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The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency. [ABSTRACT FROM AUTHOR]

With a great percentage of farms in NewZealand as pastures, they aremainly important in contributing to the milk and meat industries. Pasture quality is highly affected by weeds.Weeds grow fast and invade pastures by seed pollination. They consume the nutrients,water, and otherminerals, and once they are bitter, cattle do not eat them. Therefore, dairy farmers have to allocate a significant portion of their budget and time to monitor and clean weeds. Unfortunately, most weed management tasks are manual with no consistent technology. Thus, the motivation behind this article was to design an object detection model for weed monitoring and control in pastures. The model was designed and tested on California thistle, a dominant and widespread weed on New Zealand pastures. Our study is one of the major model designs for identifying weeds in an inpasture environment, one of the most complicated environments for any object detection model. A synthetic methodology was used to create three types of datasets: plant-based, leaf-based, and mixed. The trainedmodel based on the leaf-based dataset is one of the major contributions of our work and has not been conducted by any other weed detection models. After models had been trained, tuning experimentation was undertaken to improve the model's performance. This involved studying the model's hyperparameters in various ranges and then recording their values at the optimum points. The improved model showed a 93% mAP accuracy in the detection of training images and over 95% accuracy for testing images. The experimentation showed that the leaf-based model was slightly better than other models. The model can automate highly any weed management system. The use of this model will save farmers time and money and help them reduce the errors of manual work. [ABSTRACT FROM AUTHOR]