The growth of wireless networks has been remarkable in the last few years. One of the main reasons for this growth is the massive use of portable and stand-alone devices with wireless network connectivity. These devices have become essential on the daily basis in consumer electronics. As the dependency on wireless networks has increased, the attacks against them over time have increased as well. To detect these attacks, a network intrusion detection system (NIDS) with high accuracy and low detection time is needed. In this work, we propose a machine learning (ML) based wireless network intrusion detection system (WNIDS) for Wi-Fi networks to efficiently detect attacks against them. The proposed WNIDS consists of two stages that work together in a sequence. An ML model is developed for each stage to classify the network records into normal or one of the specific attack classes. We train and validate the ML model for WNIDS using the publicly available Aegean Wi-Fi Intrusion Dataset (AWID). Several feature selection techniques have been considered to identify the best features set for the WNIDS. Our two-stage WNIDS achieves an accuracy of 99.42% for multi-class classification with a reduced set of features. A module for eXplainable Artificial Intelligence (XAI) is implemented as well to understand the influence of features on each type of network traffic records.

Deep learning (DL) algorithms have achieved significantly high performance in object detection tasks. At the same time, augmented reality (AR) techniques are transforming the ways that we work and connect with people. With the increasing popularity of online and hybrid learning, we propose a new framework for improving students’ learning experiences with electrical engineering lab equipment by incorporating the abovementioned technologies. The DL powered automatic object detection component integrated into the AR application is designed to recognize equipment such as multimeter, oscilloscope, wave generator, and power supply. A deep neural network model, namely MobileNet-SSD v2, is implemented for equipment detection using TensorFlow’s object detection API. When a piece of equipment is detected, the corresponding AR-based tutorial will be displayed on the screen. The mean average precision (mAP) of the developed equipment detection model is $81.4\%$, while the average recall of the model is $85.3\%$. Furthermore, to demonstrate practical application of the proposed framework, we develop a multimeter tutorial where virtual models are superimposed on real multimeters. The tutorial includes images and web links as well to help users learn more effectively. The Unity3D game engine is used as the primary development tool for this tutorial to integrate DL and AR frameworks and create immersive scenarios. The proposed framework can be a useful foundation for AR and machine-learning-based frameworks for industrial and educational training.

Remote Keyless Entry (RKE) systems are essential in modern lifestyle with applications including vehicle and garage entry. However, the convenience such systems facilitate comes at the expense of their vulnerability to different attacks such as jamming, replay, jamming-and-replay, and cryptanalysis. These attacks, if lightly taken, may lead to catastrophic consequences to the general public, such as property theft and human casualties. To this end, an improved RKE system using timestamping and XOR encoding is proposed to countermeasure the aforementioned attacks. In addition, the integrated defense mechanism protects the authentication code in scenarios where the encrypted transmitter-receiver communications is compromised. The defense mechanism in the proposed RKE is demonstrated and tested in realistic setups, and is found to outperform the conventional RKE.

Wi-Fi networks are ubiquitous in both home and enterprise environments, serving as a primary medium for Internet access and forming the backbone of modern IoT ecosystems. However, their inherent vulnerabilities, combined with widespread adoption, create opportunities for malicious actors to gain unauthorized access or compromise sensitive data stored on connected devices. To address these challenges, we propose a deep learning based network intrusion detection system (NIDS) for Wi-Fi environments. Building on our previous work, we convert network traffic into two-dimensional data representations and use them to train DL models based on convolutional neural network (CNN) architectures. We implement five distinct techniques for generating the two-dimensional representations, and to ensure low detection latency, we adopt lightweight CNN architectures in our NIDS. The models are trained using the AWID3 dataset, a publicly available benchmark for Wi-Fi NIDS research, and are evaluated for both binary and multi-class classification tasks. Experimental results demonstrate that the proposed approach achieves competitive detection performance with low inference time, making it suitable for real-world Wi-Fi deployment scenarios.