More Like this

1. Reservoir Computing Meets Wi-Fi in Software Radios: Neural Network-based Symbol Detection using Training Sequences and Pilots

   https://doi.org/10.1109/WOCC48579.2020.9114937  

   Li, Lianjun; Liu, Lingjia; Zhang, Jianzhong Charlie; Ashdown, Jonathan D.; Yi, Yang (May 2020, 2020 29th Wireless and Optical Communications Conference (WOCC))

   In this paper, we introduce a neural network (NN)-based symbol detection scheme for Wi-Fi systems and its associated hardware implementation in software radios. To be specific, reservoir computing (RC), a special type of recurrent neural network (RNN), is adopted to conduct the task of symbol detection for Wi-Fi receivers. Instead of introducing extra training overhead/set to facilitate the RC-based symbol detection, a new training framework is introduced to take advantage of the signal structure in existing Wi-Fi protocols (e.g., IEEE 802.11 standards), that is, the introduced RC-based symbol detector will utilize the inherent long/short training sequences and structured pilots sent by the Wi-Fi transmitter to conduct online learning of the transmit symbols. In other words, our introduced NN-based symbol detector does not require any additional training sets compared to existing Wi-Fi systems. The introduced RC-based Wi-Fi symbol detector is implemented on the software-defined radio (SDR) platform to further provide realistic and meaningful performance comparisons against the traditional Wi-Fi receiver. Over the air, experiment results show that the introduced RC based Wi-Fi symbol detector outperforms conventional Wi-Fi symbol detection methods in various environments indicating the significance and the relevance of our work. 

   more » « less
2. Circumvent traffic shaping using virtual wireless clients in IEEE 802.11 wireless local area network

   https://doi.org/10.1109/MILCOM.2017.8170854  

   Nakhila, Omar; Zou, Cliff (October 2017, MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM))

   Accessing the Internet through Wi-Fi networks offers an inexpensive alternative for offloading data from mobile broadband connections. Businesses such as fast food restaurants, coffee shops, hotels, and airports, provide complimentary Internet access to their customers through Wi-Fi networks. Clients can connect to the Wi-Fi hotspot using different wireless devices. However, network administrators may apply traffic shaping to control the wireless client's upload and download data rates. Such limitation is used to avoid overloading the hotspot, thus providing fair bandwidth allocation. Also, it allows for the collection of money from the client in order to have access to a faster Internet service. In this paper, we present a new technique to avoid bandwidth limitation imposed by Wi-Fi hotspots. The proposed method creates multiple virtual wireless clients using only one physical wireless interface card. Each virtual wireless client emulates a standalone wireless device. The combination of the individual bandwidth of each virtual wireless client results in an increase of the total bandwidth gained by the attacker. Our proposed technique was implemented and evaluated in a real-life environment with an increase in data rate up to 16 folds. 

   more » « less
3. Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learning

   https://doi.org/10.1109/VTC2024-Fall63153.2024.10757867  

   Fasihi, Mohammad Reza; Mark, Brian L (October 2024, IEEE)

   Coexistence of 5G new radio unlicensed (NR-U) and Wi-Fi is highly prone to the collisions among NR-U gNBs (5G base stations) and Wi-Fi APs (access points). To improve performance and fairness for both networks, various collision resolution mechanisms have been proposed to replace the simple listen-before-talk (LBT) scheme used in the current 5G standard. We address two gaps in the literature: first, the lack of a comprehensive performance comparison among the proposed collision resolution mechanisms and second, the impact of multiple traffic priority classes. Through extensive simulations, we compare the performance of several recently proposed collision resolution mechanisms for NR-U/Wi-Fi coexistence. We extend one of these mechanisms to handle multiple traffic priorities. We then develop a traffic-aware multi-objective deep reinforcement learning algorithm for the scenario of coexistence of high-priority traffic gNB user equipment (UE) with multiple lower-priority traffic UEs and Wi-Fi stations. The objective is to ensure low latency for high-priority gNB traffic while increasing the airtime fairness among the NR-U and Wi-Fi networks. Our simulation results show that the proposed algorithm lowers the channel access delay of high-priority traffic while improving the fairness among both networks. 

   more » « less
4. A Billion Open Interfaces for Eve and Mallory: MitM, DoS, and Tracking Attacks on iOS and macOS Through Apple Wireless Direct Link

   Mariotto, Alex; Heinrich, Alexander; Kreitschmann, David; Noubir, Guevara; Hollick, Matthias (January 2019, Proceedings of the 28th USENIX Security Symposium)

   Apple Wireless Direct Link (AWDL) is a key protocol in Apple’s ecosystem used by over one billion iOS and macOS devices for device-to-device communications. AWDL is a proprietary extension of the IEEE 802.11 (Wi-Fi) standard and integrates with Bluetooth Low Energy (BLE) for providing services such as Apple AirDrop. We conduct the first security and privacy analysis of AWDL and its integration with BLE. We uncover several security and privacy vulnerabilities ranging from design flaws to implementation bugs leading to a man-in-the-middle (MitM) attack enabling stealthy modification of files transmitted via AirDrop, denial-of-service (DoS) attacks preventing communication, privacy leaks that enable user identification and long-term tracking undermining MAC address randomization, and DoS attacks enabling targeted or simultaneous crashing of all neighboring devices. The flaws span across AirDrop’s BLE discovery mechanism, AWDL synchronization, UI design, and Wi-Fi driver implementation. Our analysis is based on a combination of reverse engineering of protocols and code supported by analyzing patents. We provide proof-of-concept implementations and demonstrate that the attacks can be mounted using a low-cost ($20) micro:bit device and an off-the-shelf Wi-Fi card. We propose practical and effective countermeasures. While Apple was able to issue a fix for a DoS attack vulnerability after our responsible disclosure, the other security and privacy vulnerabilities require the redesign of some of their services. 

   more » « less
5. Maestro: QoE-Aware Dynamic Resource Allocation in Wi-Fi Networks

   https://doi.org/10.1145/3709371  

   Kulkarni, Umakant; Diab, Khaled; Cao, Lianjie; Ahmed, Faraz; Aggarwal, Shivang; Sharma, Puneet; Fahmy, Sonia (March 2025, Proceedings of the ACM on Networking)

   Wi-Fi is an integral part of today's Internet infrastructure, enabling a diverse range of applications and services. Prior approaches to Wi-Fi resource allocation optimized Quality of Service (QoS) metrics, which often do not accurately reflect the user's Quality of Experience (QoE). To address the gap between QoS and QoE, we introduce Maestro, an adaptive method that formulates the Wi-Fi resource allocation problem as a partially observable Markov decision process (PO-MDP) to maximize the overall system QoE and QoE fairness. Maestro estimates QoE without using any application or client data; instead, it treats them as black boxes and leverages temporal dependencies in network telemetry data. Maestro dynamically adjusts policies to handle different classes of applications and variable network conditions. Additionally, Maestro uses a simulation environment for practical training. We evaluate Maestro in an enterprise-level Wi-Fi testbed with a variety of applications, and find that Maestro achieves up to 25× and 78% improvement in QoE and fairness, respectively, compared to the widely-deployed Wi-Fi Multimedia (WMM) policy. Compared to the state-of-the-art learning approach QFlow, Maestro increases QoE by up to 69%. Unlike QFlow which requires modifications to clients, we demonstrate that Maestro improves QoE of popular over-the-top services with unseen traffic without control over clients or servers. 

   more » « less