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The lack of authentication protection for bootstrapping messages broadcast by base-stations makes impossible for devices to differentiate between a legitimate and a fake base-station. This vulnerability has been widely acknowledged, but not yet fixed and thus enables law-enforcement agencies, motivated adversaries, and nation-states to carry out attacks against targeted users. Although 5G cellular protocols have been enhanced to prevent some of these attacks, the root vulnerability for fake base-stations still exists. In this paper, we propose an efficient broadcast authentication protocol based on a hierarchical identity-based signature scheme, Schnorr-HIBS, which addresses the root cause of the fake base-station problem with minimal computation and communication overhead. We implement and evaluate our proposed protocol using off-the-shelf software-defined radios and open-source libraries. We also provide a comprehensive quantitative and qualitative comparison between our scheme and other candidate solutions for 5G base-station authentication proposed by 3GPP. Our proposed protocol achieves at least a 6x speedup in terms of end-to-end cryptographic delay and a communication cost reduction of 31% over other 3GPP proposals. 

Digital signatures are a fundamental building block for ensuring integrity and authenticity of contents delivered by the Named Data Networking (NDN) systems. However, current digital signature schemes adopted by NDN open source libraries have a high computational and communication overhead making them unsuitable for high throughput applications like video streaming and virtual reality gaming. In this poster, we propose a real-time digital signature mechanism for NDN based on the offline-online signature framework known as Structure-free and Compact Real-time Authentication scheme (SCRA). Our signature mechanism significantly reduces the signing and verification costs and provides different variants to optimize for the specific requirements of applications (i.e. signing overhead, verification overhead or communication cost). Our experiments results show that SCRA is a suitable framework for latency-sensitive NDN applications. 

Abstract This paper focuses on protecting the cellular paging protocol — which balances between the quality-of-service and battery consumption of a device — against security and privacy attacks. Attacks against this protocol can have severe repercussions, for instance, allowing attacker to infer a victim’s location, leak a victim’s IMSI, and inject fabricated emergency alerts. To secure the protocol, we first identify the underlying design weaknesses enabling such attacks and then propose efficient and backward-compatible approaches to address these weaknesses. We also demonstrate the deployment feasibility of our enhanced paging protocol by implementing it on an open-source cellular protocol library and commodity hardware. Our evaluation demonstrates that the enhanced protocol can thwart attacks without incurring substantial overhead. 

Health monitoring of civil infrastructures is a key application of Internet of things (IoT), while edge computing is an important component of IoT. In this context, swarms of autonomous inspection robots, which can replace current manual inspections, are examples of edge devices. Incorporation of pretrained deep learning algorithms into these robots for autonomous damage detection is a challenging problem since these devices are typically limited in computing and memory resources. This study introduces a solution based on network pruning using Taylor expansion to utilize pretrained deep convolutional neural networks for efficient edge computing and incorporation into inspection robots. Results from comprehensive experiments on two pretrained networks (i.e., VGG16 and ResNet18) and two types of prevalent surface defects (i.e., crack and corrosion) are presented and discussed in detail with respect to performance, memory demands, and the inference time for damage detection. It is shown that the proposed approach significantly enhances resource efficiency without decreasing damage detection performance.