Efficient SYN Spoofing Detection and Mitigation Scheme for DDoS Attack

Kavisankar, L.; Chellappan, C.; Venkatesan, S.; Sivasankar, P.

doi:10.1109/icrtccm.2017.55

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…This variation aimed to determine the suitability of Levy walk as prototypical similarity with DDoS potential features. In [16], the authors experimented with the clever subject of comedy measurement that utilizes a conference seeking philosophy and a brilliant channel that sets shares in the traditional way. Multilayer perceptron with genetic algorithm (MLP-GA) is proposed in [17] to detect DDoS attacks.…”

Section: Literature Reviewmentioning

confidence: 99%

Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron

et al. 2023

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Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Naïve Bayes, Decision Stump, Logistic Model Tree, Naïve Bayes Updateable, Naïve Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.

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Section: Literature Reviewmentioning

confidence: 99%

Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron

et al. 2023

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“…The flooding or Denial of service (DoS) attack harms this significant feature by prohibiting the legitimate customers from accessing the network resources. To serve the purpose of consuming servers processing power and the network capacity (bandwidth), the attackers commence producing DoS attacks by means of transmitting countless requests so the legitimate users become incapable of accessing the network even they are eligible for legitimate access to the network resources [5].…”

Section: A) Denial Of Service (Dos)mentioning

confidence: 99%

Economic Denial of Sustainability Attacks Mitigation in the Cloud

Alosaimi

Žák

Al‐Begain

et al. 2022

Int. j. commun. netw. inf. secur.

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Cyber security is one of the most attention seeking issues with the increasing advancement of technology specifically when the network availability is threaten by attacks such as Denial of Service attacks (DoS), Distributed DoS attacks (DDoS), and Economic Denial of Sustainability (EDoS). The loss of the availability and accessibility of cloud services have greater impacts than those in the traditional enterprises networks. This paper introduces a new technique to mitigate the impacts of attacks which is called Enhanced DDoS-Mitigation System (Enhanced DDoS-MS) that helps in overcoming the determined security gap. The proposed technique is evaluated experimentally and the result shows that the proposed method adds lower delays as a result of the enhanced security. The paper also suggests some future directions to improve the proposed framework.

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“…Kavisankar et al[20] proposed a SYN spoofing Detection and Mitigation Scheme. A Bloom Filter is used to store the legitimate IP addresses.…”

mentioning

confidence: 99%

Preventing DDoS using Bloom Filter: A Survey

Patgiri¹,

Nayak²,

Borgohain³

2018

ICST Transactions on Scalable Information Systems

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Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, causes loss of business revenue. Therefore, DDoS is a grand challenge to defeat. There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending the DDoS attack. The Bloom Filter is a probabilistic data structure for membership query that returns either true or false. Bloom Filter uses tiny memory to store information of large data. Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS. This paper presents a survey on DDoS defending technique using Bloom Filter.

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Efficient SYN Spoofing Detection and Mitigation Scheme for DDoS Attack

Cited by 5 publications

References 12 publications

Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron

Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron

Economic Denial of Sustainability Attacks Mitigation in the Cloud

Preventing DDoS using Bloom Filter: A Survey

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