Number Theoretic Transform (NTT)-based polynomial multiplication is a computationally intensive operation in lattice-based post-quantum cryptography (PQC) schemes such as CRYSTALS-Dilithium. Existing hardware accelerators optimize area and timing performance, without focusing on evaluating trade-offs among hardware utilization, execution latency, operating frequency, and power consumption. This article investigates such trade-offs through two lightweight field-programmable gate array (FPGA) implementations of an iterative NTT-based polynomial multiplication accelerator, namely non-pipelined and 4-stage pipelined architectures. Both implementations employ a single butterfly unit based on Cooley–Tukey and Gentleman–Sande configurations to compute the forward NTT (FNTT), inverse NTT (INTT), and coefficient-wise multiplication (CWM). The 4-stage pipelined architecture employs pipeline registers in the modular multiplication and Barrett reduction datapaths to maximize the operating frequency. Both architectures are implemented on an Artix-7 FPGA and evaluated across operating frequencies ranging from 10 MHz to 280 MHz. The results show that the non-pipelined architecture provides reduced hardware overhead and lower power consumption, whereas the pipelined architecture improves timing scalability and successfully operates at 280 MHz. At the maximum operating frequency, the pipelined implementation utilizes 1115 slices and achieves execution times of 4.58 μs, 0.93μs, and 4.58μs for FNTT, CWM, and INTT computations, respectively, with an average power consumption of 133 mW. The Area–Time Product (ATP) and Energy–Delay Product (EDP) evaluations demonstrate that the pipelined architecture achieves improved overall efficiency within the proposed lightweight single-butterfly-based polynomial multiplication architecture at higher operating frequencies, obtaining an ATP of 7.74×103 Slice-μs and EDP of 1501.52 nJ-μs.
Omar S. Sonbul, Muhammad Rashid, K. Khayyat et al.· Electronics· 0 citations
Modern Industrial Internet of Things (IIoT) integrates machine learning models for monitoring and control. However, they remain vulnerable to adversarial machine learning (AML) attacks, where an adversary adds small changes to the input data. These small changes degrade model quality, reduce accuracy, and can ultimately compromise the safety and security of the entire system. AML research in IIoT often focuses on individual attack types, defense methods, and datasets. Existing reviews lack a unified quantitative and system-level perspective. Therefore, a systematic literature review (SLR) is needed to provide a holistic analysis of existing attacks, defenses, and databases. This SLR analyzes 50 research articles to provide a holistic view of AML threats in IIoT systems and identifies seven distinct attack types: gradient-based perturbations, GAN-generated samples, poisoning attacks, reinforcement learning-based (RL) strategies, saliency-based feature manipulation, false data injection, and hybrid approaches. To illustrate the range of observed impacts, selected studies report the following degradation examples: saliency-based attacks cause accuracy reductions of 6–11 percentage points; iterative gradient attacks reduce accuracy from 95–99% to 30–40% in SIEM systems; and RL-based attacks reduce detection rates from 100% to 0% in rule-based IDS settings. In addition to the analysis of attack types, this SLR also evaluates current defense methods to protect IIoT systems. It has been observed that existing defense mechanisms lack generalization and require high computational resources. Moreover, the testing is performed under simplified threat models. The analysis of datasets further shows a clear gap between realistic industrial benchmarks (such as SWaT, WADI, and NSL-KDD) and synthetic datasets used for controlled experiments. By connecting attack behavior, defense performance, dataset characteristics, and system-level effects, this SLR identifies the key research gaps that must be addressed in future work.
Abeer Alsaidlani, Muhammad Rashid, M. Aljabri· Italian National Conference...· 0 citations
