arXiv subjects

Weitong Li

Publications and source records attributed to Weitong Li.

3 recordsLinked to original sources

arXiv2026

pqRPKI: A Practical RPKI Architecture for the Post-Quantum Era

The Resource Public Key Infrastructure (RPKI) secures Internet routing by binding IP prefixes to authorized Autonomous Systems, yet its RSA foundations are vulnerable to quantum adversaries. A naive swap to post-quantum (PQ) signatures (eg Falcon) is a poor fit for RPKI's bulk model: every relying party (RP) repeatedly fetches and validates the entire global repository, so larger keys and signatures inflate bandwidth and CPU cost, especially during a long dual-stack transition. We present pqRPKI , a post-quantum RPKI framework that pairs a multi-layer Merkle Tree Ladder (MTL) with RPKI objects, customized to relocate per-object verification material from certificates into the Manifest. To update RPKI for Merkle tree based schemes, pqRPKI redesign the RPKI manifest and delegation chain, introduces a ladder-guided sync and bulk-verification workflow that lets validators localize diffs top-down and rebuild trees bottom-up. pqRPKI also preserves current RPKI objects and encodings, supports both hosted and delegated operation, and provides an additive migration path that coexists with today's trust anchors for dual-stack deployment with little size overhead. Implemented as a working publication point (PP) and RPs, we show that pqRPKI reduces repository footprint to 546.8 MB on average (65.5%/83.1% smaller than Falcon/ML-DSA), cuts full-cycle validation to 102.7 s, and achieves 118.3 s end-to-end PP to Router time, enabling sub-2-minute operating cadences with full-repository validation each cycle. Dual-stack deployment with RSA only adds just 3.4% size overhead versus today's RPKI repositories.

cs.NI↗

arXiv2025

ASINT: Learning AS-to-Organization Mapping from Internet Metadata

Accurate AS-to-organization mapping underpins Internet measurement and security, yet registries are fragmented, PeeringDB is narrow, and routing views reflect connectivity rather than ownership. We take a pragmatic step: ASINT integrates curated web evidence with retrieval-guided LLM techniques and strict, evidence-cited validation to infer two relations (aliases and directed parent-child) and then revalidates them conservatively. To keep the dataset sustainable, we operate a public dashboard and API where operators can inspect per-ASN evidence and submit feedback that seeds refreshes. At scale, ASINT maps 112,172 ASNs into 82,840 organization families and, on overlapping AS sets, yields fewer, larger families with 21-24% more multi-AS groups than prior datasets (i.e., CAIDA AS2Org [11], AS2ORG+ [4], AS-Sibling [10], and Borges [28]). Quality is high in practice: ASINT achieves a precision of 0.9608, a recall of 0.9915 and an accuracy of 0.9752 under manual validation. Public deployment further drew operator-submitted reports for 595 ASNs across 106 organizations, with only 6 errors (99.0% observed clustering accuracy), with feedback coming from network operators across all RIR regions. Better organization context improves downstream analyses: +27.5% intra-organization RPKI misconfiguration detections, -9.4% benign hijack alerts, and -5.9% corrections to cases mislabeled as IP leasing. We release code, datasets, and the operator platform with APIs; given persistent ambiguity in organizational names and the continual evolution of corporate structures, an operator-in-the-loop process is essential; the platform records per ASN feedback with provenance and incorporates it into periodic refreshes and retraining. The methodology is model-agnostic and stands to improve further as base LLMs advance.

cs.NI↗

arXiv2019

Deep Nearest Class Mean Model for Incremental Odor Classification

In recent years, more machine learning algorithms have been applied to odor classification. These odor classification algorithms usually assume that the training datasets are static. However, for some odor recognition tasks, new odor classes continually emerge. That is, the odor datasets are dynamically growing while both training samples and number of classes are increasing over time. Motivated by this concern, this paper proposes a Deep Nearest Class Mean (DNCM) model based on the deep learning framework and nearest class mean method. The proposed model not only leverages deep neural network to extract deep features, but is also able to dynamically integrate new classes over time. In our experiments, the DNCM model was initially trained with 10 classes, then 25 new classes are integrated. Experiment results demonstrate that the proposed model is very efficient for incremental odor classification, especially for new classes with only a small number of training examples.

cs.LG↗