Survey of RTS-CTS Attacks in Wireless Network

Sawwashere, Supriya; Nimbhorkar, S. U.

doi:10.1109/csnt.2014.158

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…Clear-to-Send flood-it relies on the Request-to-Send (RTS)/Clear-to-Send (CTS) mechanism and involves sending a large number of CTS frames with the duration set to its maximum value. This causes STA to wait for a transmission that never occurs, preventing other clients from accessing the medium [ 38 ].…”

Section: Possible Ieee 80211 Mac Layer Threatsmentioning

confidence: 99%

Wireless Local Area Networks Threat Detection Using 1D-CNN

Natkaniec

Bednarz

2023

Sensors

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Wireless Local Area Networks (WLANs) have revolutionized modern communication by providing a user-friendly and cost-efficient solution for Internet access and network resources. However, the increasing popularity of WLANs has also led to a rise in security threats, including jamming, flooding attacks, unfair radio channel access, user disconnection from access points, and injection attacks, among others. In this paper, we propose a machine learning algorithm to detect Layer 2 threats in WLANs through network traffic analysis. Our approach uses a deep neural network to identify malicious activity patterns. We detail the dataset used, including data preparation steps, such as preprocessing and division. We demonstrate the effectiveness of our solution through series of experiments and show that it outperforms other methods in terms of precision. The proposed algorithm can be successfully applied in Wireless Intrusion Detection Systems (WIDS) to enhance the security of WLANs and protect against potential attacks.

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Section: Possible Ieee 80211 Mac Layer Threatsmentioning

confidence: 99%

Wireless Local Area Networks Threat Detection Using 1D-CNN

Natkaniec

Bednarz

2023

Sensors

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“…The last group is Flooding attacks, which comprise at least 13 different attacks such as Beacon Flooding [34], Request to Send (RTS) Flooding [35], and Fake Power Saving [36]. The Beacon Flooding attack will transmit fake beacons with either non-existing or particular spoofed identifiers.…”

Section: Data Descriptionmentioning

confidence: 99%

Multi-Class Intrusion Detection Using Two-Channel Color Mapping in IEEE 802.11 Wireless Network

et al. 2022

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The rise of interconnected devices through wireless networks provides two sides consequences. On one side, it helps many human tasks; on the other hand, the prone wireless medium opens the vulnerable system to be exploited by adversaries. An Intrusion Detection System (IDS) is one method to inspect the network traffic by leveraging state-of-the-art anomaly detection techniques. Deep learning models have been utilized to distinguish the benign and malicious traffic. However, projecting the tabular data into images before the image classification has been the main challenge of leveraging deep learning for IDS purposes. We propose the novel projection of tabular data into 2-coded color mapping for IDS purposes. The proposed method employs a feature selection method to ensure optimal dimensionality. We examined the different number of attribute subsets to obtain the relationship between the attributes. Furthermore, it takes advantage of the Convolutional Neural Network (CNN) model to classify the Wi-Fi attacks. We evaluate the proposed model using the most common Wi-Fi attacks dataset, Aegean Wi-Fi Intrusion Dataset (AWID2). The proposed method achieved an F1 score of 99.73% and a false positive rate of 0.24%. This study highlights the importance of addressing the mapping procedures from tabular data into grid-based data before deep learning training and validates the effectiveness of CNN to detect multiple types of wireless network attacks.

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“…Here we propose to apply RTS/CTS process to overcome this challenge. RTS/CTS is a handshaking mechanism to reserve the channel for a specific duration before the actual data transfer starts [39]. In Fig.…”

Section: B Inactive Phone Localizationmentioning

confidence: 99%

Passive Indoor Localization with WiFi Fingerprints

Hoang¹,

Yuen²,

Ren³

et al. 2021

Preprint

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This paper proposes passive WiFi indoor localization. Instead of using WiFi signals received by mobile devices as fingerprints, we use signals received by routers to locate the mobile carrier. Consequently, software installation on the mobile device is not required. To resolve the data insufficiency problem, flow control signals such as request to send (RTS) and clear to send (CTS) are utilized. In our model, received signal strength indicator (RSSI) and channel state information (CSI) are used as fingerprints for several algorithms, including deterministic, probabilistic and neural networks localization algorithms. We further investigated localization algorithms performance through extensive on-site experiments with various models of phones at hundreds of testing locations. We demonstrate that our passive scheme achieves an average localization error of ∼0.8 m when the phone is actively transmitting data frames and ∼1.5 m when it is not transmitting data frames.

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Survey of RTS-CTS Attacks in Wireless Network

Cited by 9 publications

References 8 publications

Wireless Local Area Networks Threat Detection Using 1D-CNN

Wireless Local Area Networks Threat Detection Using 1D-CNN

Multi-Class Intrusion Detection Using Two-Channel Color Mapping in IEEE 802.11 Wireless Network

Passive Indoor Localization with WiFi Fingerprints

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