This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks’ security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have recently gained increased popularity due to their generation’s ability to detect unseen attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. A high-performance and ultra-low power consumption anomaly-based IDS framework is proposed and evaluated in this paper. The framework has achieved the highest accuracy of 98.57% and 99.66% on the UNSW-NB15 and IoT-23 datasets, respectively. The inference engine on the MAX78000EVKIT AI-microcontroller is 11.3 times faster than the Intel Core i7-9750H 2.6 GHz and 21.3 times faster than NVIDIA GeForce GTX 1650 graphics cards, when the power drawn was 18mW. In addition, the pipelined design on the PYNQ-Z2 SoC FPGA board with the Xilinx Zynq xc7z020-1clg400c device is optimised to run at the on-chip frequency (100 MHz), which shows a speedup of 53.5 times compared to the MAX78000EVKIT.

In this work, waste C. grandis peel was the ingredient used to extract essential oil and naringin by a supercritical CO2 technique. Both natural products were evaluated for antimicrobial activity and docking studies.

With the rising data evolution, the demand for secured communications over networks is rising immensely. Elliptic Curve Cryptography (ECC) provides an attractive solution to fulfill the requirements of modern network applications. Many proposals published over the year over different variants of ECC satisfied some of the issues. Nevertheless, modern network applications such as Internet-of-Thing (IoT) and Software-Defined Networking (SDN) put the requirements on various aspects and can only be solved by different ECC algorithms. Looking at this point of view, an efficient architecture that could combine multiple ECC algorithms becomes an urgent request. In addition, even though many investigations of ECC on Field-Programmable Gate Arrays (FPGA), an efficient architecture that could be well-deployed on Application-Specific Integrated Circuit (ASIC) needs to get more study. Therefore, this paper proposes an area-efficient ECC hardware design that could integrate multiple ECC algorithms. The proposed design is deployed on both ASIC and FPGA platforms. Four well-known ECC-based Digital Signature Algorithms (DSAs), which are the Edwards-curve Digital Signature Algorithm (EdDSA) with Curve25519, Elliptic Curve Digital Signature Algorithm (ECDSA) with National Institute of Standards and Technology (NIST) Curve P-256, P-384, and P-521, are implemented. Furthermore, the design supports all DSA schemes: public-key generation, signature generation, and verification. We also provide optimized calculation flows for modular multiplication, modular inversion, point addition, point doubling, and Elliptic Curve Point Multiplication (ECPM) for two different elliptic curves: the NIST curve and Edward curve, on a unique architecture. The calculation processes are designed in projective coordinates and optimized in time and space to achieve a high level of parallelism. The proposed ECC processor could run up to 102-MHz on ASIC 180-nm and 109.7-MHz on Xilinx Virtex-7. In terms of area, The processor occupies 377,471 gates with 4.87-mm 2 on the ASIC platform and 11,401 slices on the FPGA platform. The experimental results show that our combinational design achieves area-efficient even when compared with other single-functional architecture on both ASIC and FPGA.