Special Issue "Intelligent Perception, Application and Security Mechanism in the Internet of Things"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 December 2019.

Special Issue Editors

Guest Editor
Dr. Xiaochun Cheng

Department of Computer Science, Middlesex University, London, UK
Website | E-Mail
Interests: AI computing (decision support, reasoning, pattern recognition, machine learning, deep learning, optimization) and security (intrusion detection, malware detection, spam detection, fraud detection, security protocol verification, biometrics, crime intelligence analysis, cryptography, water marking, data origin provenance and tracing)
Guest Editor
Dr. Zheli Liu

Nankai University, China
Website | E-Mail
Interests: data privacy protection, ciphertext database, ciphertext set operation, and differential privacy
Guest Editor
Dr. Bing Jia

Inner Mongolia University, China
Website | E-Mail
Interests: mobile computing, Internet of Things, and intelligent applications

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence (AI) has attracted extensive attention from both academics and industry. There are many relevant important research results. The Internet of Things systems (IoT) have extended the information system to the physical world, greatly expanding the ability of human beings to perceive, to understand and to control the physical world, and profoundly affecting the industry production and lifestyle of human beings. The methods by which to apply AI technologies to the IoT systems and to enhance the intelligence of system perception, understanding, computing, application and security are important to the implementation of intelligent IoT applications.

With the rapid development of IoT technologies, the current IoT environments have many distinctive characteristics, such as: the universality of perception, the ubiquity of connection and information transmission, and the massive volume of communicated data. Considering these characteristics, in order to apply IoT designs in the intelligent, efficient, safe and stable manner, safe and low-cost network management technologies and management methods with learning ability, understanding capability, reasoning ability, and collaborating ability are essential. Hence, the combination of AI technologies with the IoT systems provides prominent advantages. The integration of AI technologies and IoT systems enables the ability to gain valuable insight from the massive volume of data generated. The IoT era is coming. AI will help to stimulate the great potential of the IoT systems.

This special issue aims to attract contributions with new developments of intelligent perception, application and security mechanisms in the Internet of Things, to enhance the intelligence of the IoT systems. The ultimate goal is to promote research and development of AI technologies for IoT systems by publishing high-quality research articles in this rapidly developing field.

Scopes include (but are not limited to) the following:

  • Theoretical understanding of AI in the IoT
  • Hidden data awareness
  • Passive data transmission
  • Intelligent data processing
  • Multi-sources heterogeneous data fusion
  • Security and credibility verification
  • Dynamic intelligent perception in complex scenarios
  • Intelligent network
  • Intelligent application
  • Intelligent Indoor and Outdoor Seamless Positioning
  • Data privacy in IOT
  • Intrusion Detection in IoT systems
  • Crowd-sensing and Crowdsourcing

Dr. Xiaochun Cheng
Dr. Zheli Liu
Dr. Bing Jia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • IoT
  • Security
  • Intelligent application

Published Papers (3 papers)

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Research

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Open AccessArticle
Semantic-Based Representation Binary Clone Detection for Cross-Architectures in the Internet of Things
Appl. Sci. 2019, 9(16), 3283; https://doi.org/10.3390/app9163283
Received: 8 July 2019 / Revised: 7 August 2019 / Accepted: 8 August 2019 / Published: 10 August 2019
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Abstract
Code reuse is widespread in software development as well as internet of things (IoT) devices. However, code reuse introduces many problems, e.g., software plagiarism and known vulnerabilities. Solving these problems requires extensive manual reverse analysis. Fortunately, binary clone detection can help analysts mitigate [...] Read more.
Code reuse is widespread in software development as well as internet of things (IoT) devices. However, code reuse introduces many problems, e.g., software plagiarism and known vulnerabilities. Solving these problems requires extensive manual reverse analysis. Fortunately, binary clone detection can help analysts mitigate manual work by matching reusable code and known parts. However, many binary clone detection methods are not robust to various compiler optimization options and different architectures. While some clone detection methods can be applied across different architectures, they rely on manual features based on human prior knowledge to generate feature vectors for assembly functions and fail to consider the internal associations between features from a semantic perspective. To address this problem, we propose and implement a prototype GeneDiff, a semantic-based representation binary clone detection approach for cross-architectures. GeneDiff utilizes a representation model based on natural language processing (NLP) to generate high-dimensional numeric vectors for each function based on the Valgrind intermediate representation (VEX) representation. This is the first work that translates assembly instructions into an intermediate representation and uses a semantic representation model to implement clone detection for cross-architectures. GeneDiff is robust to various compiler optimization options and different architectures. Compared to approaches using symbolic execution, GeneDiff is significantly more efficient and accurate. The area under the curve (AUC) of the receiver operating characteristic (ROC) of GeneDiff reaches 92.35%, which is considerably higher than the approaches that use symbolic execution. Extensive experiments indicate that GeneDiff can detect similarity with high accuracy even when the code has been compiled with different optimization options and targeted to different architectures. We also use real-world IoT firmware across different architectures as targets, therein proving the practicality of GeneDiff in being able to detect known vulnerabilities. Full article
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Open AccessArticle
Identity Management and Access Control Based on Blockchain under Edge Computing for the Industrial Internet of Things
Appl. Sci. 2019, 9(10), 2058; https://doi.org/10.3390/app9102058
Received: 28 April 2019 / Revised: 13 May 2019 / Accepted: 14 May 2019 / Published: 18 May 2019
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Abstract
Edge computing provides a unified platform for computing, networking, and storage resources, enabling data to be processed in a timely and efficient manner near the source. Thus, it has become the basic platform for industrial Internet of things (IIoT). However, computing′s unique features [...] Read more.
Edge computing provides a unified platform for computing, networking, and storage resources, enabling data to be processed in a timely and efficient manner near the source. Thus, it has become the basic platform for industrial Internet of things (IIoT). However, computing′s unique features have also introduced new security problems. To solve the problem, in this paper, blockchain-based identity management combining access control mechanism is designed under edge computing. The self-certified cryptography is utilized to realize the registration and authentication of network entities. We bind the generated implicit certificate to its identity and construct the identity and certificate management mechanism based on blockchain. Secondly, an access control mechanism based on Bloom filter is designed and integrated with identity management. Moreover, for secure communication in resource-constrained edge devices, a lightweight secret key agreement protocol based on self-authenticated public key is constructed. These mechanisms work together to provide data security guarantees for IIoT such as authentication, auditability, and confidentiality. Full article
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Review

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Open AccessReview
VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
Appl. Sci. 2019, 9(13), 2763; https://doi.org/10.3390/app9132763
Received: 8 June 2019 / Revised: 4 July 2019 / Accepted: 4 July 2019 / Published: 9 July 2019
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Abstract
Recently, the development of smart home technologies has played a crucial role in enhancing several real-life smart applications. They help improve the quality of life through systems designed to enhance convenience, comfort, entertainment, health of the householders, and security. Note, however, that malware [...] Read more.
Recently, the development of smart home technologies has played a crucial role in enhancing several real-life smart applications. They help improve the quality of life through systems designed to enhance convenience, comfort, entertainment, health of the householders, and security. Note, however, that malware attacks on smart home devices are increasing in frequency and volume. As people seek to improve and optimize comfort in their home and minimize their daily home responsibilities at the same time, this makes them attractive targets for a malware attack. Thus, attacks on smart home-based devices have emerged. The goals of this paper are to analyze the different aspects of cyber-physical threats on the smart home from a security perspective, discuss the types of attacks including advanced cyber-attacks and cyber-physical system attacks, and evaluate the impact on a smart home system in daily life. We have come up with a taxonomy focusing on cyber threat attacks that can also have potential impact on a smart home system and identify some key issues about VPNFilter malware that constitutes large-scale Internet of Things (IoT)-based botnet malware infection. We also discuss the defense mechanism against this threat and mention the most infected routers. The specific objective of this paper is to provide efficient task management and knowledge related to VPNFilter malware attack. Full article
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