We propose in this paper an efficient FALCON accelerator called EFX based on a HW/SW co-design where FALCON is a post-quantum cryptographic (PQC) scheme tailored as a digital signature algorithm (DSA). Our findings reveal that FALCON exhibits unique characteristics and structures which distinguish it from other PQC-DSAs. A key finding is that, unlike its counterparts, FALCON doesn’t prioritize a single, time-consuming task; instead, it processes a variety of tasks with comparable execution times. Consequently, the conventional methods focusing on accelerating dominant few tasks, which are generally effective for other algorithms, prove less efficient for FALCON, especially concerning the minimization of the silicon area used. To overcome this, we strategically focus on the granular optimization of lower-level operations rather than on broader functional segments, aiming to boost performance while conserving hardware space. Moreover, to mitigate the potential degradation due to limitation of hardware resources, we have implemented a pipelined execution strategy for the FALCON functions and refined the sampling function–a critical task that is challenging to accelerate due to inherent sequential algorithm–enabling it to run concurrently on both software and hardware, thus reducing latency. Our hardware design, synthesized at $300MHz$ using Samsung’s $28nm$ and $45nm$ process technologies, demonstrates superior performance in generating FALCON signatures, with a $3.58 \times $ improvement in clock cycles over an existing hardware accelerator. EFX occupies 38K $um ^{2}$ and 74K $um ^{2}$ for $28nm$ and $45nm$ processes, respectively, comparatively small compared to other PQC accelerators.

Soft errors are becoming a critical concern in embedded system designs. Code duplication techniques have been proposed to increase the reliability in multi-issue embedded systems such as VLIW by exploiting empty slots for duplicated instructions. However, they increase code size, another important concern, and ignore vulnerability differences in instructions, causing unnecessary or inefficient protection when selecting instructions to be duplicated under constraints. In this article, we propose a compiler-assisted dynamic code duplication method to minimize the code size overhead, and present vulnerability-aware duplication algorithms to maximize the effectiveness of instruction duplication with least overheads for VLIW architecture. Our experimental results with SoarGen and Synopsys simulation environments demonstrate that our proposals can reduce the code size by up to 40% and detect more soft errors by up to 82% via fault injection experiments over benchmarks from DSPstone and Livermore Loops as compared to the previously proposed instruction duplication technique.

With the increasing proliferation of IoT systems, the security of IoT systems has become very important to individuals and businesses. IoT malware has been increasing exponentially since the emergence of Mirai in 2016. Because the IoT...

In this paper, we introduce the Optimized Epoch Selection Ensemble, a new model designed to address key challenges in the application of deep learning within network security, specifically focusing on the effective selection of ensemble members. Our model combines a custom Convolutional Neural Network (CNN) with a fine-tuned MobileNetV2, targeting the classification of the Malimg dataset. By meticulously selecting the best-performing epochs from the CNN training and pairing them with the top epoch from MobileNetV2, we employ a strategy based on analyzing true predictions to optimize ensemble composition. This approach not only enhances the ensemble’s decision-making process but also significantly boosts accuracy, achieving 99.05% compared to the previous state-of-the-art result of 98.5%. Our results highlight the potential of tailored ensemble methods to significantly improve performance in specialized applications such as network security.

Today, the amount of malware is growing very rapidly, and the types and behaviors of malware are becoming very diverse. Unlike existing malicious codes, new types or variants of malicious codes are being identified, and it takes a lot of...