SearcharxivSearch

arXiv subjects

Jung-Min Park

Publications and source records attributed to Jung-Min Park.

3 recordsLinked to original sources

Cumulative Message Authentication Codes for Resource-Constrained IoT Networks

In resource-constrained IoT networks, the use of conventional message authentication codes (MACs) to provide message authentication and integrity is not possible due to the large size of the MAC output. A straightforward yet naive solution to this problem is to employ a truncated MAC which undesirably sacrifices cryptographic strength in exchange for reduced communication overhead. In this paper, we address this problem by proposing a novel approach for message authentication called \textit{Cumulative Message Authentication Code} (CuMAC), which consists of two distinctive procedures: \textit{aggregation} and \textit{accumulation}. In aggregation, a sender generates compact authentication tags from segments of multiple MACs by using a systematic encoding procedure. In accumulation, a receiver accumulates the cryptographic strength of the underlying MAC by collecting and verifying the authentication tags. Embodied with these two procedures, CuMAC enables the receiver to achieve an advantageous trade-off between the cryptographic strength and the latency in processing of the authentication tags. Furthermore, for some latency-sensitive messages where this trade-off may be unacceptable, we propose a variant of CuMAC that we refer to as \textit{CuMAC with Speculation} (CuMAC/S). In addition to the aggregation and accumulation procedures, CuMAC/S enables the sender and receiver to employ a speculation procedure for predicting future message values and pre-computing the corresponding MAC segments. For the messages which can be reliably speculated, CuMAC/S significantly reduces the MAC verification latency without compromising the cryptographic strength. We have carried out comprehensive evaluation of CuMAC and CuMAC/S through simulation and a prototype implementation on a real car.

cs.CR

Next Generation Wi-Fi and 5G NR-U in the 6 GHz Bands: Opportunities & Challenges

The ever-increasing demand for unlicensed spectrum has prompted regulators in the US and Europe to consider opening up the 6 GHz bands for unlicensed access. These bands will open up 1.2 GHz of additional spectrum for unlicensed radio access technologies (RATs), such as Wi-Fi and 5G New Radio Unlicensed (NR-U), in the US and if permitted, 500 MHz of additional spectrum in Europe. The abundance of spectrum in these bands creates new opportunities for the design of mechanisms and features that can support the emerging bandwidth-intensive and latency-sensitive applications. However, coexistence of unlicensed devices both with the bands' incumbent users and across different unlicensed RATs present significant challenges. In this paper, we provide a comprehensive survey of the existing literature on various issues surrounding the operations of unlicensed RATs in the 6 GHz bands. In particular, we discuss how key features in next-generation Wi-Fi are being designed to leverage these additional unlicensed bands. We also shed light on the foreseeable challenges that designers of unlicensed RATs might face in the near future. Our survey encompasses key research papers, contributions submitted to standardization bodies and regulatory agencies, and documents presented at various other venues. Finally, we highlight a few key research problems that are likely to arise due to unlicensed operations in the 6 GHz bands. Tackling these research challenges effectively will be critical in ensuring that the new unlicensed bands are efficiently utilized while guaranteeing the interference-free operation of the bands' incumbent users.

cs.NI

Security of Spectrum Learning in Cognitive Radios

Due to delay and energy constraints, a cognitive radio may not be able to perform spectrum sensing in all available channels. Therefore, a sensing policy is needed to decide which channels to sense. The channel selection problem is the problem of designing such a sensing policy to maximize throughput while avoiding interference to primary users. The channel selection problem can be formulated as a reinforcement learning problem. Channel selection schemes that employ reinforcement machine learning algorithms are vulnerable to belief manipulation attacks that contaminate the knowledge base of the learning algorithms. In this paper, we analyze the security of channel selection algorithms that are based on reinforcement learning and propose mitigation techniques that make these algorithms more robust against belief manipulation attacks.

cs.CR