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1. Enabling Second Factor Authentication for Drones in 5G using Network Slicing

   https://doi.org/10.1109/GCWkshps50303.2020.9367441  

   Abdel-Malek, Mai A.; Akkaya, Kemal; Bhuyan, Arupjyoti; Cebe, Mumin; Ibrahim, Ahmed S. (March 2021, 2020 IEEE Globecom Workshops (GC Wkshps))

   null (Ed.)

   As 5G systems are starting to be deployed and becoming part of many daily life applications, there is an increasing interest on the security of the overall system as 5G network architecture is significantly different than LTE systems. For instance, through application specific virtual network slices, one can trigger additional security measures depending on the sensitivity of the running application. Drones utilizing 5G could be a perfect example as they pose several safety threats if they are compromised. To this end, we propose a stronger authentication mechanism inspired from the idea of second-factor authentication in IT systems. Specifically, once the primary 5G authentication is executed, a specific slice can be tasked to trigger a second-factor authentication utilizing different factors from the primary one. This trigger mechanism utilizes the re-authentication procedure as specified in the 3GPP 5G standards for easy integration. Our second-factor authentication uses a special challenge-response protocol, which relies on unique drone digital ID as well as a seed and nonce generated from the slice to enable freshness. We implemented the proposed protocol in ns-3 that supports mmWave-based communication in 5G. We demonstrate that the proposed protocol is lightweight and can scale while enabling stronger security for the drones. 

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2. 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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3. Hermes: Unlocking Security Analysis of Cellular Network Protocols by Synthesizing Finite State Machines from Natural Language Specifications

   Ishtiaq, Abdullah_Al; Das, Sarkar_Snigdha S; Rashid, Syed_Md M; Ranjbar, Ali; Tu, Kai; Wu, Tianwei; Song, Zhezheng; Wang, Weixuan; Akon, Mujtahid; Zhang, Rui; et al (August 2024, USENIX Association)

   In this paper, we present Hermes, an end-to-end framework to automatically generate formal representations from natural language cellular specifications. We first develop a neural constituency parser, NEUTREX, to process transition-relevant texts and extract transition components (i.e., states, conditions, and actions). We also design a domain-specific language to translate these transition components to logical formulas by leveraging dependency parse trees. Finally, we compile these logical formulas to generate transitions and create the formal model as finite state machines. To demonstrate the effectiveness of Hermes, we evaluate it on 4G NAS, 5G NAS, and 5G RRC specifications and obtain an overall accuracy of 81-87%, which is a substantial improvement over the state-of-the-art. Our security analysis of the extracted models uncovers 3 new vulnerabilities and identifies 19 previous attacks in 4G and 5G specifications, and 7 deviations in commercial 4G basebands. 

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4. On the Elliptic Curve Cryptography for Privacy-Aware Secure ACO-AODV Routing in Intent-Based Internet of Vehicles for Smart Cities

   https://doi.org/10.1109/TITS.2020.3008361  

   Safavat, Sunitha; Rawat, Danda B. (June 2020, IEEE Transactions on Intelligent Transportation Systems)

   null (Ed.)

   Internet of Vehicles (IoV) in 5G is regarded as a backbone for intelligent transportation system in smart city, where vehicles are expected to communicate with drivers, with road-side wireless infrastructure, with other vehicles, with traffic signals and different city infrastructure using vehicle-to-vehicle (V2V) and/or vehicle-to-infrastructure (V2I) communications. In IoV, the network topology changes based on drivers' destination, intent or vehicles' movements and road structure on which the vehicles travel. In IoV, vehicles are assumed to be equipped with computing devices to process data, storage devices to store data and communication devices to communicate with other vehicles or with roadside infrastructure (RSI). It is vital to authenticate data in IoV to make sure that legitimate data is being propagated in IoV. Thus, security stands as a vital factor in IoV. The existing literature contains some limitations for robust security in IoV such as high delay introduced by security algorithms, security without privacy, unreliable security and reduced overall communication efficiency. To address these issues, this paper proposes the Elliptic Curve Cryptography (ECC) based Ant Colony Optimization Ad hoc On-demand Distance Vector (ACO-AODV) routing protocol which avoids suspicious vehicles during message dissemination in IoV. Specifically, our proposed protocol comprises three components: i) certificate authority (CA) which maps vehicle's publicly available info such as number plates with cryptographic keys using ECC; ii) malicious vehicle (MV) detection algorithm which works based on trust level calculated using status message interactions; and iii) secure optimal path selection in an adaptive manner based on the intent of communications using ACO-AODV that avoids malicious vehicles. Experimental results illustrate that the proposed approach provides better results than the existing approaches. 

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5. Constant Latency in Sleepy Consensus

   https://doi.org/10.1145/3548606.3559347  

   Momose, Atsuki; Ren, Ling (November 2022, Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security)

   Dynamic participation support is an important feature of Bitcoin's longest-chain protocol and its variants. But these protocols suffer from long latency as a fundamental trade-off. Specifically, the latency depends at least on the following two factors: 1) the desired security level of the protocol, and 2) the actual participation level of the network. Classic BFT protocols, on the other hand, can achieve constant latency but cannot make progress under dynamic participation. In this work, we present a protocol that simultaneously supports dynamic participation and achieves constant latency. Our core technique is to extend the classic BFT approach from static quorum size to dynamic quorum size, i.e., according to the current participation level, while preserving important properties of static quorum. We also present a recovery mechanism for rejoining nodes that is efficient in terms of both communication and storage. Our experimental evaluation shows our protocol has much lower latency than a longest-chain protocol, especially when there is a sudden decrease of participation. 

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