arXiv subjects

Sungwoo Kim

Publications and source records attributed to Sungwoo Kim.

3 recordsLinked to original sources

arXiv2025

SoK: Kicking CAN Down the Road. Systematizing CAN Security Knowledge

For decades, the Controller Area Network (CAN) has served as the primary in-vehicle bus (IVB) and extended its use to many non-vehicular systems. Over the past years, CAN security has been intensively scrutinized, yielding extensive research literature. Despite its wealth, the literature lacks structured systematization, complicating efforts to assess attack severity, defense efficacy, identify security gaps, or root causes. This leaves non experts uncertain about the relevancy of specific attacks or defenses to their systems, inadvertently portraying CAN as irredeemably insecure. Further, the introduction of new IVB technologies--CAN evolutions, add-ons, and alternative buses--with heightened security claims risks fostering the misconception that merely adopting these technologies resolves CAN's security challenges. This paper systematizes existing CAN security knowledge, presenting a comprehensive taxonomy and assessment models of attackers, attacks, and defenses. It identifies replicable attacks and defense gaps, investigating their root causes as inherent, accidental, unique, or universal. It then extrapolates these insights to emerging IVB technologies by formally analyzing three emerging IVBs to identify shared root causes with CAN and assess their ability to close security gaps. The findings challenge common perceptions, demonstrating that CAN is more securable than perceived, that most insecurity root causes are shared across IVBs, and that merely adopting newer IVB technology does not solve persistent security issues. The paper concludes by highlighting future research directions to secure IVB communication down the road.

cs.CR↗

arXiv2025

MARché: Fast Masked Autoregressive Image Generation with Cache-Aware Attention

Masked autoregressive (MAR) models unify the strengths of masked and autoregressive generation by predicting tokens in a fixed order using bidirectional attention for image generation. While effective, MAR models suffer from significant computational overhead, as they recompute attention and feed-forward representations for all tokens at every decoding step, despite most tokens remaining semantically stable across steps. We propose a training-free generation framework MARché to address this inefficiency through two key components: cache-aware attention and selective KV refresh. Cache-aware attention partitions tokens into active and cached sets, enabling separate computation paths that allow efficient reuse of previously computed key/value projections without compromising full-context modeling. But a cached token cannot be used indefinitely without recomputation due to the changing contextual information over multiple steps. MARché recognizes this challenge and applies a technique called selective KV refresh. Selective KV refresh identifies contextually relevant tokens based on attention scores from newly generated tokens and updates only those tokens that require recomputation, while preserving image generation quality. MARché significantly reduces redundant computation in MAR without modifying the underlying architecture. Empirically, MARché achieves up to 1.7x speedup with negligible impact on image quality, offering a scalable and broadly applicable solution for efficient masked transformer generation.

cs.LG↗

arXiv2025

Posted Price versus Hybrid Mechanisms in Freight Transportation Marketplaces

We consider a freight platform that serves as an intermediary between shippers and carriers in a truckload transportation network. The platform's objective is to design a policy that determines prices for shippers and payments to carriers, as well as how carriers are matched to loads to be transported, to maximize its long-run average profit. We propose a two-stage decision framework to model carriers' load choice behavior, where carriers choose a lane according to the multinomial logit (MNL) model based on the platform's posted price in the first stage and book a load in the second stage. We analyze two types of carrier-side mechanisms commonly used by freight platforms: a posted price mechanism and a hybrid mechanism where carriers can either book loads at posted price or submit their bids in an auction. The proposed mechanisms are constructed using a fluid approximation model to incorporate carrier interactions in the freight network. We show that the hybrid mechanism has higher profits than the posted price mechanism. We prove tight bounds between these mechanisms for varying market sizes. The findings are validated through a numerical simulation using industry data from the U.S. freight market.

math.OC↗