An Analysis of the Mixed IEEE 802.11ax Wireless Networks in the 5 GHz Band.

This paper presents an analysis of the IEEE 802.11ax networks' coexistence with legacy stations, namely IEEE 802.11ac, IEEE 802.11n, and IEEE 802.11a. The IEEE 802.11ax standard introduces several new features that can enhance network performance and capacity. The legacy devices that do not support these features will continue to coexist with newer devices, creating a mixed network environment. This usually leads to a deterioration in the overall performance of such networks; therefore, in the paper, we want to show how we can reduce the negative impact of legacy devices. In this study, we investigate the performance of mixed networks by applying various parameters to both the MAC and PHY layers. We focus on evaluating the impact of the BSS coloring mechanism introduced to the IEEE 802.11ax standard on network performance. We also examine the impact of A-MPDU and A-MSDU aggregations on network efficiency. Through simulations, we analyze the typical performance metrics such as throughput, mean packet delay, and packet loss of mixed networks with different topologies and configurations. Our findings indicate that implementing the BSS coloring mechanism in dense networks can increase throughput by up to 43%. We also show that the presence of legacy devices in the network disrupts the functioning of this mechanism. To address this, we recommend using an aggregation technique, which can improve throughput by up to 79%. The presented research revealed that it is possible to optimize the performance of mixed IEEE 802.11ax networks.

Wireless Local Area Networks Threat Detection Using 1D-CNN.

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.