Industrial Internet of Things (IIoT), or Industry 4.0, is an application of IoT in the industrial sector. Its main objective is to enhance product quality and optimize production costs by leveraging advanced technologies such as edge/fog/cloud computing, 5G/6G, and artificial intelligence. In the context of Industry 4.0, numerous devices and systems are interconnected to provide seamless services to users. However, with this interconnection comes the need to protect these devices and the information they transmit from cyberthreats and intrusions. In order to tackle this challenge, our proposed solution involves the utilization of deep learning (DL) models to develop an anomaly-based detection system. Our approach involves two powerful DL models, namely Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). The proposed model’s performance is studied within binary and multiclass classification using a new real-world industrial traffic dataset called Edge-IIoTset. The outcomes of our experiments showcased the efficacy of the CNN-GRU model that we proposed, surpassing the performance of recent related works in terms of performance metrics, including accuracy, precision, false positive rate, and detection cost. The combination of the two models CNN and GRU outperforms the GRU model with 88% of detection cost in multiclass classification for one traffic flow.
This paper presents an intelligent cyber-physical system framework for detecting and managing type 2 diabetes. The framework leverages the benefits of cloud and federated-edge computing while addressing concerns of security, delay, and communication costs. It enables the seamless integration of knowledge extracted from machine learning (ML) models trained at the edges into a CNN model using distillation and ensemble techniques. To identify the effective model for detecting diabetes, we performed a comparative study using four different ML classifiers: Support Vector Machine (SVM), Artificial Neural Network (ANN), Case-Based Reasoning with Fuzzy K-Nearest Neighbor (CBR-FKNN), and K-means with Logistic Regression (K-Means-LR) at a single edge. We employed various preprocessing techniques and feature selection algorithms to identify the optimal features for the four models. An experimental evaluation was conducted using two diabetes datasets, the PIMA dataset and the Diabetes dataset 2019. The experiments were mainly conducted to assess our proposed framework but not the datasets. The results demonstrated that the SVM model outperforms other models in diabetes detection. The results also showed that the Diabetes dataset 2019 provided better accuracy and F1 scores than the PIMA dataset.
Wormhole attack has exposed-mode (internal attack with exposed attacker nodes identities) and hidden-mode (external attack with hidden attacker nodes identities). In exposed-mode, the pair-wise connected attacker nodes fool the legitimate nodes by using a hidden link to route packets and yield high packet delivery ratio. As the packets reach the wormhole nodes, attackers can initiate traffic analysis, packet dropping, and/or packet modification attacks. This paper studies and analyzes the impact of the exposed-mode of wormhole attack in Opportunistic Mobile Networks (OMNs) and the parameters affecting it. The impacts of the exposed-mode of wormhole attack are analyzed using the amount of extra routed packets the attacker nodes will obtain. The attack was launched by varying different parameters (i.e., number of wormhole nodes, attack frequency, and attack duration) that influence its intensity against four main routing protocols in OMNs (i.e., Prophet, Spray and Wait, Epidemic, and First Contact). The simulation experiments employed two widely-used mobility traces (real-world and synthetic) in OMNs and were analyzed in terms of the most vulnerable routing protocol and the most influential attack parameter. We concluded that attackers can smartly overthrow safe communications in OMNs using deep analysis of routing mechanisms and nodes density.
Traditional methods for video recognition require hand-crafted features, which often involves offline pre-processing for real-world videos. In this study, we propose a conceptually simple framework that directly takes raw videos as an input source for activity recognition. Our framework consists of two streams, namely a spatial stream and a temporal stream. The spatial stream is trained on RepVGG-B0 ConvNet using cropped RGB features, while the temporal stream uses an attention-based Bi-directional Long Short-Term Memory (Bi-LSTM) network to learn posture vectors from human pose data obtained through Faster R-CNN pre-trained model. Our proposed method is evaluated on a standard video action recognition benchmark, MSR Daily Activity3D, and proves to be competitive with state-of-the-art action recognition methods. We achieve state-of-the-art performance on MSR Daily Activity3D with a precision and recall rate of 99.01% and 98.91%, respectively. Our results demonstrate the effectiveness of our approach in recognizing video actions.
As stated by the United Arab Emirates’s (UAE) Community Development Authority (CDA), there are around 3,065 individuals with hearing disabilities in the country. These individuals often struggle to communicate with broader society and rely on scarce sign language (SL) interpreters. Moreover, Arabic’s dialects diversity compounds the issue by causing dialects in the Arabic Sign Language (ArSL). Hence, the call for a standardized reference for ArSL in the region is a priority. To address these challenges, we’ve developed an Emirate Sign Language (ESL) electronic dictionary (e-dictionary) with a dataset of 127 signs and 50 sentences, recorded by hearing-impaired individuals in the UAE with various degrees of deafness. Supervised by certified interpreters and validated by ESL’s department head at CDA in Dubai, the recordings were made using Azure Kinect DK, resulting in 708 recordings. The dataset is then processed to 10fps. The e-dictionary offers features such as webcam-based sign recognition using YOLOv8 technology, voice-based signing via Arabic Automatic Speech Recognition, text-based signing, and words spelling in ArSL.
