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The rapid growth of Internet of Things (IoT) devices has changed human interactions with the environment. IoT networks require specialized defense strategies distinct from traditional corporate contexts. Security measures such as anti-malware software, firewalls, authentication protocols, and encryption techniques are established but face limitations against evolving attack strategies. Therefore, this study proposes an intrusion detection approach for a realistic IoT environment, employing various variants of deep learning models such as deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The research tested three variants for each model: DNN1, DNN2, DNN3, CNN1, CNN2, CNN3, RNN1, RNN2, RNN3. All these variants are customized and tuned differently to analyze the efficacy of the suggested methodology. Likewise, notable observation highlights the significance of aligning training and validation accuracy curves that indicate controlled overfitting, validating the model's reliability in accurately predicting intrusion and benign network traffic in IoT settings. Results reveal that RNN1 achieves the best results: accuracy of 98.61%, precision of 98.55%, recall of 98.61%, and F1-score of 98.57% compared with other DNN and CNN architectures and benchmark papers. This study advances intrusion detection within IoT networks through a comprehensive evaluation of deep learning models and inspires ongoing research to enhance intrusion detection systems' resilience in dynamic IoT environments. [ABSTRACT FROM AUTHOR]

With rapid development and deployment of artificial intelligence methods, the Industrial Internet of Things (IIoT) has highly developed to fast tracing industrial communications and optimizing manufacturing processes. In Industry 5.0, hyper-automation process as one of technological trends navigates industrial entities to intelligent devices of the IIoT, cloud computing, smart robotics, smart agile software and embedded components by high complexity and reliability. By increasing data communication in the IIoT environments and cloud computing, the security and safety of hyper-automation process is also increasingly unstable and challengeable with respect to cyber-attacks, unstructured malwares and abnormal activities. With the diversification and unexpected behaviors of cyber-security threats, traditional cyber-attack detection systems have some critical problems with increasing massive data including unappropriated feature selection and extraction, high computation time in prediction and inaccurate classification models. Due to the above-mentioned challenges, this paper presents a new cloud-based cyber-attack detection architecture based on Ensemble Bagged Trees Detection (EBTD) algorithm for predicting malicious behaviors and types of cyber-attacks for hyper-automation process in the IIoT. The proposed architecture uses Analysis of Variance (ANOVA) and a priority-based feature selection and extraction model to find the optimal features with highly dependent on the network traffic, computation time, malicious behaviors, and types of attacks. Then, experimental results are conducted using the technical data sets UNSW-NB15 and NSL-KDD. The simulation results show that the proposed architecture performance performs better than other case studies and prediction models and effective on optimization of large-scale cyber-attack detection systems for critical hyper-automation process in the IIoT environment. [ABSTRACT FROM AUTHOR]

Securing confidential data against unauthorized users leads to access control policies with the rapid progression of Internet of Things (IoT) devices. Because of high mobility subscribers, the dynamic IoT environment is subjected to high signaling overhead which remains a challenging issue to guarantee data dissemination to legitimate users. The group's key management schemes are the central mechanism to deal with dynamic environments. But they are centralized concepts that cause scalability issues and suffer in handling large numbers of subscribers. Therefore, this paper proposes a modified deep reinforcement oppositional wolf learning-based group key management (MDROWL-GKM) system to monitor the data obtained in IoT properly. It does not maximize the network traffic as well as computational overhead when a group member leaves or joins. With the inclusion of an opposition-based learning gray wolf optimization algorithm, the overload issue of the modified deep reinforcement method is eliminated and the performance is enhanced. The efficacy of the proposed MDROWL-GKM system is investigated using different measures namely storage overhead, computation overhead, access response time, space complexity, re-evaluation time, policy adjustment accuracy, and communication overhead. The experimental analysis proves that the proposed MDROWL-GKM system is superior to other state-of-the-art techniques, particularly with high policy adjustment accuracy (96%), less communication overhead (8 μs) and area under curve (AUC) rate (0.982). [ABSTRACT FROM AUTHOR]

Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are becoming increasingly integrated into all aspects of our lives. They are capable of gathering enormous amounts of data, such as image data, which can then be sent to the cloud for processing. However, this results in an increase in network traffic and latency. To overcome these difficulties, edge computing has been proposed as a paradigm for computing that brings processing closer to the location where data is produced. This paper explores the merging of cloud and edge computing for IoT and investigates approaches using machine learning for dimensionality reduction of images on the edge, employing the autoencoder deep learning-based approach and principal component analysis (PCA). The encoded data is then sent to the cloud server, where it is used directly for any machine learning task without significantly impacting the accuracy of the data processed in the cloud. The proposed approach has been evaluated on an object detection task using a set of 4000 images randomly chosen from three datasets: COCO, human detection, and HDA datasets. Results show that a 77% reduction in data did not have a significant impact on the object detection task's accuracy. [ABSTRACT FROM AUTHOR]

In the cyberspace, device identification has become one of the most important factors in improving security of a network, containing both Internet of Things (IoT) and non-IoT devices. Resource-constraint IoT devices are generally more vulnerable than non-IoT devices, to different kinds of security threats, including Mirai botnet and spoofing attacks. In this paper, a device fingerprinting (DFP) scheme has been proposed based on the analysis of network traffic characteristics. Four statistical features from two device-specific features have been selected using statistical assessment to generate DFP for classification task using a supervised machine learning Random Forest classifier. Experimental results have shown that the proposed DFP scheme is able to classify device type with 99.81% accuracy on the public UNSW dataset, whilst accuracies of 99.50% and 97.10% have been reported for the identification of individual IoT and non-IoT devices, respectively. The proposed DFP scheme has also demonstrated superior performance as compared to other DFP methods in the literature, despite using less number of features and packets for DFP. These signify that the proposed DFP scheme can be used as a network security reinforcement tool in a heterogeneous network environment. [ABSTRACT FROM AUTHOR]