Defending Cloud Computing Environment Against the Challenge of DDoS Attacks Based on Software Defined Network

Tsai, Shuen-Chih; Liu, I-Hsien; Lu, Chien-Tung; Chang, Chan-Hua; Li, Jung-Shian

doi:10.1007/978-3-319-50209-0_35

Cited by 18 publications

(8 citation statements)

References 7 publications

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“…When a web server suspects malicious activity, it requests the application for processing, which blocks the sources of malicious activities. In [43], the authors proposed a system that can detect attacks early. It used two levels for detection in the SDN controller.…”

Section: Sdn Basedmentioning

confidence: 99%

Distributed denial of service attack detection in cloud computing using hybrid extreme learning machine

Kushwah¹,

Ranga²

2021

Turk J Elec Eng & Comp Sci

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One of the major security challenges in cloud computing is Distributed denial of service (DDoS) attacks. In these attacks, multiple nodes are used to attack the cloud by sending a large number of requests. This results in the unavailability of cloud services to legitimate users. In this research paper, a hybrid machine learning-based technique has been proposed which is a combination of extreme learning machine (ELM) model and blackhole optimization algorithm. The proposed technique is used to implement a DDoS attack detection system for cloud computing. Various experiments have been performed with the help of four benchmark datasets namely, NSL KDD, ISCX IDS 2012, CICIDS2017, and CICDDoS2019, to evaluate the performance of our proposed technique. It achieves an accuracy of 99.23%, 92.19%, 99.50%, 99.80% with NSL KDD, ISCX IDS 2012, CICIDS2017, and CICDDoS2019, respectively. The performance comparison with other techniques based on ELM, ANN trained with blackhole optimization, backpropagation ANN, and other state-of-the-art techniques is also performed.

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Section: Sdn Basedmentioning

confidence: 99%

Distributed denial of service attack detection in cloud computing using hybrid extreme learning machine

Kushwah¹,

Ranga²

2021

Turk J Elec Eng & Comp Sci

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“…S. Chih et al used an entropy‐based DDoS detection method for protecting the virtual machines and controller. A fixed entropy value was used as a threshold by which an attack is determined.…”

Section: Related Workmentioning

confidence: 99%

Fog‐SDN: A light mitigation scheme for DDoS attack in fog computing framework

Priyadarshini

Barik

Dubey

2020

Int J Communication

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Cloud computing is one of the most tempting technologies in today's computing scenario as it provides a cost-efficient solutions by reducing the large upfront cost for buying hardware infrastructures and computing power. Fog computing is an added support to cloud environment by leveraging with doing some of the less compute intensive task to be done at the edge devices, which reduces the response time for end user computing. But the vulnerabilities to these systems are still a big concern. Among several security needs, availability is one that makes the demanded services available to the targeted customers all the time. Availability is often challenged by external attacks like Denial of service (DoS) and distributed denial of service (DDoS). This paper demonstrates a novel source-based DDoS mitigating schemes that could be employed in both fog and cloud computing scenarios to eliminate these attacks. It deploys the DDoS defender module which works on a machine learning-based light detection method, present at the SDN controller. This scheme uses the network traffic data to analyze, predict, and filter incoming data, so that it can send the filtered legitimate packets to the server and blocking the rest. KEYWORDS fog computing, K nearest neighborhood, machine learning, Security, software-defined network, support vector machine Int J Commun Syst. 2020;33:e4389.wileyonlinelibrary.com/journal/dac

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“…[86] ISP-level monitoring of traffic and routing [87] Strict authentication and access control [88] Re-configurable network monitoring and control [95] SDN based deep packet inspection…”

Section: Sdn (M4)mentioning

confidence: 99%

“…The major idea of this work lies in the strict access control policies for the incoming traffic which requires strict authentication for each incoming request. Advanced deep packet inspection based approaches using SDN are discussed in [95]. A detailed tutorial and guideline of SDN-based solutions are given in [14].…”

Section: Software Defined Networking (M4)mentioning

confidence: 99%

DDoS attacks in cloud computing: Issues, taxonomy, and future directions

Somani

Gaur

Sanghi

et al. 2017

Computer Communications

238

102

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Security issues related to the cloud computing are relevant to various stakeholders for an informed cloud adoption decision. Apart from data breaches, the cyber security research community is revisiting the attack space for cloud-specific solutions as these issues affect budget, resource management, and service quality. Distributed Denial of Service (DDoS) attack is one such serious attack in the cloud space. In this paper, we present developments related to DDoS attack mitigation solutions in the cloud. In particular, we present a comprehensive survey with a detailed insight into the characterization, prevention, detection, and mitigation mechanisms of these attacks. Additionally, we present a comprehensive solution taxonomy to classify DDoS attack solutions. We also provide a comprehensive discussion on important metrics to evaluate various solutions. This survey concludes that there is a strong requirement of solutions, which are designed keeping utility computing models in mind. Accurate auto-scaling decisions, multi-layer mitigation, and defense using profound resources in the cloud, are some of the key requirements of the desired solutions. In the end, we provide a definite guideline on effective solution building and detailed solution requirements to help the cyber security research community in designing defense mechanisms. To the best of our knowledge, this work is a novel attempt to identify the need of DDoS mitigation solutions involving multi-level information flow and effective resource management during the attack.

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Defending Cloud Computing Environment Against the Challenge of DDoS Attacks Based on Software Defined Network

Cited by 18 publications

References 7 publications

Distributed denial of service attack detection in cloud computing using hybrid extreme learning machine

Distributed denial of service attack detection in cloud computing using hybrid extreme learning machine

Fog‐SDN: A light mitigation scheme for DDoS attack in fog computing framework

DDoS attacks in cloud computing: Issues, taxonomy, and future directions

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