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10 pages, 1210 KiB

Open AccessArticle

Vulnerabilities and Limitations of MQTT Protocol Used between IoT Devices

by Dan Dinculeană and Xiaochun Cheng

Appl. Sci. 2019, 9(5), 848; https://doi.org/10.3390/app9050848 - 27 Feb 2019

Cited by 111 | Viewed by 12181

With the proliferation of smart devices capable of communicating over a network using different protocols, each year more and more successful attacks are recorded against these, underlining the necessity of developing and implementing mechanisms to protect against such attacks. This paper will review [...] Read more.

With the proliferation of smart devices capable of communicating over a network using different protocols, each year more and more successful attacks are recorded against these, underlining the necessity of developing and implementing mechanisms to protect against such attacks. This paper will review some existing solutions used to secure a communication channel, such as Transport Layer Security or symmetric encryption, as well as provide a novel approach to achieving confidentiality and integrity of messages. The method, called Value-to-Keyed-Hash Message Authentication Code (Value-to-HMAC) mapping, uses signatures to send messages, instead of encryption, by implementing a Keyed-Hash Message Authentication Code generation algorithm. Although robust solutions exist that can be used to secure the communication between devices, this paper considers that not every Internet of Things (IoT) device or network design is able to afford the overhead and drop in performance, or even support such protocols. Therefore, the Value-to-HMAC method was designed to maximize performance while ensuring the messages are only readable by the intended node. The experimental procedure demonstrates how the method will achieve better performance than a symmetric-key encryption algorithm, while ensuring the confidentiality and integrity of information through the use of one mechanism. Full article

(This article belongs to the Special Issue Access Control Schemes for Internet of Things)

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14 pages, 935 KiB

Open AccessArticle

A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

by Shen Su, Yanbin Sun, Xiangsong Gao, Jing Qiu and Zhihong Tian

Appl. Sci. 2019, 9(3), 437; https://doi.org/10.3390/app9030437 - 28 Jan 2019

Cited by 36 | Viewed by 3783

Abstract

Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor [...] Read more.

Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor data are usually correlated (e.g., duplicated deployed sensors), and the correlations would be broken when anomalies occur with happen to the monitored equipment. In this paper, we propose to capture such sensor data correlation changes to improve the performance of IoT (Internet of Things) equipment anomaly detection. In our feature selection method, we first cluster correlated sensors together to recognize the duplicated deployed sensors according to sensor data correlations, and we monitor the data correlation changes in real time to select the sensors with correlation changes as the representative features for anomaly detection. To that end, (1) we conducted curve alignment for the sensor clustering; (2) we discuss the appropriate window size for data correlation calculation; (3) and adopted MCFS (Multi-Cluster Feature Selection) into our method to adapt to the online feature selection scenario. According to the experiment evaluation derived from real IoT equipment, we prove that our method manages to reduce the false negative of IoT equipment anomaly detection of 30% with almost the same level of false positive. Full article

(This article belongs to the Special Issue Access Control Schemes for Internet of Things)

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17 pages, 4021 KiB

Open AccessArticle

Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals

by Jiguang Lv, Dapeng Man, Wu Yang, Liangyi Gong, Xiaojiang Du and Miao Yu

Appl. Sci. 2019, 9(1), 175; https://doi.org/10.3390/app9010175 - 05 Jan 2019

Cited by 23 | Viewed by 3808

Abstract

WiFi infrastructures are widely deployed in both public and private buildings. They make the connection to the internet more convenient. Recently, researchers find that WiFi signals have the ability to sense the changes in the environment that can detect human motion and even [...] Read more.

WiFi infrastructures are widely deployed in both public and private buildings. They make the connection to the internet more convenient. Recently, researchers find that WiFi signals have the ability to sense the changes in the environment that can detect human motion and even identify human activities and his identity in a device-free manner, and has many potential security applications in a smart home. Previous human detection systems can only detect human motion of regular moving patterns. However, they may have a significant detection performance degradation when used in intrusion detection. In this study, we propose Robust Device-Free Intrusion Detection (RDFID) system leveraging fine-grained Channel State Information (CSI). The noises in the signals are removed by a Principle Component Analysis (PCA) and a low pass filter. We extract a robust feature of frequency domain utilizing Continuous Wavelet Transform (CWT) from all subcarriers. RDFID captures the changes from the whole wireless channel, and a threshold is obtained self-adaptively, which is calibration-free in different environments, and can be deployed in smart home scenarios. We implement RDFID using commodity WiFi devices and evaluate it in three typical office rooms with different moving patterns. The results show that our system can accurately detect intrusion of different moving patterns and different environments without re-calibration. Full article

(This article belongs to the Special Issue Access Control Schemes for Internet of Things)

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18 pages, 3722 KiB

Open AccessArticle

by Xu Kang, Bin Song and Fengyao Sun

Appl. Sci. 2019, 9(1), 135; https://doi.org/10.3390/app9010135 - 02 Jan 2019

Cited by 21 | Viewed by 4576

Abstract

In recent years, with the development of the Internet of Things (IoT) technology, a large amount of data can be captured from sensors for real-time analysis. By monitoring the traffic video data from the IoT, we can detect the anomalies that may occur [...] Read more.

In recent years, with the development of the Internet of Things (IoT) technology, a large amount of data can be captured from sensors for real-time analysis. By monitoring the traffic video data from the IoT, we can detect the anomalies that may occur and evaluate the security. However, the number of traffic anomalies is extremely limited, so there is a severe over-fitting problem when using traditional deep learning methods. In order to solve the problem above, we propose a similarity metric Convolutional Neural Network (CNN) based on a channel attention model for traffic anomaly detection task. The method mainly includes (1) A Siamese network with a hierarchical attention model by word embedding so that it can selectively measure similarities between anomalies and the templates. (2) A deep transfer learning method can automatically annotate an unlabeled set while fine-tuning the network. (3) A background modeling method combining spatial and temporal information for anomaly extraction. Experiments show that the proposed method is three percentage points higher than deep convolutional generative adversarial network (DCGAN) and five percentage points higher than AutoEncoder on the accuracy. No more time consumption is needed for the annotation process. The extracted candidates can be classified correctly through the proposed method. Full article

(This article belongs to the Special Issue Access Control Schemes for Internet of Things)

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15 pages, 4334 KiB

Open AccessArticle

Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks

by Yifeng Zhao, Kai Liu, Xueting Xu, Huayu Yang and Lianfen Huang

Appl. Sci. 2019, 9(1), 132; https://doi.org/10.3390/app9010132 - 02 Jan 2019

Cited by 14 | Viewed by 3238

Abstract

With the rapid growth of Internet-of-things (IoT) devices, IoT communication has become an increasingly crucial part of 5G wireless communication systems. The large-scale IoT devices access results in system overload and low utilization of energy efficiency under the existing network framework. In this [...] Read more.

With the rapid growth of Internet-of-things (IoT) devices, IoT communication has become an increasingly crucial part of 5G wireless communication systems. The large-scale IoT devices access results in system overload and low utilization of energy efficiency under the existing network framework. In this paper, the cluster head uses the LTE-M protocol, and the intra-cluster uses the low-power wide-area network (LPWAN) self-networking protocol in the wireless sensor network. By a detailed analysis of the messages exchanged between the device and the base station, we describe the causes of overload and the steps of data aggregate combined with the physical channel. Then, we explore the cluster head quantity and the optimal scale in the intra-cluster under the traditional K-mean algorithm. When K is 30 under specific resources, the simulation results show that the system’s access success probability and resource utilization are optimal. Also, we propose a distributed dynamic cluster-head selection and clustering scheme based on an improved K-means algorithm. Simulation results show that the proposed scheme can reach 88.07% on the access success probability. The throughput and resource utilization are 3.5 times high than that of the optimal K-means. Full article

(This article belongs to the Special Issue Access Control Schemes for Internet of Things)

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13 pages, 1027 KiB

Open AccessArticle

Application Communities Detection in Network

by Shuzhuang Zhang, Yingjun Qiu, Hao Luo and Zhigang Wu

Appl. Sci. 2019, 9(1), 31; https://doi.org/10.3390/app9010031 - 22 Dec 2018

Viewed by 2066

Abstract

The continuous growth of Internet traffic and its applications causes more difficulties for analyzing Internet communications. It has become an increasingly challenging task to discover latent community structure and find abnormal behavior patterns in network communication. In this paper, we propose a new [...] Read more.

The continuous growth of Internet traffic and its applications causes more difficulties for analyzing Internet communications. It has become an increasingly challenging task to discover latent community structure and find abnormal behavior patterns in network communication. In this paper, we propose a new type of network community—the application community—which can help understand large network structure and find anomaly network behavior. To detect such a community, a method is proposed whose first step is aggregating the nodes according to their topological relationships of the communication. It then clusters different application nodes according to the communication behavior modes in the same topological partition. Empirical results show that this method can accurately detect communities of different applications without any prior knowledge. In addition, it can identify the communities more accurately than other methods. Thus, this research greatly benefits the administration of IoT and cyber security. Full article

(This article belongs to the Special Issue Access Control Schemes for Internet of Things)

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14 pages, 2698 KiB

Open AccessArticle

Lightweight NFC Protocol for Privacy Protection in Mobile IoT

by Kai Fan, Chen Zhang, Kan Yang, Hui Li and Yintang Yang

Appl. Sci. 2018, 8(12), 2506; https://doi.org/10.3390/app8122506 - 05 Dec 2018

Cited by 9 | Viewed by 3565

Abstract

The Internet of Things (IoT) aims to achieve the interconnection of all devices in our lives. Due to the complex network environment, the IoT with mobile devices often faces many security problems, such as privacy leakages and identity forgery attacks. As a developing [...] Read more.

The Internet of Things (IoT) aims to achieve the interconnection of all devices in our lives. Due to the complex network environment, the IoT with mobile devices often faces many security problems, such as privacy leakages and identity forgery attacks. As a developing technology in mobile IoT, near field communication (NFC) is widely used in electronic payments and identity authentications. The current NFC studies mainly focus on payment technology, but there are a few studies on privacy protection and the lightweight requirements in the mobile IoT authentication protocol. We focus on the lightweight privacy protection authentication technology in mobile IoT. In the paper, we summarize the clustering model in mobile IoT networks and propose a lightweight authentication protocol. A security analysis shows that the protocol can resist many security threats, such as privacy leakages, identity forgeries, and replay attacks. The simulation also shows that the protocol is lightweight, with the utilization of look-up-tables (LUTs) and registers in our protocol being less than 0.5%. Our work can provide a secure and lightweight mobile authentication serve in the NFC-based mobile IoT network such as smart home and office attendance. Full article

(This article belongs to the Special Issue Access Control Schemes for Internet of Things)

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16 pages, 3386 KiB

Open AccessArticle

Research on the Open-Categorical Classification of the Internet-of-Things Based on Generative Adversarial Networks

by Caidan Zhao, Mingxian Shi, Zhibiao Cai and Caiyun Chen

Appl. Sci. 2018, 8(12), 2351; https://doi.org/10.3390/app8122351 - 22 Nov 2018

Cited by 13 | Viewed by 2476

Abstract

Nowadays, it is more and more important to deal with the potential security issues of internet-of-things (IoT). Indeed, using the physical layer features of IoT wireless signals to achieve individual identity authentication is an effective way to enhance the security of IoT. However, [...] Read more.

Nowadays, it is more and more important to deal with the potential security issues of internet-of-things (IoT). Indeed, using the physical layer features of IoT wireless signals to achieve individual identity authentication is an effective way to enhance the security of IoT. However, traditional classifiers need to know all the categories in advance to get the recognition models. Realistically, it is difficult to collect all types of samples, which will result in some mistakes that the unknown target class may be decided as a known one. Consequently, this paper constructs an improving open-categorical classification model based on the generative adversarial networks (OCC-GAN) to solve the above problems. Here, we have modified the loss function of the generative model G and the discriminative model D. Compared to the traditional GAN model which can generate the fake sample overlapping with the real samples, our proposed G model generates the fake samples as negative samples which are evenly surrounding with the real samples, while the D model learns to distinguish between real samples and fake samples. Besides, we add auxiliary training not only to gain a better recognition result but also to improve the efficiency of the model. Furthermore, Our proposed model is verified through experimental study. Compared to other common methods, such as one-class support vector machine (OC-SVM) and one-versus-rest support vector machine (OvR-SVM), the OCC-GAN model has a better performance. The recognition rate of the OCC-GAN model can reach more than 90% with a recall rate of 97% by the data of the IoT module. Full article

(This article belongs to the Special Issue Access Control Schemes for Internet of Things)

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Review

Jump to: Research

25 pages, 2476 KiB

Open AccessReview

Towards Trust and Friendliness Approaches in the Social Internet of Things

by Farhan Amin, Awais Ahmad and Gyu Sang Choi

Appl. Sci. 2019, 9(1), 166; https://doi.org/10.3390/app9010166 - 04 Jan 2019

Cited by 59 | Viewed by 4921

Abstract

The Internet of Things (IoT) is an interconnected network of heterogeneous entities, such as sensors and embedded devices. During the current era, a new field of research has emerged, referred to as the social IoT, which mainly includes social networking features. The social [...] Read more.

The Internet of Things (IoT) is an interconnected network of heterogeneous entities, such as sensors and embedded devices. During the current era, a new field of research has emerged, referred to as the social IoT, which mainly includes social networking features. The social IoT refers to devices that are capable of creating interactions with each other to independently achieve a common goal. Based on the structure, the support of numerous applications, and networking services, the social IoT is preferred over the traditional IoT. However, aspects like the roles of users and network navigability are major challenges that provoke users’ fears of data disclosure and privacy violations. Thus, it is important to provide reliable data analyses by using trust- and friendliness-based properties. This study was designed because of the limited availability of information in this area. It is a classified catalog of trust- and friendliness-based approaches in the social IoT with important highlights of important constraints, such as scalability, adaptability, and suitable network structures (for instance, human-to-human and human-to-object). In addition, typical concerns like communities of interest and social contacts are discussed in detail, with particular emphasis on friendliness- and trust-based properties, such as service composition, social similarity, and integrated cloud services. Full article

(This article belongs to the Special Issue Access Control Schemes for Internet of Things)

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