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1. Efficient Authentication of Drones to mmWave Wireless Mesh Networks in Post-Disaster Scenarios

   https://doi.org/10.1109/GLOBECOM42002.2020.9322282  

   Abdel-Malek, Mai A.; Akkaya, Kemal; Saputro, Nico; Ibrahim, Ahmed S. (December 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference)

   null (Ed.)

   Unmanned Aerial Vehicles (UAVs), or drones, are increasingly being utilized for public safety circumstances including post-disaster recovery of destroyed communication infrastructure. For instance, drones are temporarily positioned within an affected area to create a wireless mesh network among public safety personnel. To serve the need for high-rate video-based damage assessment, drone-assisted communication can utilize high- bandwidth millimeter wave (mmWave) technologies such as IEEE 802.11ad. However, short-range mmWave communication makes it hard for optimally- positioned drones to be authenticated with a centralized network control center. Therefore and assuming that there are potential imposters, we propose two lightweight and fast authentication mechanisms that take into account the physical limitations of mmWave communication. First, we propose a drone-to-drone authentication mechanism, which is based on proxy signatures from a control center. Accordingly, any newly joining drone can authenticate itself to an exist one rather than attempting to authenticate to the outof-reach control center. Second, we propose a drone-to- ground authentication mechanism, to enable each drone to authenticate itself to its associated ground users. Such authentication approach is based on challenge-response broadcast type, and it is still utilizing fast proxy signature approach. The evaluation of the proposed authentication mechanisms, conducted using NS-3 implementation of IEEE 802.11ad protocol, show their efficiency and practicality. 

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2. liteGAP: Lightweight Group Authentication Protocol for Internet of Drones Systems

   https://doi.org/10.1109/TVT.2023.3335839  

   Pu, Cong; Warner, Clare; Choo, Kim-Kwang Raymond; Lim, Sunho; Ahmed, Imtiaz (April 2024, IEEE Transactions on Vehicular Technology)

   Over the past few years, the synergic usage of unmanned aerial vehicles (later drones) and Internet of Things (IoT) has successfully transformed into the Internet of Drones (IoD) paradigm, where the data of interest is gathered and delivered to the Zone Service Provider (ZSP) by drones for substantial additional analysis. Considering the sensitivity of collected information and the impact of information disclosure, information privacy and security issues should be resolved properly so that the maximum potential of IoD can be realized in the increasingly complex cyber threat environment. Ideally, an authentication and key agreement protocol can be adopted to establish secure communications between drones and the ZSP in an insecure environment. Nevertheless, a large group of drones authenticating with the ZSP simultaneously will lead to a severe authentication signaling congestion, which inevitably degrades the quality of service (QoS) of IoD systems. To properly address the above-mentioned issues, a lightweight group authentication protocol, called liteGAP, is proposed in this paper. liteGAP can achieve the authenticated key establishment between a group of drones and the ZSP concurrently in the IoD environment using lightweight operations such as hash function, bitwise XOR, and physical unclonable function (PUF). We verify liteGAP using AVISPA (a tool for the automatic verification of security protocols) and conduct formal and informal security analysis, proving that liteGAP meets all pre-defined security requirements and withstand various potential cyber attacks. Moreover, we develop an experimental framework and conduct extensive experiments on liteGAP and two benchmark schemes (e.g., GASE and rampIoD). Experimental findings show that liteGAP outperforms its counterparts in terms of computational cost as well as communication overhead. 

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3. Hash-MAC-DSDV: Mutual Authentication for Intelligent IoT-Based Cyber-Physical Systems

   https://doi.org/10.1109/jiot.2021.3083731  

   Adil, Muhammad; Jan, Mian Ahmad; Mastorakis, Spyridon; Song, Houbing; Jadoon, Muhammad Mohsin; Abbas, Safia; Farouk, Ahmed (September 2021, IEEE Internet of Things Journal)

   null (Ed.)

   Cyber-Physical Systems (CPS) connected in the form of Internet of Things (IoT) are vulnerable to various security threats, due to the infrastructure-less deployment of IoT devices. Device-to-Device (D2D) authentication of these networks ensures the integrity, authenticity, and confidentiality of information in the deployed area. The literature suggests different approaches to address security issues in CPS technologies. However, they are mostly based on centralized techniques or specific system deployments with higher cost of computation and communication. It is therefore necessary to develop an effective scheme that can resolve the security problems in CPS technologies of IoT devices. In this paper, a lightweight Hash-MAC-DSDV (Hash Media Access Control Destination Sequence Distance Vector) routing scheme is proposed to resolve authentication issues in CPS technologies, connected in the form of IoT networks. For this purpose, a CPS of IoT devices (multi-WSNs) is developed from the local-chain and public chain, respectively. The proposed scheme ensures D2D authentication by the Hash-MAC-DSDV mutual scheme, where the MAC addresses of individual devices are registered in the first phase and advertised in the network in the second phase. The proposed scheme allows legitimate devices to modify their routing table and unicast the one-way hash authentication mechanism to transfer their captured data from source towards the destination. Our evaluation results demonstrate that Hash-MAC-DSDV outweighs the existing schemes in terms of attack detection, energy consumption and communication metrics. 

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4. A Quantum Good Authentication Protocol

   Wang, Shuangbao Paul (May 2025, CSIAC journal)

   This article presents a novel network protocol that incorporates a quantum photonic channel for symmetric key distribution, a Dilithium signature to replace factor-based public key cryptography for enhanced authentication, security, and privacy. The protocol uses strong hash functions to hash original messages and verify heightened data integrity at the destination. This Quantum good authentication protocol (QGP) provides high-level security provided by the theory of quantum mechanics. QGP also has the advantage of quantum-resistant data protection that prevents current digital computer and future quantum computer attacks. QGP transforms the transmission control protocol/internet protocol (TCP/IP) by adding a quantum layer at the bottom of the Open Systems Interconnection (OSI) model (layer 0) and modifying the top layer (layer 7) with Dilithium signatures, thus improving the security of the original OSI model. In addition, QGP incorporates strong encryption, hardware-based quantum channels, post-quantum signatures, and secure hash algorithms over a platform of decryptors, switches, routers, and network controllers to form a testbed of the next-generation, secure quantum internet. The experiments presented here show that QGP provides secure authentication and improved security and privacy and can be adopted as a new protocol for the next-generation quantum internet. 

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5. Trust in 5G Open RANs through Machine Learning: RF Fingerprinting on the POWDER PAWR Platform

   Reus-Muns, Guillem; Jaisinghani, Dheryta; Sankhe, Kunal Sankhe; Chowdhury, Kaushik (December 2020, IEEE Global Communications Conference)

   5G and open radio access networks (Open RANs) will result in vendor-neutral hardware deployment that will require additional diligence towards managing security risks. This new paradigm will allow the same network infrastructure to support virtual network slices for transmit different waveforms, such as 5G New Radio, LTE, WiFi, at different times. In this multi- vendor, multi-protocol/waveform setting, we propose an additional physical layer authentication method that detects a specific emitter through a technique called as RF fingerprinting. Our deep learning approach uses convolutional neural networks augmented with triplet loss, where examples of similar/dissimilar signal samples are shown to the classifier over the training duration. We demonstrate the feasibility of RF fingerprinting base stations over the large-scale over-the-air experimental POWDER platform in Salt Lake City, Utah, USA. Using real world datasets, we show how our approach overcomes the challenges posed by changing channel conditions and protocol choices with 99.86% detection accuracy for different training and testing days. 

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