In this study, copper nanoparticles (CuNPs) were synthesized by sonochemical reduction of copper(II) hydrazine carboxylate complex in an aqueous medium. Asymmetric neat polyethersulfone (PES) and PES/CuNPs nanocomposite membranes were prepared via phase inversion method by dispersing copper nanoparticles in the PES casting solutions. X-ray diffraction and particle size distribution analyses were applied to characterize the CuNPs. Moreover, field emission scanning electron microscopy, atomic force microscopy (AFM), conductive atomic force microscopy (C-AFM), contact angle goniometry, and four-point probe techniques were used to investigate the physical and morphological properties of the PES/CuNPs nanocomposite membranes. Synthesized CuNPs were added into the casting solution at different concentrations. The effect of CuNPs concentration (0, 0.25, 0.5, 1.0, 2.0%, w/w) was tested on morphological changes and antibacterial properties of the prepared membranes. These copper nanoparticle composite membranes hold great promise in the engineering field for the production of sensors and self-cleaning membranes.
Intrusion detection systems play a vital role in traffic flow monitoring on Internet of Things networks by providing a secure network traffic environment and blocking unwanted traffic packets. Various intrusion detection systems approaches have been proposed previously based on data mining, fuzzy techniques, genetic, neurogenetic, particle swarm intelligence, rough sets, and conventional machine learning. However, these methods are not energy efficient and do not perform accurately due to the inappropriate feature selection or the use of full features of datasets. In general, datasets contain more than 10 features. Any machine learning–based lightweight intrusion detection systems trained with full features turn into an inefficient and heavyweight intrusion detection systems. This case challenges Internet of Things networks that suffer from power efficiency problems. Therefore, lightweight (energy-efficient), accurate, and high-performance intrusion detection systems are paramount instead of inefficient and heavyweight intrusion detection systems. To address these challenges, a new approach that can help to determine the most effective and optimal feature pairs of datasets which enable the development of lightweight intrusion detection systems was proposed. For this purpose, 10 machine learning algorithms and the recent BoT-IoT (2018) dataset were selected. Twelve best features recommended by the developers of this dataset were used in this study. Sixty-six unique feature pairs were generated from the 12 best features. Next, 10 full-feature-based intrusion detection systems were developed by training the 10 machine learning algorithms with the 12 full features. Similarly, 660 feature-pair-based lightweight intrusion detection systems were developed by training the 10 machine learning algorithms via each feature pair out of the 66 feature pairs. Moreover, the 10 intrusion detection systems trained with 12 best features and the 660 intrusion detection systems trained via 66 feature pairs were compared to each other based on the machine learning algorithmic groups. Then, the feature-pair-based lightweight intrusion detection systems that achieved the accuracy level of the 10 full-feature-based intrusion detection systems were selected. This way, the optimal and efficient feature pairs and the lightweight intrusion detection systems were determined. The most lightweight intrusion detection systems achieved more than 90% detection accuracy.
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