Secure Multifactor Remote Access User Authentication Framework for IoT Networks

The term IoT refers to the interconnection and exchange of data among devices/sensors. IoT devices are often small, low cost, and have limited resources. The IoT issues and challenges are growing increasingly. Security and privacy issues are among the most important concerns in IoT applications, such as smart buildings. Remote cybersecurity attacks are the attacks which do not require physical access to the IoT networks, where the attacker can remotely access and communicate with the IoT devices through a wireless communication channel. Thus, remote cybersecurity attacks are a significant threat. Emerging applications in smart environments such as smart buildings require remote access for both users and resources. Since the user/building communication channel is insecure, a lightweight and secure authentication protocol is required. In this paper, we propose a new secure remote user mutual authentication protocol based on transitory identities and multi-factor authentication for IoT smart building environment. The protocol ensures that only legitimate users can authenticate with smart building controllers in an anonymous, unlinkable, and untraceable manner. The protocol also avoids clock synchronization problem and can resist quantum computing attacks. The security of the protocol is evaluated using two different methods: (1) informal analysis; (2) model check using the automated validation of internet security protocols and applications (AVISPA) toolkit. The communication overhead and computational cost of the proposed are analyzed. The security and performance analysis show that our protocol is secure and efficient.     

Cogent and Energy Efficient Authentication Protocol for WSN in IoT

Given the accelerating development of Internet of things (IoT), a secure and robust authentication mechanism is urgently required as a critical architectural component. The IoT has improved the quality of everyday life for numerous people in many ways. Owing to the predominantly wireless nature of the IoT, connected devices are more vulnerable to security threats compared to wired networks. User authentication is thus of utmost importance in terms of security on the IoT. Several authentication protocols have been proposed in recent years, but most prior schemes do not provide sufficient security for these wireless networks. To overcome the limitations of previous schemes, we propose an efficient and lightweight authentication scheme called the Cogent Biometric-Based Authentication Scheme (COBBAS). The proposed scheme is based on biometric data, and uses lightweight operations to enhance the efficiency of the network in terms of time, storage, and battery consumption. A formal security analysis of COBBAS using Burrows–Abadi–Needham logic proves that the proposed protocol provides secure mutual authentication. Formal security verification using the Automated Validation of Internet Security Protocols and Applications tool shows that the proposed protocol is safe against man-in-the-middle and replay attacks. Informal security analysis further shows that COBBAS protects wireless sensor networks against several security attacks such as password guessing, impersonation, stolen verifier attacks, denial-of-service attacks, and errors in biometric recognition. This protocol also provides user anonymity, confidentiality, integrity, and biometric recovery in acceptable time with reasonable computational cost.     

Anomaly Detection for Internet of Things Cyberattacks

The Internet of Things (IoT) has been deployed in diverse critical sectors with the aim of improving quality of service and facilitating human lives. The IoT revolution has redefined digital services in different domains by improving efficiency, productivity, and cost-effectiveness. Many service providers have adapted IoT systems or plan to integrate them as integral parts of their systems’ operation; however, IoT security issues remain a significant challenge. To minimize the risk of cyberattacks on IoT networks, anomaly detection based on machine learning can be an effective security solution to overcome a wide range of IoT cyberattacks. Although various detection techniques have been proposed in the literature, existing detection methods address limited cyberattacks and utilize outdated datasets for evaluations. In this paper, we propose an intelligent, effective, and lightweight detection approach to detect several IoT attacks. Our proposed model includes a collaborative feature selection method that selects the best distinctive features and eliminates unnecessary features to build an effective and efficient detection model. In the detection phase, we also proposed an ensemble of learning techniques to improve classification for predicting several different types of IoT attacks. The experimental results show that our proposed method can effectively and efficiently predict several IoT attacks with a higher accuracy rate of 99.984%, a precision rate of 99.982%, a recall rate of 99.984%, and an F1-score of 99.983%.     

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.     

A Novel Ensemble Learning Algorithm Based on D-S Evidence Theory for IoT Security

In the last decade, IoT has been widely used in smart cities, autonomous driving and Industry 4.0, which lead to improve efficiency, reliability, security and economic benefits. However, with the rapid development of new technologies, such as cognitive communication, cloud computing, quantum computing and big data, the IoT security is being confronted with a series of new threats and challenges. IoT device identification via Radio Frequency Fingerprinting (RFF) extracting from radio signals is a physical-layer method for IoT security. In physical-layer, RFF is a unique characteristic of IoT device themselves, which can difficultly be tampered. Just as people’s unique fingerprinting, different IoT devices exhibit different RFF which can be used for identification and authentication. In this paper, the structure of IoT device identification is proposed, the key technologies such as signal detection, RFF extraction, and classification model is discussed. Especially, based on the random forest and Dempster-Shafer evidence algorithm, a novel ensemble learning algorithm is proposed. Through theoretical modeling and experimental verification, the reliability and differentiability of RFF are extracted and verified, the classification result is shown under the real IoT device environments.     

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

The Internet of Things (IoT) is a modern approach that enables connection with a wide variety of devices remotely. Due to the resource constraints and open nature of IoT nodes, the routing protocol for low power and lossy (RPL) networks may be vulnerable to several routing attacks. That’s why a network intrusion detection system (NIDS) is needed to guard against routing assaults on RPL-based IoT networks. The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks. Therefore, we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique (LSH-SMOTE). The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms. To prove the effectiveness of the proposed approach, a set of experiments were conducted to evaluate the performance of NIDS for three cases, namely, detection without dataset balancing, detection with SMOTE balancing, and detection with the proposed optimized LSH-SOMTE balancing. Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy. In addition, a statistical analysis is performed to study the significance and stability of the proposed approach. The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset. Based on the proposed approach, the achieved accuracy is (98.1%), sensitivity is (97.8%), and specificity is (98.8%).     

M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT

In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture known as a Multi-class Classification based Intrusion Detection Model (M-IDM), which typically relies on data collected by real devices and the use of convolutional neural networks (i.e., it exhibits better performance compared with conventional machine learning algorithms, such as naïve Bayes, support vector machine (SVM)). Unlike existing studies, the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices, such as a patient’s monitors (i.e., electrocardiogram and thermometers). The proposed architecture classifies the data into multiple classes: Critical, informal, major, and minor, for intrusion detection. Further, we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms, including naïve Bayes, SVM, and logistic regression, using neural networks.     

An Intelligent Hybrid Mutual Authentication Scheme for Industrial Internet of Thing Networks

Internet of Things (IoT) network used for industrial management is vulnerable to different security threats due to its unstructured deployment, and dynamic communication behavior. In literature various mechanisms addressed the security issue of Industrial IoT networks, but proper maintenance of the performance reliability is among the common challenges. In this paper, we proposed an intelligent mutual authentication scheme leveraging authentication aware node (AAN) and base station (BS) to identify routing attacks in Industrial IoT networks. The AAN and BS uses the communication parameter such as a route request (RREQ), node-ID, received signal strength (RSS), and round-trip time (RTT) information to identify malicious devices and routes in the deployed network. The feasibility of the proposed model is validated in the simulation environment, where OMNeT++ was used as a simulation tool. We compare the results of the proposed model with existing field-proven schemes in terms of routing attacks detection, communication cost, latency, computational cost, and throughput. The results show that our proposed scheme surpasses the previous schemes regarding these performance parameters with the attack detection rate of 97.7 %.     

Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so far by conventional threat detection techniques. The current research article develops a Hybrid Deep Learning to Combat Sophisticated False Data Injection Attacks detection (HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD model majorly recognizes the presence of FDI attacks in the IoT environment. The HDL-FDIAD model exploits the Equilibrium Optimizer-based Feature Selection (EO-FS) technique to select the optimal subset of the features. Moreover, the Long Short Term Memory with Recurrent Neural Network (LSTM-RNN) model is also utilized for the purpose of classification. At last, the Bayesian Optimization (BO) algorithm is employed as a hyperparameter optimizer in this study. To validate the enhanced performance of the HDL-FDIAD model, a wide range of simulations was conducted, and the results were investigated in detail. A comparative study was conducted between the proposed model and the existing models. The outcomes revealed that the proposed HDL-FDIAD model is superior to other models.     

Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning

Nowadays, the amount of wed data is increasing at a rapid speed, which presents a serious challenge to the web monitoring. Text sentiment analysis, an important research topic in the area of natural language processing, is a crucial task in the web monitoring area. The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data. Deep learning is a hot research topic of the artificial intelligence in the recent years. By now, several research groups have studied the sentiment analysis of English texts using deep learning methods. In contrary, relatively few works have so far considered the Chinese text sentiment analysis toward this direction. In this paper, a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network (CNN) in deep learning in order to improve the analysis accuracy. The feature values of the CNN after the training process are nonuniformly distributed. In order to overcome this problem, a method for normalizing the feature values is proposed. Moreover, the dimensions of the text features are optimized through simulations. Finally, a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances. Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods, e.g., the support vector machine method.     

基于深度学习的双耳声源定位算法研究

针对多种定位因素存在复杂关联且不易准确提取的问题,提出了以完整双耳声信号作为输入的、基于深度学习的双耳声源定位算法。首先,分别采用深层全连接后向传播神经网络(Deep Back Propagation Neural Network,D-BPNN)和卷积神经网络(Convolutional Neural Network, CNN)实现深度学习框架;然后,分别以水平面 15°、30°和 45°空间角度间隔的双耳声信号进行模型训练;最后,采用前后混乱率、定位准确率与训练时长等指标进行算法有效性分析。模型预测结果表明,CNN模型的前后混乱率远低于 D-BPNN;D-BPNN模型的定位准确率能够达到87%以上,而 CNN模型的定位准确率能够达到 98%左右;在相同实验条件下,CNN模型的训练时长大于 D-BPNN,且随着水平面角度间隔的减小,两者训练时长之间的差异愈发显著。     

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after five-fold cross validation.     

Pedestrian Physical Education Training Over Visualization Tool

E-learning approaches are one of the most important learning platforms for the learner through electronic equipment. Such study techniques are useful for other groups of learners such as the crowd, pedestrian, sports, transports, communication, emergency services, management systems and education sectors. E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and methods. Many of them are currently working on this domain to fulfill the requirements of industry and the environment. In this paper, we proposed a method for pedestrian behavior mining of aerial data, using deep flow feature, graph mining technique, and convocational neural network. For input data, the state-of-the-art crowd activity University of Minnesota (UMN) dataset is adopted, which contains the aerial indoor and outdoor view of the pedestrian, for simplification of extra information and computational cost reduction the pre-processing is applied. Deep flow features are extracted to find more accurate information. Furthermore, to deal with repetition in features data and features mining the graph mining algorithm is applied, while Convolution Neural Network (CNN) is applied for pedestrian behavior mining. The proposed method shows 84.50% of mean accuracy and a 15.50% of error rate. Therefore, the achieved results show more accuracy as compared to state-of-the-art classification algorithms such as decision tree, artificial neural network (ANN).     

基于多传感器信号和卷积神经网络的滚动轴承故障诊断

针对滚动轴承故障信号非平稳非线性且易受背景噪声干扰的特点,结合深度学习的优势,提出了一种基于卷积神经网络(CNN)的滚动轴承故障诊断法。将不同故障下多个传感器测得的1维(1D)振动信号转化为2维(2D)灰度图像作为网络输入,并将其分为训练集和测试集;将训练集输入卷积神经网络进行训练,自动提取其中的特征;测试集被用于验证学习完毕的网络的有效性,实现滚动轴承故障识别。该方法不依赖于人为经验和信号处理技术进行预先的信号特征提取,实验数据分析表明,相比于经典的支持向量机和概率神经网络方法,提出的方法识别准确率更高且更稳定。     

基于自适应深度卷积神经网络的发射车滚动轴承故障诊断研究

作为发射车的关键组成部件,滚动轴承的工作环境复杂,故障诊断困难。提出一种自适应深度卷积神经网络,针对传统CNN诊断方法存在的计算效率较低、参数调试需人工经验指导等问题,采用粒子群优化算法确定CNN模型结构和参数,应用主成分分析法将故障诊断特征学习过程可视化,评估其特征学习能力。将提出方法应用于发射车滚动轴承故障诊断,对比标准CNN、SVM、ANN诊断方法,10种工况的诊断结果表明,提出方法诊断精度高且鲁棒性好。     

Accurate Multi-Scale Feature Fusion CNN for Time Series Classification in Smart Factory

Time series classification (TSC) has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the process of building a smart factory. However, it is still challenging for the efficiency and accuracy of classification due to complexity, multi-dimension of time series. This paper presents a new approach for time series classification based on convolutional neural networks (CNN). The proposed method contains three parts: short-time gap feature extraction, multi-scale local feature learning, and global feature learning. In the process of short-time gap feature extraction, large kernel filters are employed to extract the features within the short-time gap from the raw time series. Then, a multi-scale feature extraction technique is applied in the process of multi-scale local feature learning to obtain detailed representations. The global convolution operation with giant stride is to obtain a robust and global feature representation. The comprehension features used for classifying are a fusion of short time gap feature representations, local multi-scale feature representations, and global feature representations. To test the efficiency of the proposed method named multi-scale feature fusion convolutional neural networks (MSFFCNN), we designed, trained MSFFCNN on some public sensors, device, and simulated control time series data sets. The comparative studies indicate our proposed MSFFCNN outperforms other alternatives, and we also provided a detailed analysis of the proposed MSFFCNN.     

Robust Attack Detection Approach for IIoT Using Ensemble Classifier

Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition, outlier analysis, and speech recognition. Traditional techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of upgradation. In this paper, the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network. In the first phase, SVM and Naïve Bayes, are integrated using an ensemble blending technique. K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test sets. Ensemble blending uses a random forest technique to predict class labels. An Artificial Neural Network (ANN) classifier that uses the Adam optimizer to achieve better accuracy is also used for prediction. In the second phase, both the ANN and random forest results are fed to the model’s classification unit, and the highest accuracy value is considered the final result. The proposed model is tested on standard IoT attack datasets, such as WUSTL_IIOT-2018, N_BaIoT, and Bot_IoT. The highest accuracy obtained is 99%. A comparative analysis of the proposed model using state-of-the-art ensemble techniques is performed to demonstrate the superiority of the results. The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.     

SIMAD: Secure Intelligent Method for IoT-Fog Environments Attacks Detection

The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress. Nevertheless, alongside this advancement, IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments. In order to overcome these security challenges, the fog system has delivered a powerful environment that provides additional resources for a more improved data security. However, because of the emerging of various breaches, several attacks are ceaselessly emerging in IoT and Fog environment. Consequently, the new emerging applications in IoT-Fog environment still require novel, distributed, and intelligent security models, controls, and decisions. In addition, the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems. This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks. In fact, to address the above problems, Artificial Intelligence (AI) methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and fog systems. In this respect, this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoT-Fog environments. We evaluated the performance of our distributed approach with the application of certain machine learning mechanisms. The experiments show that the proposed scheme, applied with the Random Forest (RF) is more efficient and provides better accuracy (99.50%), better scalability, and lower false alert rates. In this regard, the distribution character of our method brings about faster detection and better learning.     

An End-to-End Authentication Scheme for Healthcare IoT Systems Using WMSN

The healthcare internet of things (IoT) system has dramatically reshaped this important industry sector. This system employs the latest technology of IoT and wireless medical sensor networks to support the reliable connection of patients and healthcare providers. The goal is the remote monitoring of a patient’s physiological data by physicians. Moreover, this system can reduce the number and expenses of healthcare centers, make up for the shortage of healthcare centers in remote areas, enable consultation with expert physicians around the world, and increase the health awareness of communities. The major challenges that affect the rapid deployment and widespread acceptance of such a system are the weaknesses in the authentication process, which should maintain the privacy of patients, and the integrity of remote medical instructions. Current research results indicate the need of a flexible authentication scheme. This study proposes a scheme with enhanced security for healthcare IoT systems, called an end-to-end authentication scheme for healthcare IoT systems, that is, an E2EA. The proposed scheme supports security services such as a strong and flexible authentication process, simultaneous anonymity of the patient and physician, and perfect forward secrecy services. A security analysis based on formal and informal methods demonstrates that the proposed scheme can resist numerous security-related attacks. A comparison with related authentication schemes shows that the proposed scheme is efficient in terms of communication, computation, and storage, and therefore cannot only offer attractive security services but can reasonably be applied to healthcare IoT systems.