Detection of distributed denial of service attacks in cloud computing by identifying spoofed IP

Jeyanthi, N.; Barde, Uttara; Sravani, M. M.; Tiwari, Venu; Iyengar, N. Ch. S. N.

doi:10.1504/ijcnds.2013.056223

Cited by 15 publications

(4 citation statements)

References 15 publications

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“…To analyze the performance, input files of varied sizes are employed.The system's performance is evaluated using three metrics: attack detection time, Rate of Reporting. The above system is contrasted with the AntiDDoS framework [10] and the Scale in-out model [17]. The experiment was carried out by varying the number of DDoS attacks recorded on the deployed cloud server.…”

Section: B Results Analysismentioning

confidence: 99%

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Mitigation of DDoS Attack in Cloud Computing Domain by Integrating the DCLB Algorithm with Fuzzy Logic

Nair¹,

Santhosh²

2022

IJACSA

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Cloud computing would be an easy method to obtain services, resources and applications from any location on the internet. In the future of data generation, it is an unavoidable conclusion. Despite its many attractive properties, the cloud is vulnerable to a variety of attacks. One such well-known attack that emphasizes the availability of amenities is the Distributed Denial of Service (DDoS). A DDoS assault overwhelms the server with massive quantities of regular or intermittent traffic. It compromises with the cloud servers' services and makes it harder to reply to legitimate users of the cloud. A monitoring system with correct resource scaling approach should be created to regulate and monitor the DDoS assault. The network is overwhelmed with excessive traffic of significant resource usage requests during the attack, resulting in the denial of needed services to genuine users. In this research, a unique way to the analyze resources used by the cloud users, lowering of the resources consumed is done when the network is overburdened with excessive traffic, and the dynamic cloud load balancing algorithm DCLB (Dynamic Cloud Load Balancing) is used to balance the overhead towards the server. The core premise is to monitor traffic using the fuzzy logic approach, which employs different traffic parameters in conjunction with various built in measured to recognize the DDoS attack traffic in the network. Finally, the proposed method shows a 93% of average detection rate when compared to the existing model. This method is a unique attempt to comprehend the importance of DDoS mitigation techniques as well as good resource management during an attack and analysis of the.

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Section: B Results Analysismentioning

confidence: 99%

“…Barde et al, [17] A "deception detection" approach were proposed for detecting high-rate DDoS attack traffic in the cloud computing domain.…”

Section: Related Workmentioning

confidence: 99%

Mitigation of DDoS Attack in Cloud Computing Domain by Integrating the DCLB Algorithm with Fuzzy Logic

Nair¹,

Santhosh²

2022

IJACSA

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“…Application response time: Response time refers to the fact that the defense system can save the bandwidth and resources of the cloud system from congestion and unavailability as a result of attacks [115], [124]. Preetha et al [125] use this metric to determine the performance of their defense technique given three kinds of application: FTP, email, and HTTP.…”

Section: ) Attack Mitigationmentioning

confidence: 99%

“…Cloud resource consumption: Authors use this metric in defense and non-defense scenarios as a way of demonstrating how the defense system alleviates the impact that DDoS attacks have on cloud service performance. It is typically expressed through CPU consumption [126] or network bandwidth utilization [123], [124] and [127]. It can be evaluated while the type of attack is made to vary [128].…”

Section: ) Attack Mitigationmentioning

confidence: 99%

Comprehensive Review of Artificial Intelligence and Statistical Approaches in Distributed Denial of Service Attack and Defense Methods

et al. 2019

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Until now, an effective defense method against Distributed Denial of Service (DDoS) attacks is yet to be offered by security systems. Incidents of serious damage due to DDoS attacks have been increasing, thereby leading to an urgent need for new attack identification, mitigation, and prevention mechanisms. To prevent DDoS attacks, the basic features of the attacks need to be dynamically analyzed because their patterns, ports, and protocols or operation mechanisms are rapidly changed and manipulated. Most of the proposed DDoS defense methods have different types of drawbacks and limitations. Some of these methods have signature-based defense mechanisms that fail to identify new attacks and others have anomaly-based defense mechanisms that are limited to specific types of DDoS attacks and yet to be applied in open environments. Subsequently, extensive research on applying artificial intelligence and statistical techniques in the defense methods has been conducted in order to identify, mitigate, and prevent these attacks. However, the most appropriate and effective defense features, mechanisms, techniques, and methods for handling such attacks remain to be an open question. This review paper focuses on the most common defense methods against DDoS attacks that adopt artificial intelligence and statistical approaches. Additionally, the review classifies and illustrates the attack types, the testing properties, the evaluation methods and the testing datasets that are utilized in the methodology of the proposed defense methods. Finally, this review provides a guideline and possible points of encampments for developing improved solution models of defense methods against DDoS attacks. INDEX TERMS DDoS attack, DDoS defense, artificial intelligence technique, statistical technique.

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Unknown Attack Detection in Cloud Based on Correlation Analysis

Kiranmai

Vasantha

2022

Proceedings of Data Analytics and Management

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Detection of distributed denial of service attacks in cloud computing by identifying spoofed IP

Cited by 15 publications

References 15 publications

Mitigation of DDoS Attack in Cloud Computing Domain by Integrating the DCLB Algorithm with Fuzzy Logic

Mitigation of DDoS Attack in Cloud Computing Domain by Integrating the DCLB Algorithm with Fuzzy Logic

Comprehensive Review of Artificial Intelligence and Statistical Approaches in Distributed Denial of Service Attack and Defense Methods

Unknown Attack Detection in Cloud Based on Correlation Analysis

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