Delay Performance of Distributed Physical Layer Authentication Under Sybil Attacks

Physical layer authentication (PLA) has recently been discussed in the context of URLLC due to its low complexity and low overhead. Nevertheless, these schemes also introduce additional sources of error through missed detections and false alarms. The trade-offs of these characteristics are strongly dependent on the deployment scenario as well as the processing architecture. Thus, considering a feature-based PLA scheme utilizing channel-state information at multiple distributed radio-heads, we study these trade-offs analytically. We model and analyze different scenarios of centralized and decentralized decision-making and decoding, as well as the impacts of a single-antenna attacker launching a Sybil attack. Based on stochastic network calculus, we provide worst-case performance bounds on the system-level delay for the considered distributed scenarios under a Sybil attack. Results show that the arrival-rate capacity for a given latency deadline is increased for the distributed scenarios. For a clustered sensor deployment, we find that the distributed approach provides 23% higher capacity when compared to the centralized scenario.

[1]  Nicola Laurenti,et al.  Physical Layer Authentication over MIMO Fading Wiretap Channels , 2012, IEEE Transactions on Wireless Communications.

[2]  Mats Bengtsson,et al.  Feasibility of large antenna arrays towards low latency ultra reliable communication , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[3]  James Gross,et al.  Performance Analysis of Distributed SIMO Physical Layer Authentication , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[4]  Raja Sattiraju,et al.  Application of Machine Learning for Channel based Message Authentication in Mission Critical Machine Type Communication , 2017, ArXiv.

[5]  James Gross,et al.  On the Impact of Feature-Based Physical Layer Authentication on Network Delay Performance , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[6]  Frank Barickman,et al.  Enhanced Authentication Based on Angle of Signal Arrivals , 2019, IEEE Transactions on Vehicular Technology.

[7]  Raja Sattiraju,et al.  Supervised Learning for Physical Layer Based Message Authentication in URLLC Scenarios , 2019, 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall).

[8]  Jörg Liebeherr,et al.  Network-Layer Performance Analysis of Multihop Fading Channels , 2016, IEEE/ACM Transactions on Networking.

[9]  James Gross,et al.  Bound-based power optimization for multi-hop heterogeneous wireless industrial networks under statistical delay constraints , 2019, Comput. Networks.

[10]  Paolo Baracca,et al.  URLLC for Factory Automation: an Extensive Throughput-Reliability Analysis of D-MIMO , 2020, WSA.

[11]  Amr Rizk,et al.  A Guide to the Stochastic Network Calculus , 2015, IEEE Communications Surveys & Tutorials.

[12]  Henrik Forssell,et al.  Worst-Case Detection Performance for Distributed SIMO Physical Layer Authentication , 2020 .

[13]  He Chen,et al.  Physical Layer Authentication for Non-coherent Massive SIMO-Based Industrial IoT Communications , 2020, 2020 IEEE Wireless Communications and Networking Conference (WCNC).

[14]  James Gross,et al.  Physical Layer Authentication in Mission-Critical MTC Networks: A Security and Delay Performance Analysis , 2019, IEEE Journal on Selected Areas in Communications.

[15]  Larry J. Greenstein,et al.  Fingerprints in the Ether: Using the Physical Layer for Wireless Authentication , 2007, 2007 IEEE International Conference on Communications.

[16]  Xianbin Wang,et al.  Robust physical layer authentication using inherent properties of channel impulse response , 2011, 2011 - MILCOM 2011 Military Communications Conference.