Identification of probe request attacks in WLANs using neural networks

Ratnayake, Deepthi; Kazemian, Hassan B.; Yusuf, Syed Adnan

doi:10.1007/s00521-013-1478-8

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…In reference [18], they described a method for identifying probe request attacks by classifying real-world WLAN data frames from a station (STA) using a neural network (NN) classifier. Signal strength, sequence number, frame sub-type, and delta time were the four variables used to train the supervised feed-forward NN classifier to distinguish between genuine and malicious frames.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…Table I shows the operations performed during the Thread DTLS handshake, indicating that elliptic curve cryptography (ECC) and SHA-256 algorithms are executed many times during the handshake, causing significant computational load. Unfortunately, most hardware platforms feature only [35] √ − + Handshake Flooding [28] √ − + Network Sniffing [20] √ + + Man-In-Middle Attack [20] √ + + Offline Dictionary Attack [26] √ + + Online Dictionary Attack [26] √ + + Radio Jamming [32] √ − + Symbol Flipping [32] √ + + Signal Overshadowing [32] √ + + Link layer jamming [31] √ − + Node-specific flooding [31] √ − + Back-off Manipulation [31] √ − + CCA Manipulation [31] √ − + Same Nonce Attack [30] √ + + Replay Attack [31] √ + + Replay-protection Attack [31] √ + + ACK Spoofing [31] √ − + ACK Dropping [31] √ − + GTS-related Attacks [31] √ √ + + PANID Conflict Attack [31] √ + + Bootstrapping Attack [31] √ + + Steganography Attack [31] √ + + Routing Hop Manipulation [33] √ + + Routing Redirection [33] √ + + Black/grey Hole Attack [34] √ + + Worm Hole Attack [34] √ + + Link Spoofing Attack [34] √ + + Configuration Mechanism Abuse [20] √ + + Component Replacement [20] √ + + Microprobing [20] √ − − hardware acceleration for AES to support message encryption, but not for ECC and SHA-256. An effective countermeasure for handshake flooding attacks would be to speed up ECC and SHA-256 computation through hardware acceleration.…”

Section: A Handshake Flooding (Attack C/2)mentioning

confidence: 99%

A Taxonomy for the Security Assessment of IP-Based Building Automation Systems: The Case of Thread

Liu

Pang

Dán

et al. 2018

IEEE Trans. Ind. Inf.

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Motivated by the proliferation of wireless building automation systems (BAS) and increasing security-awareness among BAS operators, in this paper we propose a taxonomy for the security assessment of BASs. We apply the proposed taxonomy to Thread, an emerging native IP-based protocol for BAS. Our analysis reveals a number of potential weaknesses in the design of Thread. We propose potential solutions for mitigating several identified weaknesses and discuss their efficacy. We also provide suggestions for improvements in future versions of the standard. Overall, our analysis shows that Thread has a well-designed security control for the targeted use case, making it a promising candidate for communication in next generation BASs.

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“…The results show that the Radmin can accurately work on both the user and kernel spaces. Ratnayake et al [22] proposed in their study a scheme for probe request attack detection using a neural network (NN)classifier to classify a Station (STA's) real-world WLAN traffic frames. Four features, signal strength, sequence number, frame sub-type and delta time, were used to train the supervised feed forward NN classifier to differentiate a legitimate frame from a malicious one.…”

Section: Introductionmentioning

confidence: 99%

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

Abdallah¹,

Hamdan²,

Razak³

et al. 2023

Computers, Materials &Amp; Continua

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IEEE 802.11 Wi-Fi networks are prone to many denial of service (DoS) attacks due to vulnerabilities at the media access control (MAC) layer of the 802.11 protocol. Due to the data transmission nature of the wireless local area network (WLAN) through radio waves, its communication is exposed to the possibility of being attacked by illegitimate users. Moreover, the security design of the wireless structure is vulnerable to versatile attacks. For example, the attacker can imitate genuine features, rendering classificationbased methods inaccurate in differentiating between real and false messages. Although many security standards have been proposed over the last decades to overcome many wireless network attacks, effectively detecting such attacks is crucial in today's real-world applications. This paper presents a novel resource exhaustion attack detection scheme (READS) to detect resource exhaustion attacks effectively. The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack. The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in the WLAN. The proposed scheme consists of four modules which make it capable to alleviates the attack impact more effectively than the related work. The experimental results show the effectiveness of the proposed technique by gaining an 89.11% improvement compared to the existing works in terms of detection.

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Identification of probe request attacks in WLANs using neural networks

Cited by 8 publications

References 13 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

A Taxonomy for the Security Assessment of IP-Based Building Automation Systems: The Case of Thread

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

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