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Lecture notes in computer science

A SLR of Modern AI-Driven SCA on NIST’s PQC Standards

Edgard Nicéas Arcoverde Gusmão Lima, Fábio Wlademir Monteiro Maia, Tiago A. E. Ferreira, Danilo Monteiro Ribeiro

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-25149-7_16

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This survey is on forward-looking, emerging security concerns in post-quantum era, i.e., the implementation attacks for 2022 winners of NIST post-quantum cryptography (PQC) competition and thus the visions, insights, and discussions can be used as a step forward towards scrutinizing the new standards for applications ranging from Metaverse, Web 3.0 to deeply-embedded systems. The rapid advances in quantum computing have brought immense opportunities for scientific discovery and technological progress; however, it poses a major risk to today's security since advanced quantum computers are believed to break all traditional public-key cryptographic algorithms. This has led to active research on PQC algorithms that are believed to be secure against classical and powerful quantum computers. However, algorithmic security is unfortunately insufficient, and many cryptographic algorithms are vulnerable to side-channel attacks (SCA), where an attacker passively or actively gets side-channel data to compromise the security properties that are assumed to be safe theoretically. In this survey, we explore such imminent threats and their countermeasures with respect to PQC. We provide the respective, latest advancements in PQC research, as well as assessments and providing visions on the different types of SCAs.
The accelerating development of quantum computing creates substantial cybersecurity challenges for cloud and Internet of Things (IoT) environments that continue to depend on quantum-vulnerable public-key cryptography. This study developed and quantitatively evaluated an AI-driven quantum-resistant security framework for accelerating the migration of U.S. cloud and IoT systems toward National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standards. The final analytical environment included 52 organizations, 164 cloud environments, 18,764 IoT devices, and 139,684 retained cryptographic assets, with 98.7% data completeness and 97.9% inventory-validation accuracy. Among retained assets, 62.1% contained quantum-vulnerable dependencies, including 34.6% associated with RSA, 20.0% with elliptic-curve cryptography, and 7.5% with Diffie–Hellman, while 63.2% of certificates remained quantum vulnerable. AI-assisted cryptographic discovery achieved 97.4% asset coverage and 97.9% vulnerability-detection sensitivity while reducing assessment time by 87.8%. XGBoost achieved the strongest quantum-risk classification performance, with 97.6% accuracy, 97.5% F1-score, 0.994 ROC-AUC, and 0.992 PR-AUC. Experimental evaluation of ML-KEM, ML-DSA, and SLH-DSA showed that pure PQC increased cloud secure-session latency by 23.8% and reduced throughput by 10.0%, whereas hybrid configurations increased latency by 39.5% and reduced throughput by 15.6%. AI-assisted migration increased implementation success from 84.6% to 97.1%, reduced remediation time by 60.2%, increased migration throughput by 151.1%, and reduced unresolved quantum-vulnerable exposure from 15.4% to 2.9%. High-security-maturity organizations achieved 98.8% implementation success compared with 87.8% among low-maturity organizations. Validation confirmed strong generalizability, with XGBoost achieving 97.1% cross-validation accuracy, 96.9% independent holdout accuracy, and 95.9% temporal-validation accuracy. Overall, the findings demonstrated that integrating AI-assisted cryptographic discovery, quantum-risk classification, migration prioritization, PQC performance assessment, cryptographic agility, interoperability testing, and organizational readiness provided a robust quantitative framework for strengthening post-quantum security across heterogeneous U.S. cloud and IoT infrastructures.