Personal authentication security is an essential area of research in privacy and cybersecurity. For individual verification, fingerprint and facial recognition have proved particularly useful. However, such technologies have flaws such as fingerprint fabrication and external impediments. Different AI-based technologies have been proposed to overcome forging or impersonating authentication concerns. Electrocardiogram (ECG)-based user authentication has recently attracted considerable curiosity from researchers. The Electrocardiogram is among the most reliable advanced techniques for authentication since, unlike other biometrics, it confirms that the individual is real and alive. This study utilizes a user authentication system based on electrocardiography (ECG) signals using deep learning algorithms. The ECG data are collected from users to create a unique biometric profile for each individual. The proposed methodology utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to analyze the ECG data. The CNNs are trained to extract features from the ECG data, while the LSTM networks are used to model the temporal dependencies in the data. The evaluation of the performance of the proposed system is conducted through experiments. It demonstrates that it effectively identifies users based on their ECG data, achieving high accuracy rates. The suggested techniques obtained an overall accuracy of 98.34% for CNN and 99.69% for LSTM using the Physikalisch–Technische Bundesanstalt (PTB) database. Overall, the proposed system offers a secure and convenient method for user authentication using ECG data and deep learning algorithms. The approach has the potential to provide a secure and convenient method for user authentication in various applications.

This study investigates the combination of image processing and supervised classification to identify invasive yellow flag iris (YFI; Iris pseudacorus) plants in images collected by an un-calibrated, visible-light camera carried aloft by an unmanned aerial vehicle. Specifically, the image-processing steps of colour thresholding, template matching, and/or de-speckling prior to training a supervised random forest classifier are explored in terms of their benefits towards improving the resulting classification of YFI plants within an image. The impacts of performing feature selection prior to training the random forest classifier are also explored. This analysis demonstrates the importance of image processing when preparing images for classification and reveals that applying the image-processing steps of colour thresholding and de-speckling prior to classification by a random forest classifier trained to identify patches of YFI plants using spectral and textural features provided the best results.

The physical layer of cyber-physical systems (CPSs) is composed of resource-constrained devices connected in a wireless sensor network (WSN). Although this layer is easy to deploy, in most cases, it has many security issues. Several intrusion detection systems (IDSs) have been proposed and tested as effective and efficient solutions to detect only a few known attacks. In this article, we propose a novel, Supervised machine learning-based IDS that is capable of detecting several attacks. This article discusses all IDS design steps, starting from data collection to the feature engineering analysis and building the trained models. Experimental results show that the proposed IDS can detect four different types of attacks that were seen by the machine learning models during the training phase. The IDS can also detect the existence of several other attacks that are not seen by the model and classify them as unknown attack types. The proposed model achieves 99.97% classification accuracy when detecting known attacks and 85% classification accuracy when detecting a new attack type.