[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126412-en":3,"doc-seo-126412-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126412,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning and Side-Channel Attacks on Post-Quantum Cryptography - Survey and Review","The document reviews how the shift to post-quantum cryptography (PQC) accelerates alongside threats from quantum computing that can undermine classical public-key systems. While schemes like CRYSTALS-Kyber, CRYSTALS-Dilithium, and SPHINCS+ show strong theoretical security, real deployments remain exposed to side-channel attacks (SCAs) exploiting power, electromagnetic emissions, and timing leakage to recover secrets. It surveys classical and machine learning-based profiling methods, assesses effectiveness, and discusses limitations of existing countermeasures. Hybrid defense strategies are outlined to improve secure implementation in embedded, resource-constrained environments.","Machine Learning and Side-Channel Attacks on Post-Quantum Cryptography  \nAbiodun Olaluwe, Nouf Nur Nabilah, Sheikh Tareq Ahmed, Akshay Raghavendra Kulkarni and Annamalai Annamalai  \nPrairie View A&M University, [Prairie View TX USA](Prairie View TX USA aolaluwe@pvamu.edu)[ aolaluwe@pvamu.edu](Prairie View TX USA aolaluwe@pvamu.edu),nnabilah@pvamu. edu,sahmed13@pvamu .edu,arkulkarni@pvamu .edu,aaannamalai@pvamu .edu  \nAbstract. The transition to post-quantum cryptography (PQC) is accelerating due to the potential of quantum computing to compromise classical public-key cryptosystems.  \nWhile standardized schemes such as CRYSTALS-Kyber, CRYSTALS-Dilithium, and SPHINCS+ offer strong theoretical security, practical deployments remain susceptible to physical-layer vulnerabilities, notably side-channel attacks (SCAs) . SCAs exploit unintentional leakages in hardware and software implementations—such as power traces, electromagnetic emissions, and timing variations—to recover secret keys without altering the target system. These attacks are non-invasive, cost-effective, and applicable across diverse platforms, making them a critical threat vector for PQC in embedded and resource-constrained environments.  \nThis survey provides a structured, in-depth review of SCAs targeting PQC implementations, encompassing both classical methods—such as Simple Power Analysis, Differential Power Analysis, Correlation Power Analysis, Template Attacks, and Mutual Information Analysis—and emerging machine learning (ML)-driven approaches.  \nSpecial attention is given to deep learning models, including CNNs, RNNs, and MLPs, which have demonstrated superior performance in profiling attacks by automatically learning leakage patterns from high-dimensional trace data, even in the presence of countermeasures like masking and desynchronization.  \nWe categorize and compare recent attack strategies, analyze their effectiveness against various PQC schemes, and examine the limitations of existing countermeasures.  \nFinally, we identify open research challenges and outline hybrid defense strategies that integrate classical protections with adaptive, ML-aware mitigation techniques.  \nThis comprehensive synthesis aims to bridge the gap between PQC algorithm design and secure, implementation-level deployment in the quantum era.  \nKeywords: Post-Quantum Cryptography · Side-Channel Attacks · Machine Learning  \n· Cryptographic Hardware · Cybersecurity  \n1 Introduction  \nThe advent of practical quantum computing threatens the foundational security of modern digital infrastructure. Classical public-key systems such as RSA and ECC, which underpin secure communications and authentication, are vulnerable to quantum algorithms like Shor’s and Grover’s, [1, 2] . In response, post-quantum cryptography (PQC) has emerged as strategic, imperative, and offering cryptographic schemes based on mathematically hard problems believed to be resistant to both classical and quantum adversaries. These schemes—including lattice-based, code-based, and hash-based constructions—are undergoing standardization by the National Institute of Standards and Technology (NIST) [3, 4] .  \nHowever, beyond mathematical soundness, the physical realization of PQC algorithms introduces new attack vectors, such as side-channel attacks (SCAs), fault injection attacks  \n(FIAs), electromagnetic (EM) analysis, and related physical leakage attacks. These physical attacks are not new: they have historically posed significant risks to conventional cryptographic implementations like RSA and AES. [5–9]  \nIn general-purpose embedded systems, SCAs have demonstrated the ability to extract sensitive secrets (e.g., encryption keys, biometric templates) by exploiting physical phenomena such as power consumption, EM emissions, timing variations, and acoustic signals. For instance, classic differential power analysis (DPA) attacks have successfully recovered AES keys with just a few thousand power traces. Timing attacks on RSA ca","cbCaihja0wSNDPmj","https://ap.wps.com/l/cbCaihja0wSNDPmj","pdf",715670,6,1,31,"English","en",105,"# 1 Introduction\n## Quantum computing threats and PQC overview\n## Physical-layer vulnerabilities and SCA context\n## Why SCAs become more severe for PQC\n# 2 Survey of SCA techniques and ML-driven approaches\n## Classical SCA methods\n## Deep learning and profiling attacks\n## Countermeasures and their limitations\n# 3 Challenges and hybrid defense strategies\n## Open research problems\n## Hybrid defense integrating classical and ML-aware methods","[{\"question\":\"Why is post-quantum cryptography still vulnerable after standardization?\",\"answer\":\"PQC schemes resist known mathematical attacks, but physical implementations can leak information through power use, electromagnetic emissions, and timing variations, enabling side-channel attacks to recover secrets.\"},{\"question\":\"What makes machine learning useful for side-channel attacks on PQC implementations?\",\"answer\":\"Deep learning models can automatically learn leakage patterns from high-dimensional trace data, improving profiling attack performance even under countermeasures such as masking and desynchronization.\"},{\"question\":\"Which classical side-channel methods are reviewed in the document?\",\"answer\":\"The review covers Simple Power Analysis, Differential Power Analysis, Correlation Power Analysis, Template Attacks, and Mutual Information Analysis, along with emerging ML-driven approaches targeting PQC.\"}]","Machine Learning and Side-Channel Attacks on Post-Quantum Cryptography - Survey and Review | PDF",1785904922,78,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-and-side-channel-attacks-on-post-quantum-cryptography-survey-and-review","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-and-side-channel-attacks-on-post-quantum-cryptography-survey-and-review/126412/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is post-quantum cryptography still vulnerable after standardization?","Question",{"text":77,"@type":78},"PQC schemes resist known mathematical attacks, but physical implementations can leak information through power use, electromagnetic emissions, and timing variations, enabling side-channel attacks to recover secrets.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What makes machine learning useful for side-channel attacks on PQC implementations?",{"text":82,"@type":78},"Deep learning models can automatically learn leakage patterns from high-dimensional trace data, improving profiling attack performance even under countermeasures such as masking and desynchronization.",{"name":84,"@type":75,"acceptedAnswer":85},"Which classical side-channel methods are reviewed in the document?",{"text":86,"@type":78},"The review covers Simple Power Analysis, Differential Power Analysis, Correlation Power Analysis, Template Attacks, and Mutual Information Analysis, along with emerging ML-driven approaches targeting PQC.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]