Search

The Internet of Things (IoT) is an important virtual network that allows remote users to access linked multimedia devices. The development of IoT and its ubiquitous application across various domains of everyday life has led to continuous research efforts. Security is a perceptual concern for researchers involved in IoT as it is a key factor in the acceptance of any innovative technology. Numerous research studies have been conducted concentrating on the level of IoT security on a particular mechanism, on specific applications, or on categorizing vulnerabilities, in order to address a defined situation of securing an IoT network. This present paper aims to comprehensively review potential solutions for securing IoT, between emerging and traditional mechanisms, such as blockchain, machine learning, cryptography, and quantum computing. This study provides a comparative analysis of related papers with their characteristics, pros and cons. Accordingly, it taxonomizes relevant solutions based on their achieved security requirements. Furthermore, the potential benefits and challenges of each of the four mechanisms are discussed. [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]

Federated learning is a distributed machine learning approach that enables a large number of edge/end devices to perform on-device training for a single machine learning model, without having to share their own raw data. We consider in this paper a federated learning scenario wherein the local training is carried out on IoT devices and the global aggregation is done at the level of an edge server. One essential challenge in this emerging approach is IoT devices selection (also called scheduling), i.e., how to select the IoT devices to participate in the distributed training process. The existing approaches suggest to base the scheduling decision on the resource characteristics of the devices to guarantee that the selected devices would have enough resources to carry out the training. In this work, we argue that trust should be an integral part of the decision-making process and therefore design a trust establishment mechanism between the edge server and IoT devices. The trust mechanism aims to detect those IoT devices that over-utilize or under-utilize their resources during the local training. Thereafter, we introduce DDQN-Trust, a double deep Q learning-based selection algorithm that takes into account the trust scores and energy levels of the IoT devices to make appropriate scheduling decisions. Finally, we integrate our solution into four federated learning aggregation approaches, namely, FedAvg, FedProx, FedShare and FedSGD. Experiments conducted using a real-world dataset show that our DDQN-Trust solution always achieves better performance compared to two main benchmarks: the DQN and random scheduling algorithms. The results also reveal that FedProx outperforms the competitor aggregation models in terms of accuracy when integrated into our DDQN-Trust solution. [ABSTRACT FROM AUTHOR]

Today we are living in an era where everything is changing to be smart, whether it be a smart home, smart industries, smart irrigation, or a smart meter, where the word smart refers to the involvement of the Internet of Things (IoT). The increased use of IoT infrastructure in these fields has led to the failure of the nodes, increase in threats, attacks, abnormalities, and spying, which is the primary concern and an important domain of an IoT. The main objective of this paper is to use a supervised learning model to predict anomalies in the historical data which can later be incorporated into real-world scenarios to block the upcoming anomalies and attacks. This paper predicts the anomalies on the 350 K data set using the Machine Learning models and compares its performance based on the state of arts. In this paper, two different approaches are used based on the analysis done on the dataset. The classification algorithms were applied to the whole dataset in the first, and then the same classification algorithms were applied after excluding the data points having binary values (0 and 1) in the feature "value" and have achieved an average of 99.4% accuracy for the first case and 99.99% accuracy for the later. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves 99.5 % detection and 0.5 % false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks. [ABSTRACT FROM AUTHOR]

Thanks to the continuous development of the tourism industry, the application of Internet of things (IoT) begins to boom among modern tourists. Smart tourism utilizes Internet of things to facilitate the information dissemination and exchange, making IoT technology an indispensable part of next-generation travelling tool kits. In this paper, based on visitor selection behavior, we develop a hybrid intelligent categorization approach. Particularly, our proposed method of categorization could help tourists decide whether or not to visit a sightseeing attraction in a minutes. Cross-validation testing and performance assessment show that our designed platform can work effectively and efficiently by considering a variety of factors. [ABSTRACT FROM AUTHOR]

With simple connectivity and fast-growing demand of smart devices and networks, IoT has become more prone to cyber attacks. In order to detect and prevent cyber attacks in IoT networks, intrusion detection system (IDS) plays a crucial role. However, most of the existing IDS have dimensionality curse that reduces overall IoT systems efficiency. Hence, it is important to remove repetitive and irrelevant features while designing effective IDS. Motivated from aforementioned challenges, this paper presents an intelligent cyber attack detection system for IoT network using a novel hybrid feature reduced approach. This technique first performs feature ranking using correlation coefficient, random forest mean decrease accuracy and gain ratio to obtain three different feature sets. Then, features are combined using a suitably designed mechanism (AND operation), to obtain single optimized feature set. Finally, the obtained reduced feature set is fed to three well-known machine learning algorithms such as random forest, K-nearest neighbor and XGBoost for detection of cyber attacks. The efficiency of the proposed cyber attack detection framework is evaluated using NSL-KDD and two latest IoT-based datasets namely, BoT-IoT and DS2OS. Performance of the proposed framework is evaluated and compared with some recent state-of-the-art techniques found in literature, in terms of accuracy, detection rate (DR), precision and F1 score. Performance analysis using these three datasets shows that the proposed model has achieved DR up to 90%–100%, for most of the attack vectors that has close similarity to normal behaviors and accuracy above 99%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and devices able to be connected to the Internet. Current and future research and development efforts aim at adding artificial intelligence to IoT systems, enabling devices to become smart and thus make autonomous decisions individually or collectively. Additionally, such smart devices have the ability to interact not only with other smart devices but also with humans. Thus, the aim of this paper is to investigate the usability of the artificial intelligence in the IoT paradigm. To achieve the approach, a system called smart-IoT is built based on artificial neural networks, namely, neural networks have been learned by back-propagation algorithm. The system is tested using mobile devices under Android as smart objects. Experiments with neural networks were carried on certain services (such as auto set alarms for a specific event, or estimating the time to return home). These experiments showed the feasibility of embedding neural networks techniques into the IoT system. The approach allows also for easy adding of new services, which in turn means that smart IoT is a modular and full-fledged system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new Internet of Things (IoT) based predictive modelling by using fuzzy cluster based augmentation and classification for predicting the lung cancer disease through continuous monitoring and also to improve the healthcare by providing medical instructions. Here, the fuzzy clustering method is used and which is based on transition region extraction for effective image segmentation. Moreover, Fuzzy C-Means Clustering algorithm is used to categorize the transitional region features from the feature of lung cancer image. In this work, Otsu thresholding method is used for extracting the transition region from lung cancer image. Moreover, the right edge image and the morphological thinning operation are used for enhancing the performance of segmentation. In addition, the morphological cleaning and the image region filling operations are performed over an edge lung cancer image for getting the object regions. In addition, we also propose a new incremental classification algorithm which combines the existing Association Rule Mining (ARM), the standard Decision Tree (DT) with temporal features and the CNN. The experiments have been conducted by using the standard images that are collected from database and the current health data which are collected from patient through IoT devices. The results proved that the performance of the proposed prediction model which is able to achieve the better accuracy when it is compared with other existing prediction model. [ABSTRACT FROM AUTHOR]