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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Sharifpour, Mohammadjavad Tochae, Seyyed Amir Asghari Bohlooli, Shabnam Department of Computer Engineering Tehran Iran Kharazmi University Faculty of Engineering Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331507565

The Internet of Things (IoT) seeks to establish a vast network comprising billions of interconnected devices to enable seamless data exchange and intelligent interactions between people and objects. This network is characterized by openness, diversity, and dynamicity, leading to trust issues that impede the widespread adoption of IoT. Trust management plays a crucial role in identifying malicious nodes, fostering trust relationships, and bolstering system security. Conventional trust management systems face significant challenges that hinder their effectiveness in supporting IoT development. Blockchain, as a disruptive technology, shows promise in addressing these trust management challenges by leveraging its decentralized, consistent, and tamper-proof features. This study aims to enhance trust in the Internet of Things by leveraging blockchain technology. The article introduces key concepts such as blockchain technology, CTL (Certificate Trust List), IoT, and trust management, emphasizing critical research and development in trust management models. To highlight the importance of blockchain in maintaining trust, a novel trust management model is proposed, incorporating CTL to validate IoT Devices' legitimacy in blockchain transactions. Finally, the article assesses the efficiency of the proposed model, with the outcomes providing strong proof of its effectiveness. Our simulation results indicate that utilizing the PoW (Proof of Work) consensus model in an IoT system may not be the most optimal approach. The model we have presented displays a reduced memory growth rate of only 0.39% per block. This enhancement is projected to greatly improve the process of block verification. Specifically, the PoW algorithm took 4.2 seconds to incorporate 10 blocks, whereas our model completed the same task in just 0.61 seconds. © 2024 IEEE.

关键词： Blockchain

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Printing Stretchable Electronics With Fused Filament Fabrication

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第2期4卷 98-107页

作者： Jahan, Sharmin Lazarus, Nathan University of Delaware Electrical and Computer Engineering Department NewarkDE19716 United States

Additive manufacturing is a powerful approach forintegrating flexible and stretchable conductors into complex3-D structures, but many current printing technologies, suchas direct ink writing (DIW), are expensive and challengingto access for many potential users, such as hobbyists andsmall companies. In this work, a low-cost commercial fusedfilament fabrication (FFF) 3-D printer is used to manufacturelow-resistivity stretchable serpentine conductors. By using dualextruder heads, serpentines are printed in a highly conductivemetal-particulate-based composite on a thermoplastic elastomer(TPE) part and demonstrated to survive mechanical strains of upto 25%, sufficient for use in close contact with the human *** electroless plating is also demonstrated to metalize theprinted traces, depositing a thin layer of copper for improvedelectrical conductivity. Using this approach, we demonstrateconductivity improvements by about three orders of magnitude(∼ 103) over past FFF printed serpentines, a major milestonein making stretchable electronics cheaper and more *** resulting stretchable conductors are then demonstrated toprint devices, including stretchable inductors and stretchableinterconnect on a soft robotic actuator. © 2022 IEEE.

关键词： Reinforced plastics

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Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8639-8658页

作者： Karthika, K. Rangasamy, Devi Priya Anna University Chennai India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time. © 2024 – IOS Press. All rights reserved.

关键词： Sensitive data

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Physical Vulnerability Evaluation of LoRaWAN Network-based Cyber-Physical Systems under Electromagnetic Interference 5

Physical Vulnerability Evaluation of LoRaWAN Network-based C...

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5th CPSSI International Symposium on Cyber-Physical Systems (Applications and Theory), CPSAT 2024

作者： Saghaei, Faezeh Tavakkoli, Morteza Zarandi, Hamid R. Department of Computer Engineering Iran

ISBN: (纸本)9798331529284

LoRaWAN is a crucial communication protocol for cyber-physical systems. Therefore, it is important to study the performance and check its reliability. This paper investigates the performance of LoRaWAN under Electromagnetic Interference (EMI) conditions using a safety-critical case study that encompasses typical scenarios. A system was developed to monitor environmental parameters. The end transmitter device, equipped with a LoRa module, sends data to a gateway connected to a monitoring system. Initial experiments were created based on different payload sizes. Each one was repeated multiple times, with both horizontal and vertical antennas. Evaluation metrics such as packet loss rate, delay, SNR, and RSSI were analyzed. Experiments were conducted in compliance with IEC 61000-4-3 standard, covering 80 MHz to 1 GHz, while excluding LoRa working frequency. Results show that the LoRa module exhibits high robustness to EMI up to 1 GHz. Firstly, no packet loss happened. Secondly, initial failures in 150-380 MHz range were resolved by adding a shield clamp. Thirdly, variations in packet size did not significantly impact performance metrics. Experiments with a vertical antenna resulted in slightly worse RSSI and SNR compared to a horizontal antenna, however all values remained within acceptable limits by standard. Overall, the LoRa module demonstrated effective performance against EMI, proving its reliability for IoT applications in such conditions. © 2024 IEEE.

关键词： Packet loss

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RFOOD: Real-time Facial Authentication and Out -of-distribution Detection with Short-range FMCW Radar 23

RFOOD: Real-time Facial Authentication and Out -of-distribut...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Kahya, Sabri Mustafa Yavuz, Muhammet Sami Sivrikaya, Boran Hamdi Steinbach, Eckehard Department of Computer Engineering Germany

ISBN: (纸本)9798350374889

Out-of-distribution (OOD) detection is critical for the safe deployment of modern neural network architectures, as it aims to identify samples outside the training domain. In this paper, we introduce RFOOD, a novel OOD detection framework designed for real-time, privacy-preserving facial authentication using low-cost frequency-modulated continuous-wave (FMCW) radar. RFOOD employs both range-Doppler and micro range- Doppler images to enhance the detection accuracy. The architecture consists of a multi-encoder multi-decoder Body Part (BP) and Intermediate Linear Encoder-Decoder (ILED) components. This design allows the system to accurately classify a single individual's face as in-distribution (ID) while identifying all other faces as OOD. On our dataset collected with 60 GHz short-range FMCW radar, RFOOD achieves an Area Under the Receiver Operating Characteristic (AUROC) curve of 94.13 % and a False Positive Rate of 18.12% at a True Positive Rate of 95 % (FPR95). Additionally, RFOOD outperforms state-of-the-art OOD detection methods in common OOD detection metrics and operates in real-time. © 2024 IEEE.

关键词： Image enhancement

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Underwater object detection based on enhanced YOLOv4 architecture

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Multimedia Tools and Applications 2024年第18期83卷 53759-53783页

作者： Liu, Ching-Hua Lin, Chang Hong Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan

Object detection and image restoration pose significant challenges in deep learning and computer vision. These tasks are widely employed in various applications, and there is an increasing demand for specialized environments where images are prone to blur or noise, which can adversely affect subsequent results. In recent years, significant breakthroughs have been achieved in object detection performance. While some previously proposed methods prioritize high accuracy at the cost of longer inference times, others prioritize speed. Therefore, it is crucial to design an efficient network architecture that maintains both inference speed and high accuracy. This research proposes a network architecture for underwater object detection with an attention mechanism. The proposed approach differentiates itself from other methods by employing a deblurring network as a preprocessing step to restore and enhance the image quality of the underwater dataset. Additionally, in the feature extraction stage of the detection network, channel and spatial feature information are individually enhanced. These adaptive attention features are then integrated into a multi-scale feature fusion. Finally, the cross-stage local method is combined to improve the learning ability of the convolutional neural network while reducing the size of the model. Based on the experimental results, our proposed model architecture achieves leading accuracy and strikes a favorable balance in terms of model size compared to previously proposed methods. Based on our structure, the metrics of AP and AP50 reach 66.8 and 87.6, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Image reconstruction

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IDC-insight: boosting intrusion detection accuracy in IoT networks with Naïve Bayes and multiple classifiers

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International Journal of Information Technology (Singapore) 2025年第2期17卷 869-881页

作者： Zaben, Sufyan Othman Department of Computer Engineering College of Engineering Al-Iraqia University Baghdad Iraq

In this paper, a novel hybrid architecture, combining a Machine Learning (ML) infrastructure, operates as an IDS on IoT networks for the purpose of detecting intrusions. IDS systems learn to understand peoples' network behavior and make delimitation of anomalies by machine learning and statistical methods providing more enhanced protection. The IDC-Insight champion Decision Tree and Naïve Bayes algorithms for detection purposes and five ML algorithms for the classification task from the Random Forest, AdaBoost, C4.5, MLP and SVM. An evaluation on NSL-KDD-Cup dataset after an improvement, show that Naïve Bayes model outperformed Decision Tree (0.8330) under the framework, and this confirms the framework's potential in classifying network intrusion attacks along with improving network intrusion detection accuracy with (0.9847). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Classification Detection Intrusion detection IoT MPL

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Hybrid ECD model for firewall tuning and attack detection

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International Journal of Wireless and Mobile Computing 2025年第1期28卷 86-102页

作者： Thyagarajan, C. Vijay Bhanu S., S.V. Suthir, S. Department of Computer Science and Engineering Annamalai University Tamil Nadu Annamalai Nagar India

The rigorous security requirements and domain experts are necessary for the tuning of firewalls and for the detection of attacks. Those firewalls may create an incorrect sense or state of protection if they are improperly configured. One of the major configuration problems in firewalls is related to misconfiguration in the access control roles added to the firewall that will control network traffic. Furthermore, Software-Defined Networking (SDN) has greatly improved the network management. In this research, a hybrid Deep Learning (DL)-based firewall is designed. The request log is sent to the primary firewall, which tracks the network traffic and restricts the vulnerabilities and undesirable traffic. The EfficientNet-B3-Attn-2 fused Cascade Neuro-Fuzzy Network (ECD) is developed for network security whenever the primary firewall fails to regulate the network traffic. Furthermore, the devised framework is evaluated in terms of accuracy, sensitivity and specificity metrics that yield values like 0.885, 0.946 and 0.915. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： computer system firewalls

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Optimised hybrid classification approach for rice leaf disease prediction with proposed texture features

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Journal of Control and Decision 2024年第1期11卷 84-97页

作者： Sakhamuri Sridevi K.Kiran Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramIndia

This paper aims to frame a new rice disease prediction model that included three major ***,median filtering(MF)is deployed during pre-processing and then‘proposed Fuzzy Means Clustering(FCM)based segmentation’is *** that,‘Discrete Wavelet Transform(DWT),Scale-Invariant Feature Transform(SIFT)and low-level features(colour and shape),Proposed local Binary Pattern(LBP)based features’are extracted that are classified via‘MultiLayer Perceptron(MLP)and Long Short Term Memory(LSTM)’and predicted outcomes are *** exact prediction,this work intends to optimise the weights of LSTM using Inertia Weighted Salp Swarm Optimisation(IW-SSO)***,the development of IW-SSO method is established on varied metrics.

关键词： Rice disease improved fuzzy hybrid classifiers optimised LSTM IW-SSO algorithm

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Physiological biometric image quality assessment -a review

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Multimedia Tools and Applications 2025年 1-35页

作者： Choudhary, Parul Pathak, Pooja Gupta, Phalguni Department of Computer Engineering & Applications GLA University U.P Mathura India

This paper makes a literature survey on biometric image quality assessment (BIQA) techniques focusing on physiological traits. It covers a wide range of methodologies, metrics and evaluation techniques. Objective image quality assessment (IQA) methods primarily focus on quantifying image quality using computational algorithms, while subjective IQA methods rely on human observers to provide quality ratings based on visual perception. The paper categorises the existing IQA techniques based on their characteristics and applications. It explores different types of distortion models, such as noise, blur, compression artefacts, and colour inconsistencies that are commonly encountered in digital images. Additionally, it investigates the influence of various factors on image quality, including image content, context and viewer preferences. The survey concludes by summarising the key findings and identifying the current trends and future directions in BIQA research. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Biometrics

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