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In recent years, ultra-low-voltage (ULV) operation is gaining more importance for achieving minimum energy consumption. Full adder is the basic computational arithmetic block in many of the computing and signal/image processing applications. Here, a new hybrid 1-bit full adder circuit which employs both Gate Diffusion Input (GDI) logic and multi-threshold voltage (MVT) transistor logic is reported. The main objective of the proposed MVT-GDI-based hybrid full adder design is to provide minimum energy consumption with less area. The proposed hybrid design is simulated using standard 45 nm CMOS process technology at an ULV of 0.2 V. The post-layout simulation results have shown that the proposed design achieved significant improvements in comparison with the other reported designs by achieving >57%, 92% savings in the Energy and EDP, respectively, with only 14 transistors. Monte-Carlo simulations have also been performed and is found that the proposed design methodology yields full functionality and robustness against local and global process variations. Normalised energy metrics to 32 and 22 nm technologies shows that the proposed design achieves >57% energy savings in prior to the recent works.

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In recent years, different variants of the botnet are targeting government, private organizations and there is a crucial need to develop a robust framework for securing the IoT (Internet of Things) network. In this paper, a Hadoop based framework is proposed to identify the malicious IoT traffic using a modified Tomek-link under-sampling integrated with automated Hyper-parameter tuning of machine learning classifiers. The novelty of this paper is to utilize a big data platform for benchmark IoT datasets to minimize computational time. The IoT benchmark datasets are loaded in the Hadoop Distributed File System (HDFS) environment. Three machine learning approaches namely naive Bayes (NB), K-nearest neighbor (KNN), and support vector machine (SVM) are used for categorizing IoT traffic. Artificial immune network optimization is deployed during cross-validation to obtain the best classifier parameters. Experimental analysis is performed on the Hadoop platform. The average accuracy of 99% and 90% is obtained for BoT_IoT and ToN_IoT datasets. The accuracy difference in ToN-IoT dataset is due to the huge number of data samples captured at the edge layer and fog layer. However, in BoT-IoT dataset only 5% of the training and test samples from the complete dataset are considered for experimental analysis as released by the dataset developers. The overall accuracy is improved by 19% in comparison with state-of-the-art techniques. The computational times for the huge datasets are reduced by 3–4 hours through Map Reduce in HDFS.

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