Key exchange mechanisms are foundational to secure communication, yet traditional methods face challenges from quantum computing. The Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) is a post-quantum cryptographic key exchange protocol with unknown successful quantum vulnerabilities. This study evaluates the ML-KEM using experimental benchmarks. We implement the ML-KEM in Python for clarity and in C++ for performance, demonstrating the latter’s substantial performance improvements. The C++ implementation achieves microsecond-level execution times for key generation, encapsulation, and decapsulation. Python, while slower, provides a user-friendly introduction to the ML-KEM’s operation. Moreover, our Python benchmark confirmed that the ML-KEM consistently outperformed RSA in execution speed across all tested parameters. Beyond benchmarking, the ML-KEM is shown to handle the computational hardness of the Module Learning With Errors (MLWE) problem, ensuring resilience against quantum attacks, classical attacks, and Artificial Intelligence (AI)-based attacks, since the ML-KEM has no pattern that could be detected. To evaluate its practical feasibility on constrained devices, we also tested the C++ implementation on a Raspberry Pi 4B, representing IoT use cases. Additionally, we attempted to run integration and benchmark tests for the ML-KEM on microcontrollers such as the ESP32 DevKit, ESP32 Super Mini, ESP8266, and Raspberry Pi Pico, but these trials were unsuccessful due to memory constraints. The results showed that while the ML-KEM can operate effectively in such environments, only devices with sufficient resources and runtimes can support its computational demands. While resource-intensive, the ML-KEM offers scalable security across diverse domains such as IoT, cloud environments, and financial systems, making it a key solution for future cryptographic standards.
Key exchange mechanisms are foundational to secure communication, yet traditional methods face challenges from quantum computing. The Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) is a post-quantum cryptographic key exchange protocol with unknown successful quantum vulnerabilities. This study evaluates the ML-KEM using experimental benchmarks. We implement the ML-KEM in Python for clarity and in C++ for performance, demonstrating the latter’s substantial performance improvements. The C++ implementation achieves microsecond-level execution times for key generation, encapsulation, and decapsulation. Python, while slower, provides a user-friendly introduction to the ML-KEM’s operation. Moreover, our Python benchmark confirmed that the ML-KEM consistently outperformed RSA in execution speed across all tested parameters. Beyond benchmarking, the ML-KEM is shown to handle the computational hardness of the Module Learning With Errors (MLWE) problem, ensuring resilience against quantum attacks, classical attacks, and Artificial Intelligence (AI)-based attacks, since the ML-KEM has no pattern that could be detected. To evaluate its practical feasibility on constrained devices, we also tested the C++ implementation on a Raspberry Pi 4B, representing IoT use cases. Additionally, we attempted to run integration and benchmark tests for the ML-KEM on microcontrollers such as the ESP32 DevKit, ESP32 Super Mini, ESP8266, and Raspberry Pi Pico, but these trials were unsuccessful due to memory constraints. The results showed that while the ML-KEM can operate effectively in such environments, only devices with sufficient resources and runtimes can support its computational demands. While resource-intensive, the ML-KEM offers scalable security across diverse domains such as IoT, cloud environments, and financial systems, making it a key solution for future cryptographic standards.
The integration of NIST-standardized lattice-based cryptographic algorithms (ML-KEM and ML-DSA) into TLS 1.3 enables post-quantum secure authentication for Industrial IoT (IIoT) environments. This work implements these algorithms within the X.509 certificate infrastructure and evaluates their performance on resource-constrained IIoT hardware (Raspberry Pi 4). Experimental measurements covering key generation, encapsulation, decapsulation, and signature operations were obtained using a liboqs-enabled TLS 1.3 stack. The results show that PQ TLS 1.3 achieves comparable handshake latency to conventional TLS on IIoT-class gateways, with certificate size identified as the dominant overhead. These findings confirm the practicality of post-quantum authentication in IIoT systems while acknowledging limitations related to the hardware scope and simulated networking. Future work will extend validation to field deployments and additional PQC candidates.
The recent introduction of Google’s Willow quantum chip has demonstrated remarkable computational capabilities, solving problems in microseconds that would take classical computers millions of years. As quantum computers advance toward cryptographic relevance, existing encryption algorithms become vulnerable to attacks that could break them within seconds or minutes, rendering current cryptographic protections ineffective. This necessitates the establishment of post-quantum cryptography (PQC) standards to secure systems in the quantum era. This study evaluates the performance of NIST-standardized PQC algorithms ML-KEM (512, 768, 1024) and ML-DSA (2, 3, 5) on a Raspberry Pi 4 Model B, representing baseline IoT device capabilities. Using the Liboqs library, each algorithm was executed across 1000 iterations in a real-world wireless network environment connected to a router, accounting for practical network conditions such as packet loss, latency, and jitter to assess computational feasibility under resource-constrained conditions. Experimental results demonstrate the practical viability of PQC algorithms in IoT environments. ML-KEM-512 achieves encapsulation in 55.76 μs and decapsulation in 56.15 μs, with key generation in 123.78 μs (median), while ML-DSA-44 completes signature generation in 379.83 μs and verification in 159.38 μs. These findings confirm that both ML-KEM and ML-DSA algorithms offer efficient and rapid performance, making them suitable candidates for securing IoT devices against quantum threats.
Quantum computing poses a significant threat to Internet of Things (IoT) communications that rely on classical cryptographic algorithms such as RSA, Diffie–Hellman, and Elliptic Curve Cryptography (ECC). These algorithms are vulnerable to Shor’s algorithm, making long-term data security uncertain, particularly for IoT devices with extended operational lifespans. Post-quantum cryptographic (PQC) algorithms offer resistance to quantum attacks; however, their deployment on resource-constrained IoT devices remains challenging due to limitations in processing power, memory, and energy consumption. This study benchmarks seven Key Encapsulation Mechanisms (KEMs) ML-KEM, Kyber, NTRU, BIKE, Classic McEliece, FrodoKEM, and sntrup761 across key generation, encapsulation, and decapsulation operations. Experiments were conducted using the liboqs library in a virtualized Linux environment running WSL2 to simulate constrained computing conditions. Results revealed substantial performance differences among the algorithms. ML-KEM-512 achieved the best overall performance, completing all operations in under 14 µs, while Kyber-512 showed similar efficiency. NTRU provided the fastest encapsulation but incurred significantly higher key-generation costs. BIKE, FrodoKEM, and Classic McEliece exhibited execution times unsuitable for real-time IoT applications. Overall, lattice-based schemes, particularly ML-KEM and Kyber, emerged as the most practical PQC solutions for resource-constrained IoT environments. The findings also demonstrate the usefulness of virtualized benchmarking for deployment planning.
With the advent of quantum computers, traditional key exchange mechanisms are under threat, necessitating the development of new methods. Recently, Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM) has been standardized as a quantum-safe key exchange method. Another emerging technique is Quantum Key Distribution (QKD) and its most famous protocol, BB84, which relies on the principles of quantum physics to exchange key information. Both techniques are considered secure against attacks by quantum computers, but their security is based on different principles. However, both key exchange types include a statistical component which, if an attacker were lucky, could allow circumventing these underlying hard problems. In this paper, we suggest that as a key exchange mechanism, BB84 should be run with security parameters comparable to those of ML-KEM, and analyze performance implications if this choice is taken. We illustrate the impact by estimating the number of raw bits required to generate a 256-bit symmetric key using BB84 and assessing the resulting performance implications. We further show how the BB84 finite-size security parameter can be chosen such that the post-processing failure probability is of the same order as the cumulative decapsulation-failure probability of ML-KEM. According to our results, the security parameter in BB84 should be at most 2−75.8 if statistical failure probability comparable to the ML-KEM decapsulation-failure target is desired. These findings offer general guidelines for BB84 parameter selection, with hybrid protocol design representing one potential application context.
The emergence of quantum computing poses an existential threat to classical cryptographic systems that form the backbone of contemporary digital security infrastructure. RSA, the most widely deployed public-key cryptosystem, relies on the computational hardness of integer factorization, a problem efficiently solvable by quantum computers using Shor's algorithm. This impending vulnerability has catalyzed the development of post-quantum cryptographic alternatives designed to withstand both classical and quantum attacks. CRYSTALS-Kyber, standardized by NIST as ML-KEM, represents the forefront of latticebased key encapsulation mechanisms, offering quantum resistance through the Module Learning With Errors (MLWE) problem. This paper presents a comprehensive comparative analysis of RSA and Kyber, examining their mathematical foundations, security architectures, operational characteristics, and performance metrics across diverse computational environments. The study investigates the fundamental trade-offs between legacy systems optimized for classical threat models and emerging quantum-resistant algorithms designed for future-proof security. Through detailed analysis of key generation, encryption/encapsulation, decryption/decapsulation operations, and security guarantees, this research establishes the technical rationale behind NIST's selection of Kyber as the primary post-quantum key establishment mechanism. The findings demonstrate that while RSA maintains advantages in key size efficiency, Kyber's superior performance profile, quantum resilience, and robust security foundation position it as the essential successor for securing digital communications in the quantum era. This comprehensive study provides critical insights for organizations transitioning from classical to post-quantum cryptographic infrastructure
In the era of rapid development of quantum computers and post-quantum algorithms, basic cryptographic algorithms like RSA (Rivest-Shamir-Adleman) become vulnerable, especially under the influence of possible factorization by Shor's algorithm. This research work analyzes safety challenges and possible hazards caused by quantum attacks on the RSA, while also presenting ML-KEM (Module-Lattice-Based Key-Encapsulation Mechanism) as a convenient, standardized post-quantum algorithm that secures the safe exchange of keys. Besides the fundamental theoretical basis, the research work presents a clear demonstration of the technical implementation of the ML-KEM algorithm, including its architecture and principles of operation. Also, this research work compares the performances of ML-KEM and RSA algorithms using modern processor architecture, pointing out their superiority in the aspects of speed and energy efficiency. Furthermore, possible challenges caused by the migration of existing systems are presented.