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Security and Privacy for IoT Networks and the Mobile Internet

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 12414

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Special Issue Editors

Prof. Dr. Huachun Zhou

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Guest Editor

School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
Interests: network security; Internet of Things; service function chains; mobile networks; etc.

Dr. Bohao Feng

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Guest Editor

School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
Interests: network security; software-defined networks; satellite networks; mobile internet; etc.

Dr. Lichuan Ma

E-Mail Website
Guest Editor

School of Cyber Engineering, Xidian University, Xi’an, China
Interests: trust management; data security; blockchain

Dr. Youyang Qu

E-Mail Website
Guest Editor

Data61, Commonwealth Scientific and Industrial Research Organization, Melbourne 3008, Australia
Interests: personalized privacy protection; federated learning; cybersecurity; blockchain, etc.
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Special Issue Information

Dear Colleagues,

The massive data generated by tremendous sensors, devices, and machines have raised great concerns for the security and privacy of IoT (Internet of Things) networks and the mobile Internet (e.g., 5G/B5G/6G, mobile/multi-access edge computing, satellite networks), while traditional countermeasures are facing serious challenges in both effectiveness and efficiency. Recently, with the rise of emerging network paradigms such as SDNs (software-defined networks), NFVs (network functions virtualization), and SFCs (service function chains), in addition to data analytics techniques such as machine learning and knowledge graphs, both the academic and industrial communities are seeking more smart, flexible, and efficient solutions for better network defense from all types of attacks and data/identity privacy protection.

This Special Issue addresses all possible solutions to cope with the increasingly severe security and privacy problems of IoT networks and the mobile Internet. Topics of interest include, but are not limited to, the following:

Advanced security models for IoT networks and the mobile Internet;
Privacy protection algorithms or mechanisms for data sharing over IoT networks and the mobile Internet;
Novel and lightweight authentication methods for different roles in IoT networks and the mobile Internet;
Trust management models considering diverse trust levels in IoT networks and the mobile Internet;
New blockchain architectures and consensus algorithms to improve communication and storage performance for IoT networks and the mobile Internet;
Evaluation of blockchain-based security and privacy systems for IoT networks and the mobile Internet;
Machine learning models, especially distributed machine learning models, to enhance the security and privacy of IoT networks and the mobile Internet;
Federated learning, such as asynchronous federated learning, decentralized federated learning, personalized federated learning, etc.; to enhance the security and privacy of IoT networks and the mobile Internet;
Other security or privacy research directions are also welcome.

Prof. Dr. Huachun Zhou
Dr. Bohao Feng
Dr. Lichuan Ma
Dr. Youyang Qu
Guest Editors

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Published Papers (7 papers)

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Research

15 pages, 2515 KiB

Open AccessArticle

Distributed Detection of Malicious Android Apps While Preserving Privacy Using Federated Learning

by Suchul Lee

Sensors 2023, 23(4), 2198; https://doi.org/10.3390/s23042198 - 15 Feb 2023

Viewed by 1351

Abstract

Recently, deep learning has been widely used to solve existing computing problems through large-scale data mining. Conventional training of the deep learning model is performed on a central (cloud) server that is equipped with high computing power, by integrating data via high computational intensity. However, integrating raw data from multiple clients raises privacy concerns that are increasingly being focused on. In federated learning (FL), clients train deep learning models in a distributed fashion using their local data; instead of sending raw data to a central server, they send parameter values of the trained local model to a central server for integration. Because FL does not transmit raw data to the outside, it is free from privacy issues. In this paper, we perform an experimental study that explores the dynamics of the FL-based Android malicious app detection method under three data distributions across clients, i.e., (i) independent and identically distributed (IID), (ii) non-IID, (iii) non-IID and unbalanced. Our experiments demonstrate that the application of FL is feasible and efficient in detecting malicious Android apps in a distributed manner on cellular networks. Full article

(This article belongs to the Special Issue Security and Privacy for IoT Networks and the Mobile Internet)

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19 pages, 4816 KiB

Open AccessArticle

A Dynamic Deployment Method of Security Services Based on Malicious Behavior Knowledge Base

by Qi Guo, Man Li, Weilin Wang and Ying Liu

Sensors 2022, 22(22), 9021; https://doi.org/10.3390/s22229021 - 21 Nov 2022

Cited by 1 | Viewed by 1473

Abstract

In view of various security requirements, there are various security services in the network. In particular, DDoS attacks have various types and detection methods. How to flexibly combine security services and make full use of the information provided by security services have become urgent problems to be solved. This paper combines the reasoning ability of the malicious behavior knowledge base to realize the dynamic deployment of the service function chain and dynamic configuration of the security service function. The method feeds back the information generated by the security service to the knowledge base. After the analysis of the knowledge base, the service function chain path and the security service configuration policies are generated, and these policies will be dynamically distributed to the security service function. Finally, security services can be dynamically arranged for different network traffic, realizing the coordinated use of various security services and improving the overall detection rate of the network. The experimental results show that by arranging the paths under the UDP and the TCP, the overall detection rate of the network can reach 99% and 88%, respectively, indicating that it has a good overall detection performance for multiple distributed denial of service (DDoS) attacks. Full article

(This article belongs to the Special Issue Security and Privacy for IoT Networks and the Mobile Internet)

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28 pages, 5922 KiB

Open AccessArticle

Blockchain-Based Access Control and Behavior Regulation System for IoT

by Haoxiang Song, Zhe Tu and Yajuan Qin

Sensors 2022, 22(21), 8339; https://doi.org/10.3390/s22218339 - 30 Oct 2022

Cited by 4 | Viewed by 1887

Abstract

With the development of 5G and the Internet of things (IoT), the multi-domain access of massive devices brings serious data security and privacy issues. At the same time, most access systems lack the ability to identify network attacks and cannot adopt dynamic and timely defenses against various security threats. To this end, we propose a blockchain-based access control and behavior regulation system for IoT. Relying on the attribute-based access control model, this system deploys smart contracts on the blockchain to achieve distributed and fine-grained access control and ensures that the identity and authority of access users can be trusted. At the same time, an inter-domain communication mechanism is designed based on the locator/identifier separation protocol and ensures the traffic of access users are authorized. A feedback module that combines traffic detection and credit evaluation is proposed, ensuring real-time detection and fast, proactive responses against malicious behavior. Ultimately, all modules are linked together through workflows to form an integrated security model. Experiments and analysis show that the system can effectively provide comprehensive security protection in IoT scenarios. Full article

(This article belongs to the Special Issue Security and Privacy for IoT Networks and the Mobile Internet)

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25 pages, 5316 KiB

Open AccessArticle

Trusted Multi-Domain DDoS Detection Based on Federated Learning

by Ziwei Yin, Kun Li and Hongjun Bi

Sensors 2022, 22(20), 7753; https://doi.org/10.3390/s22207753 - 12 Oct 2022

Cited by 4 | Viewed by 1740

Abstract

Aiming at the problems of single detection target of existing distributed denial of service (DDoS) attacks, incomplete detection datasets and privacy caused by shared datasets, we propose a trusted multi-domain DDoS detection method based on federated learning. Firstly, we divide the types of DDoS attacks into different sub-attacks, design the federated learning dataset for DDoS detection in each domain, and use them to realize a more comprehensive detection method of DDoS attacks on the premise of protecting the data privacy of each domain. Secondly, in order to improve the robustness of federated learning and alleviate poisoning attack, we propose a reputation evaluation method based on blockchain, which estimates interaction reputation, data reputation and resource reputation of each participant comprehensively, so as to obtain the trusted federated learning participants and identify the malicious participants. In addition, we also propose a combination scheme of multi-domain detection and distributed knowledge base and design a feature graph of malicious behavior based on a knowledge graph to realize the memory of multi-domain feature knowledge. The experimental results show that the accuracy of most categories of the multi-domain DDoS detection method can reach more than 95% with the protection of datasets, and the reputation evaluation method proposed in this paper has a higher ability to identify malicious participants against the data poisoning attack when the threshold is set to 0.6. Full article

(This article belongs to the Special Issue Security and Privacy for IoT Networks and the Mobile Internet)

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24 pages, 1497 KiB

Open AccessArticle

MODECP: A Multi-Objective Based Approach for Solving Distributed Controller Placement Problem in Software Defined Network

by Chenxi Liao, Jia Chen, Kuo Guo, Shang Liu, Jing Chen and Deyun Gao

Sensors 2022, 22(15), 5475; https://doi.org/10.3390/s22155475 - 22 Jul 2022

Cited by 3 | Viewed by 1198

Abstract

Software-Defined Network is an emerging networking paradigm that enables intelligent and flexible network management. Specifically, the design of the control plane is crucial. Therefore, in order to avoid a single point of failure, multiple controllers are deployed constantly in a distributed manner on the control plane. In this paper, we propose a controller placement approach based on multiple objectives (MODECP), including network delay, network security, load-balancing rate, and link occupancy. In the controller placement stage, an improved multi-objective differential evolution algorithm is proposed to search for controllers’ positions and assign switches to controllers reasonably. Furthermore, an improved affinity propagation algorithm is proposed to obtain the number of controllers placed in the network partition stage, comprehensively considering the delay, node security, and load. Simulations are performed based on several topologies from Internet Topology Zoo. Extensive results show that the proposed algorithm can realize trade-offs among multiple objectives and improve network performance in delay, security, controller load, and link occupancy compared to the single-objective based approach. Moreover, compared with the genetic algorithm and random placement algorithm, the proposed algorithm performs better with low latency, high security, low load rate, and low link overhead. Full article

(This article belongs to the Special Issue Security and Privacy for IoT Networks and the Mobile Internet)

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18 pages, 4152 KiB

Open AccessArticle

Dual Attention-Based Industrial Surface Defect Detection with Consistency Loss

by Xuyang Li, Yu Zheng, Bei Chen and Enrang Zheng

Sensors 2022, 22(14), 5141; https://doi.org/10.3390/s22145141 - 08 Jul 2022

Cited by 6 | Viewed by 1983

Abstract

In industrial production, flaws and defects inevitably appear on surfaces, resulting in unqualified products. Therefore, surface defect detection plays a key role in ensuring industrial product quality and maintaining industrial production lines. However, surface defects on different products have different manifestations, so it is difficult to regard all defective products as being within one category that has common characteristics. Defective products are also often rare in industrial production, making it difficult to collect enough samples. Therefore, it is appropriate to view the surface defect detection problem as a semi-supervised anomaly detection problem. In this paper, we propose an anomaly detection method that is based on dual attention and consistency loss to accomplish the task of surface defect detection. At the reconstruction stage, we employed both channel attention and pixel attention so that the network could learn more robust normal image reconstruction, which could in turn help to separate images of defects from defect-free images. Moreover, we proposed a consistency loss function that could exploit the differences between the multiple modalities of the images to improve the performance of the anomaly detection. Our experimental results showed that the proposed method could achieve a superior performance compared to the existing anomaly detection-based methods using the Magnetic Tile and MVTec AD datasets. Full article

(This article belongs to the Special Issue Security and Privacy for IoT Networks and the Mobile Internet)

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21 pages, 4183 KiB

Open AccessArticle

A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles

by Qianyong Huang, Xianyun Xu, Huifang Chen and Lei Xie

Sensors 2022, 22(12), 4423; https://doi.org/10.3390/s22124423 - 11 Jun 2022

Cited by 6 | Viewed by 1796

Abstract

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate. Full article

(This article belongs to the Special Issue Security and Privacy for IoT Networks and the Mobile Internet)

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