Prevention Techniques against Distributed Denial of Service Attacks in Heterogeneous Networks: A Systematic Review

Cheema, Ammarah; Tariq, Moeenuddin; Hafiz, Adnan; Khan, Muhammad Murad; Ahmad, Fahad; Anwar, Muhammad

doi:10.1155/2022/8379532

Cited by 37 publications

(14 citation statements)

References 38 publications

(56 reference statements)

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“…DoS attacks are simple to carry out, especially in the wireless domain, due to the broadcast environment of wireless networks [2,3]. DoS attack aims to make network resources inaccessible to other authorised users [4,5]. While data is changed or stolen in different types of assaults, a DoS attack seeks to impede Sensors 2023, 23, 2663 2 of 13 or exhaust system resources for other users.…”

Section: Introductionmentioning

confidence: 99%

Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural Network

Abdallah

Hamdan

Gismalla

et al. 2023

Sensors

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Wireless Local Area Networks (WLANs) have become an increasingly popular mode of communication and networking, with a wide range of applications in various fields. However, the increasing popularity of WLANs has also led to an increase in security threats, including denial of service (DoS) attacks. In this study, management-frames-based DoS attacks, in which the attacker floods the network with management frames, are particularly concerning as they can cause widespread disruptions in the network. Attacks known as denial of service (DoS) can target wireless LANs. None of the wireless security mechanisms in use today contemplate defence against them. At the MAC layer, there are multiple vulnerabilities that can be exploited to launch DoS attacks. This paper focuses on designing and developing an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks. The proposed scheme aims to effectively detect fake de-authentication/disassociation frames and improve network performance by avoiding communication interruption caused by such attacks. The proposed NN scheme leverages machine learning techniques to analyse patterns and features in the management frames exchanged between wireless devices. By training the NN, the system can learn to accurately detect potential DoS attacks. This approach offers a more sophisticated and effective solution to the problem of DoS attacks in wireless LANs and has the potential to significantly enhance the security and reliability of these networks. According to the experimental results, the proposed technique exhibits higher effectiveness in detection compared to existing methods, as evidenced by a significantly increased true positive rate and a decreased false positive rate.

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Section: Introductionmentioning

confidence: 99%

Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural Network

Abdallah

Hamdan

Gismalla

et al. 2023

Sensors

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“…Among the various types of network attacks, denial of service (DoS) attacks are prevalent, encompassing numerous attack methods [4]. One such attack method is the SYN Flood, which falls under the broader category of DoS attacks.…”

Section: Introductionmentioning

confidence: 99%

Study limitations of DoS attackers due to computer resources

Liu

2023

Third International Conference on Signal Image Processing and Communication (ICSIPC 2023)

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The advancement of computer technology has facilitated the provision of a wide range of services to customers through computer networks. However, the security of these networks is constantly threatened by malicious actors who employ various means to launch attacks. Denial-of-Service (DoS) attacks are a common type of network attack that poses a significant risk to many computer information systems. In this research paper, we aim to investigate how the efficiency of DoS attackers is affected when they have limited computer resources. The findings of our experiments confirm that DoS attackers face challenges in executing efficient attacks when their computers lack sufficient CPU resources. By understanding this limitation, it becomes possible for the targeted entities to choose appropriate defense strategies when faced with such attacks.

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“…A DDoS attack includes flooding a server with requests to prevent it from providing its service and denying access to authorized users. Hackers assault the server with a flood of messages and connection requests that slows down or even crash, blocking normal access [2]. It may be possible to detect DDoS attacks on the network, but there is a type of DDoS that may be difficult for traditional detection systems to detect: Low-Rate DDoS.…”

Section: Introductionmentioning

confidence: 99%

Resilient intrusion detection system for adversarial attacks on Low-Rate DDoS

Droos,

Al-Haija

2023

Preprint

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With the increase of attacks and illegal activities on networks, there has become an urgent need for systems that detect these activities and attacks and protect networks, such as intrusion detection systems that have an important role in protecting computer networks. Still, an adversarial attack has recently appeared against these systems, adversely affecting their performance. This paper proposes a framework that uses a Generative Adversarial Network (GAN) to generate adversarial instances to deceive IDS. The framework consists of two phases: building IDS models using two datasets and generating adversarial examples using GAN. Through careful evaluation, the GAN model achieves an impressive 99.9% success rate in generating samples that successfully evade intrusion detection systems, which sheds light on the potential vulnerabilities of IDSs for adversarial attacks.

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Prevention Techniques against Distributed Denial of Service Attacks in Heterogeneous Networks: A Systematic Review

Cited by 37 publications

References 38 publications

Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural Network

Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural Network

Study limitations of DoS attackers due to computer resources

Resilient intrusion detection system for adversarial attacks on Low-Rate DDoS

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