Security of sensitive data exchanged between devices is essential. Low-resource devices (LRDs), designed for constrained environments, are increasingly becoming ubiquitous. Lightweight block ciphers provide confidentiality for LRDs by balancing the required security with minimal resource overhead. SIMON is a lightweight block cipher targeted for hardware implementations. The objective of this research is to implement, optimize, and model SIMON cipher design for LRDs, with an emphasis on energy and power, which are critical metrics for LRDs. Various implementations use field-programmable gate array (FPGA) technology. Two types of design implementations are examined: scalar and pipelined. Results show that scalar implementations require 39% less resources and 45% less power consumption. The pipelined implementations demonstrate 12 times the throughput and consume 31% less energy. Moreover, the most energy-efficient and optimum design is a two-round pipelined implementation, which consumes 31% of the best scalar’s implementation energy. The scalar design that consumes the least energy is a four-round implementation. The scalar design that uses the least area and power is the one-round implementation. Balancing energy and area, the two-round pipelined implementation is optimal for a continuous stream of data. One-round and two-round scalar implementations are recommended for intermittent data applications.

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Information security is essential to ensure security of exchanged sensitive data in resource‐constrained devices (RCDs) because they are used widely in the Internet of things (IoT). The implementation of special ciphers is required in these RCDs, as they have many limitations and constraints, such as low power/energy dissipation, and require low hardware resources. The SM4 cipher is one of the common block ciphers, which can be easily implemented and offers a high level of security. The objective of this study is to determine the optimum field‐programmable gate array (FPGA) design for SM4 to facilitate reconfiguring the FPGA with an optimum design during operation. Various FPGA design options for SM4 ciphers are examined, and the performance metrics are modeled: power, energy, area, and speed. Scalar and pipelined designs with one or multiple hardware rounds are considered without altering the cipher algorithm. The results show that the best scalar implementation utilises less resources than the pipelined implementations by 7%. Alternatively, pipelined implementations perform better regarding speed and energy dissipation by 10 times and 40% of the scalar implementation, respectively. The pipeline implementations with eight or 16 rounds are optimum for continuous streams of data, and the two‐round design is the optimum design across ciphers.

Recent advances in machine learning have shown promising results for detecting network intrusion through supervised machine learning. However, such techniques are ineffective for new types of attacks. In the preferred unsupervised and semi-supervised cases, these newer techniques suffer from lower accuracy and higher rates of false alarms. This work proposes a machine learning model that combines auto-encoder with one-class support vectors machine. In this model, the auto-encoders learn the representation of the input data in a latent space and reduces the dimensionality of the input data. The dimensionality-reduced input is then extracted from the auto-encoder and passed to a one-class support vectors machine to classify the network event as an attack or a normal event. The model is trained on normal network events only. The proposed model is then evaluated and compared with several existing models. It achieves high accuracy when tested on the NSL-KDD and KDD99 datasets, with total accuracies of 96.24% and 99.45%, respectively.