Mitigation of Application Layer DDoS Flood Attack Against Web Servers

Aljuhani, Ahamed; Alharbi, Talal; Taylor, Bradley

doi:10.26735/16587790.2019.002

Cited by 6 publications

(4 citation statements)

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“…However, the proposed work should be validated with a real traffic dataset. Aljuhani et al [88] designed and developed an App-DDoS attack detection and mitigation model to protect webservers against App-DDoS attacks. They first developed a holistic DDoS mitigation model to detect and mitigate a diverse range of DDoS attacks [89].…”

Section: Ddos Defense Systems Based On ML Techniques In Nfv Enviromentioning

confidence: 99%

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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Section: Ddos Defense Systems Based On ML Techniques In Nfv Enviromentioning

confidence: 99%

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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“…Artificial intelligence may prove to be a helpful ally in the construction of defense against attackers. AI is capable of detecting and analyzing patterns for any anomaly [6,7]. This entails protecting IoT systems from hackers and using artificial intelligence to detect anomalous behaviour that might point to an assault.…”

Section: Introductionmentioning

confidence: 99%

Prevention of Cyber Security with the Internet of Things Using Particle Swarm Optimization

Alterazi

Kshirsagar

Manoharan³

et al. 2022

Sensors

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High security for physical items such as intelligent machinery and residential appliances is provided via the Internet of Things (IoT). The physical objects are given a distinct online address known as the Internet Protocol to communicate with the network’s external foreign entities through the Internet (IP). IoT devices are in danger of security issues due to the surge in hacker attacks during Internet data exchange. If such strong attacks are to create a reliable security system, attack detection is essential. Attacks and abnormalities such as user-to-root (U2R), denial-of-service, and data-type probing could have an impact on an IoT system. This article examines various performance-based AI models to predict attacks and problems with IoT devices with accuracy. Particle Swarm Optimization (PSO), genetic algorithms, and ant colony optimization were used to demonstrate the effectiveness of the suggested technique concerning four different parameters. The results of the proposed method employing PSO outperformed those of the existing systems by roughly 73 percent.

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“…Master DDoS is a special malware that the attacker installs to detect device with vulnerabilities in the network. The effect of the attack largely depends on the period by which service is suspended and the size of the attack [5]. A large quantity of bots gives computer the power to develop prime tools to carryout malicious activities like the spread of SPAM email, virus, click fraud and so on [3].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning has made a great progress in recent years in detecting DDoS attack [9]. [5] This research proposes an intrusion detection and prevention on IoT devices using machine learning techniques. A DDoS attack dataset is obtained from Canadian institute of cyber security for the training purposes.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Intrusion Detection of DDoS Attack on IoT Devices

2021

IJATCSE

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DDoS attack aims to prevent legitimate users from getting access to a targeted system service by exhausting the resources, bandwidth and so on. Though, there are different intrusion mechanisms for detection DDoS attack, having an automated system that can learn the nature of the attack and instantly detect it is the reason why machine learning is used in this work. Decision tree, KNN and Naïve Bayes are the algorithms used classify a benign traffic from a DDoS attack. About nineteen different feature was carefully selected from CIC2019DDoS dataset. The DDoS attack types used for the experiment are UDP, DNS, SYN and NetBIOS. The results of the experiment indicate that Decision tree and KNN proved to be the most effective with an accuracy of 100% and 98% respectively. Naïve Bayes gave a very poor result with an accuracy of 29%.

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Mitigation of Application Layer DDoS Flood Attack Against Web Servers

Cited by 6 publications

References 0 publications

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Prevention of Cyber Security with the Internet of Things Using Particle Swarm Optimization

A Machine Learning-Based Intrusion Detection of DDoS Attack on IoT Devices

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