

Author:

Dhanapal A.,Nithyanandam P.

Abstract

Cloud computing became popular due to nature as it provides the flexibility to add or remove the resources on-demand basis. This also reduces the cost of investments for the enterprises significantly. The adoption of cloud computing is very high for enterprises running their online applications. The availability of online services is critical for businesses like financial services, e-commerce applications, etc. Though cloud provides availability, still these applications are having potential threats of going down due to the slow HTTP Distributed Denial of Service (DDoS) attack in the cloud. The slow HTTP attacks intention is to consume all the available server resources and make it unavailable to the real users. The slow HTTP DDoS attack comes with different formats such as slow HTTP headers attacks, slow HTTP body attacks and slow HTTP read attacks. Detecting the slow HTTP DDoS attacks in the cloud is very crucial to safeguard online cloud applications. This is a very interesting and challenging topic in DDoS as it mimics the slow network. This paper proposed a novel method to detect slow HTTP DDoS attacks in the cloud. The solution is implemented using the OpenStack cloud platform. The experiments conducted exhibits the accurate results on detecting the attacks at the early stages. The slowHTTPTest open source tool is used in this experiment to originate slow HTTP DDoS attacks.

Publisher

Scalable Computing: Practice and Experience

Subject

General Computer Science

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset;Applied Sciences;2023-08-09

2. A Software Defined Network (SDN) architecture is used to protect against SLOW HTTP DOS attacks;2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC);2023-06-16

3. Enhancing the Detection of DDoS Attacks in Cloud using Linear Discriminant Algorithm;2023 8th International Conference on Communication and Electronics Systems (ICCES);2023-06-01

4. Detection of HTTP DDoS Attacks Using NFStream and TensorFlow;Applied Sciences;2023-05-30

5. FACVO-DNFN: Deep learning-based feature fusion and Distributed Denial of Service attack detection in cloud computing;Knowledge-Based Systems;2023-02

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