| 1. |
- Butun, Ismail, et al.
(författare)
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Security of the Internet of Things : Vulnerabilities, Attacks, and Countermeasures
- 2020
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Ingår i: IEEE Communications Surveys and Tutorials. - 1553-877X. ; 22:1, s. 616-644
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Tidskriftsartikel (refereegranskat)abstract
- Wireless Sensor Networks (WSNs) constitute one of the most promising third-millennium technologies and have wide range of applications in our surrounding environment. The reason behind the vast adoption of WSNs in various applications is that they have tremendously appealing features, e.g., low production cost, low installation cost, unattended network operation, autonomous and longtime operation. WSNs have started to merge with the Internet of Things (IoT) through the introduction of Internet access capability in sensor nodes and sensing ability in Internet-connected devices. Thereby, the IoT is providing access to huge amount of data, collected by the WSNs, over the Internet. Hence, the security of IoT should start with foremost securing WSNs ahead of the other components. However, owing to the absence of a physical line-of-defense, i.e., there is no dedicated infrastructure such as gateways to watch and observe the flowing information in the network, security of WSNs along with IoT is of a big concern to the scientific community. More specifically, for the application areas in which CIA (confidentiality, integrity, availability) has prime importance, WSNs and emerging IoT technology might constitute an open avenue for the attackers. Besides, recent integration and collaboration of WSNs with IoT will open new challenges and problems in terms of security. Hence, this would be a nightmare for the individuals using these systems as well as the security administrators who are managing those networks. Therefore, a detailed review of security attacks towards WSNs and IoT, along with the techniques for prevention, detection, and mitigation of those attacks are provided in this paper. In this text, attacks are categorized and treated into mainly two parts, most or all types of attacks towards WSNs and IoT are investigated under that umbrella: 'Passive Attacks' and 'Active Attacks'. Understanding these attacks and their associated defense mechanisms will help paving a secure path towards the proliferation and public acceptance of IoT technology.
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| 2. |
- Li, Mianjie, et al.
(författare)
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A dual-embedded tamper detection framework based on block truncation coding for intelligent multimedia systems
- 2023
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Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 649
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Tidskriftsartikel (refereegranskat)abstract
- The rich intelligent multimedia systems provide great convenience and efficiency. Unfortunately, it faces a series of security challenges and threats in developing and deploying multimedia ser-vices, such as tampering, hijacking, and adversarial attacks. Therefore, this paper proposes a dual -embedding framework based on block truncation coding to improve the security of intelligent multimedia systems. First, the signal is decomposed in frequency domain by using approximate translation invariance to obtain multi-layer frequency-domain parameters; then, this paper hides the encrypted data in two layers of low-frequency coefficients through fragile and robust embedding algorithms, respectively. In addition, in order to further improve the security per-formance, this paper adopts the method of block truncation coding to encrypt the embedded data. On the basis of performance analysis, the superiority of this method is illustrated by comparing with the existing methods.
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| 3. |
- Liu, Yongxin, et al.
(författare)
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Exploring data validity in transportation systems for smart cities
- 2017
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Ingår i: IEEE Communications Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 0163-6804 .- 1558-1896. ; 55:5, s. 26-33
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Tidskriftsartikel (refereegranskat)abstract
- Efficient urban transportation systems are widely accepted as essential infrastructure for smart cities, and they can highly increase a city°s vitality and convenience for residents. The three core pillars of smart cities can be considered to be data mining technology, IoT, and mobile wireless networks. Enormous data from IoT is stimulating our cities to become smarter than ever before. In transportation systems, data-driven management can dramatically enhance the operating efficiency by providing a clear and insightful image of passengers° transportation behavior. In this article, we focus on the data validity problem in a cellular network based transportation data collection system from two aspects: Internal time discrepancy and data loss. First, the essence of time discrepancy was analyzed for both automated fare collection (AFC) and automated vehicular location (AVL) systems, and it was found that time discrepancies can be identified and rectified by analyzing passenger origin inference success rate using different time shift values and evolutionary algorithms. Second, the algorithmic framework to handle location data loss and time discrepancy was provided. Third, the spatial distribution characteristics of location data loss events were analyzed, and we discovered that they have a strong and positive relationship with both high passenger volume and shadowing effects in urbanized areas, which can cause severe biases on passenger traffic analysis. Our research has proposed some data-driven methodologies to increase data validity and provided some insights into the influence of IoT level data loss on public transportation systems for smart cities.
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| 4. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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5G for mobile augmented reality
- 2022
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Ingår i: International Journal of Communication Systems. - : John Wiley & Sons. - 1074-5351 .- 1099-1131. ; 35:5
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 5. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Cross-Layer Optimization for Industrial Internet of Things in Real Scene Digital Twins
- 2022
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 9:17, s. 15618-15629
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Tidskriftsartikel (refereegranskat)abstract
- The development of the Industrial Internet of Things (IIoT) and digital twins (DTs) technology brings new opportunities and challenges to all walks of life. The work aims to study the cross-layer optimization of DTs in IIoT. The specific application scenarios of hazardous gas leakage boundary tracking in the industry is explored. The work proposes an industrial hazardous gas tracking algorithm based on a parallel optimization framework, establishes a three-layer network of distributed edge computing based on IIoT, and develops a two-stage industrial hazardous gas tracking algorithm based on a state transition model. The performance of different algorithms is analyzed. The results indicate that the tracking state transition and target wake-up module can effectively track the gas boundary and reduce the network energy consumption. The task success rate of the parallel optimization algorithm exceeds 0.9 in 5 s. When the number of network nodes in the state transition algorithm is N = 600, the energy consumption is only 2.11 J. The minimum tracking error is 0.31, which is at least 1.33 lower than that of the exact conditional tracking algorithm. Therefore, the three-layer network edge computing architecture proposed here has an excellent performance in industrial gas diffusion boundary tracking.
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| 6. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Deep Learning for Intelligent Human-Computer Interaction
- 2022
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:22
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Forskningsöversikt (refereegranskat)abstract
- In recent years, gesture recognition and speech recognition, as important input methods in Human-Computer Interaction (HCI), have been widely used in the field of virtual reality. In particular, with the rapid development of deep learning, artificial intelligence, and other computer technologies, gesture recognition and speech recognition have achieved breakthrough research progress. The search platform used in this work is mainly the Google Academic and literature database Web of Science. According to the keywords related to HCI and deep learning, such as "intelligent HCI", "speech recognition", "gesture recognition", and "natural language processing", nearly 1000 studies were selected. Then, nearly 500 studies of research methods were selected and 100 studies were finally selected as the research content of this work after five years (2019-2022) of year screening. First, the current situation of the HCI intelligent system is analyzed, the realization of gesture interaction and voice interaction in HCI is summarized, and the advantages brought by deep learning are selected for research. Then, the core concepts of gesture interaction are introduced and the progress of gesture recognition and speech recognition interaction is analyzed. Furthermore, the representative applications of gesture recognition and speech recognition interaction are described. Finally, the current HCI in the direction of natural language processing is investigated. The results show that the combination of intelligent HCI and deep learning is deeply applied in gesture recognition, speech recognition, emotion recognition, and intelligent robot direction. A wide variety of recognition methods were proposed in related research fields and verified by experiments. Compared with interactive methods without deep learning, high recognition accuracy was achieved. In Human-Machine Interfaces (HMIs) with voice support, context plays an important role in improving user interfaces. Whether it is voice search, mobile communication, or children's speech recognition, HCI combined with deep learning can maintain better robustness. The combination of convolutional neural networks and long short-term memory networks can greatly improve the accuracy and precision of action recognition. Therefore, in the future, the application field of HCI will involve more industries and greater prospects are expected.
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| 7. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Digital Twins : The Confluence of Virtual Reality With IoT
- 2023
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Ingår i: IEEE Consumer Electronics Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 2162-2248 .- 2162-2256. ; 12:6, s. 27-28
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Virtual reality (VR) and the Internet of Things (IoT) are two of the most important technologies to arise in the past decade or more. Taken individually, each technology represents a real sea change. VR has the potential to truly change the world in some surprising ways, while the IoT has already transformed the way we live our lives. Digital Twins is the confluence of these two developments, though, that offers the most promise and opportunity of all. VR applications promise to be the next mainstream business. They are used by companies in a wide range of industries, from product design to healthcare and employee training. At the same time, IoT platforms and related devices are much in demand in today's tech and business world. These smart devices connected to the Internet are capable of collecting, interpreting, and relaying data without human intervention or supervision.
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| 8. |
- Lv, Zhihan, et al.
(författare)
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Digital Twins Based on Quantum Networking
- 2022
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Ingår i: IEEE Network. - : IEEE. - 0890-8044 .- 1558-156X. ; 36:5, s. 88-93
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Tidskriftsartikel (refereegranskat)abstract
- This work aims to improve the communication security of the industrial Internet of Things (IIoT) based on digital twins (DTs). The related technologies of quantum communication are introduced to improve network communication. Firstly, the key DTs technologies in the construction of IIoT are expounded. Also, the characteristics of quantum communication are analyzed. Secondly, a channel encryption scheme based on quantum communication is proposed to ensure the communication security of IIoT. The scheme uses the five-particle entanglement state and two-particle bell state as entanglement channels to realize two-particle quantum teleportation. Finally, an Adaptive Key Residue algorithm is proposed based on the quantum key distribution mechanism. The algorithm verification suggests that the success rate of service distribution decreases with the increase in network load. When the service load reaches 1000, the Adaptive Key Residue algorithm can maintain a success rate of service distribution in the network higher than 0.6. Besides, the success rate of service distribution increases with the growth of the total key generation rate V and the key pool capacity C. The research results reported here are of great significance for realizing the secure communication of IIoT systems based on digital twins to ensure the effective operation of network communication and the secure transmission of data.
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| 9. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Editorial : Digital twins of plant and forest
- 2022
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Ingår i: Frontiers in Plant Science. - : Frontiers Media S.A.. - 1664-462X. ; 13
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 10. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Secure Deep Learning in Defense in Deep-Learning-as-a-Service Computing Systems in Digital Twins
- 2024
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Ingår i: IEEE Transactions on Computers. - : IEEE. - 0018-9340 .- 1557-9956. ; 73:3, s. 656-668
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Tidskriftsartikel (refereegranskat)abstract
- While Digital Twins (DTs) bring convenience to city managers, they also generate new challenges to city network security. Currently, cyberspace security becomes increasingly complicated. Intrusion detection and Deep Learning (DL) are combined with shunning security threats in service computing systems and improving network defense capabilities. DTs can be applied to network security. People's understanding of cyberspace security can be improved using DTs to digitally define, model, and display the network environment and security status. The intrusion detection data are optimized based on DL technology, and a network intrusion detection algorithm integrated with Deep Neural Network (DNN) model is proposed. In the cloud service system, a trust model based on Keyed-Hashing-based Self-Synchronization (KHSS) is introduced. This model predicts the security state and detects attacks according to existing malicious attacks, ensuring the network security defense system's regular operation. Finally, simulation experiments verify the Deep Belief Networks (DBN) model's feasibility and the cloud trust model. The DBN algorithm proposed improves the correct detection rate of unknown samples by 4.05% compared with the Support Vector Machine (SVM) algorithm. From the 20,100 pieces of data in the test dataset, the number of correct attacks detected by the DBN algorithm exceeds those by the SVM algorithm by 818. DBN algorithm requires a short detection time while ensuring optimal detection accuracy. The KHSS+DBN model predicts cloud security states, and the results are the same as the actual states, with an error of only 1%similar to 2%.
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