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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Jaisachin, B. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

The security of integrated circuits is crucial due to their wide range of applications in modern gadgets. An unauthorized access (i.e., third-party access) or a security breach in the logic-locked system can pose a great threat to user privacy, thus leading to data theft and misusing the user data. Several logic locking methods have been proposed, but with the advancements of technology, the latest attack techniques can easily crack those existing locking techniques. Thus, in this research paper we have proposed a sensor-based methodology to detect the unauthorized access with microswitch and UART. In a globalized world where the security of logic-locked systems is really important, this paper empathizes with UART-based mechanisms as a practical solution. By focusing on the existing logic locking methodologies, this paper contributes to the ongoing security efforts for a secured logic locked system. © 2025 IEEE.

关键词： computer circuits

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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Resist Denial-of-service Attack and Enhance Network Security with Protocol-independent Packet Processor-Based Two-Layer Firewall 4

Resist Denial-of-service Attack and Enhance Network Security...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Chou, Pin-Lin Wei, Hsin-Wen Lee, Wei-Tsong Tamkang University Department of Electrical and Computer Engineering New Taipei Taiwan

ISBN: (纸本)9798350360721

We introduced a Protocol-independent Packet Processor (P4)-based two-layer firewall with a detection system for identifying denial-of-service (DDoS) attacks. P4-based switch enables real-time packet inspection in the Data Plane by reducing latency associated with packet filtering. The system enhances network security through dynamic traffic-blocking rules trained by machine learning modeling. Using Software-Defined Networking (SDN), the system seamlessly operates with or without current SDN protocols and dynamically adapts to changing threats. The simulation results showed that the proposed approach enhanced network security in the continuously evolving internet environment. © 2024 IEEE.

关键词： Software defined networking

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Research on Internet of Medical Things: Systematic Review, Research Trends and Challenges

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Recent Advances in computer science and Communications 2024年第6期17卷 72-82页

作者： Anand, Dinesh Kaur, Avinash Singh, Manpreet School of Computer Applications Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Applications Guru Nanak Dev Engineering College Punjab Ludhiana India

Introduction: Remote data exchange operations in healthcare are observed, consult-ed, monitored and treated by the Internet of Medical Things (IoMT). It is an extension of the Internet of Things (IoT). Method: At the growing stage of IoT, IoMT is speedily drawing researchers’ interest due to its extensive use in healthcare systems. Smaller and lower-priced wireless devices with various communication protocols have led to the formation of IoMT. Healthcare data is exchanged through wireless communication with IoMT. The margining of IoMT and healthcare can yield multiple benefits in terms of: better quality of life, care services and developing solution/s at low cost. In this article, a systematic literature review has been conducted on IoMT. Results: Authors have thoroughly investigated the different versions of healthcare 1.0, 2.0, 3.0 and 4.0 as proposed by the healthcare industry. Furthermore, the taxonomy of IoMT has been designed and compared with existing surveys. Conclusion: This survey is unique and stands different from the point of view of existing sur-veys. It supports the future of IoMT researchers to bring new insight to their researches. © 2024 Bentham science Publishers.

关键词： Electronic data interchange

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PCI Classification in 5G-NR: Deep Learning Unravels Synchronization Signal Blocks 21

PCI Classification in 5G-NR: Deep Learning Unravels Synchron...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Hossain, Md Rabiul Krunz, Marwan Department of Electrical and Computer Engineering University of Arizona Tucson United States

ISBN: (纸本)9798331519186

Accurate detection of the Physical Cell Identity (PCI) is critical for rapid synchronization and connection establishment in 5G New Radio (5G-NR) systems. This paper introduces a deep learning-based approach for PCI classification, aiming to mitigate the computational complexity associated with traditional methods that rely on decoding the Synchronization Signal Block (SSB). Our approach processes only time-domain baseband samples of the downlink signal, arranged in fixed-length windows. These windows are inputted into a pre-trained Convolutional Neural Network (CNN), which classifies the samples into one of several known PCI values (representing nearby cells) or into an ‘other’ category (representing all non-nearby cells, as well as windows that do not contain SSB samples). Because PCI-related information is contained only in the SSB symbols of a frame, it is possible for an input window to include no or a few SSB samples. Accordingly, in labeling the training set, we use a threshold Ttrain on the fraction of the samples within a window: if Ttrain or more of the samples belong to an SSB of cell with a target PCI value, the true label for that window is set to that PCI value;otherwise, it is set to ‘other.’ A separate threshold Ttest is used for labeling the test windows. We also study another labeling mechanism whereby only samples of the third OFDM symbol in an SSB (which contains the Secondary Synchronization Signal) is used to determine the label. Our analysis considers two commonly used SSB formats that correspond to 15 and 30 kHz subcarrier spacings, respectively. Extensive simulations are conducted which reveal that the proposed classifier can reliably (above 98%) identify the PCI value of a captured signal even under Signal-to-Noise Ratio (SNR) values as low as −10 dB. This performance comes with a significant reduction in computational complexity as it bypasses the need for traditional SSB decoding procedures used for PCI estimation in 5G networks. © 2024 I

关键词： Signal to noise ratio

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Autism Disease Prediction Using Deep Learning & Transfer Learning 2

Autism Disease Prediction Using Deep Learning & Transfer Lea...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Das, Abhishek Alam, Towhidul Hossain, Md. Zihad Lamiya, Habiba Rashid North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798350367461

The neurological disease known as autism spectrum disorder (ASD) is characterized by impaired social interaction, communication issues, and constrained and repetitive behavior patterns. For the benefit of early interventions and support for afflicted persons, timely and accurate ASD prognosis is essential. Deep learning methods have become effective tools for predictive modeling across a range of industries, including healthcare. This study examines the use of deep learning and transfer learning to forecast ASD using a large dataset of clinical and behavioral variables. In this study, the effectiveness of three well-known deep learning architectures VGG16, DenseNet121, and MobileNetv2 in predicting ASDs is compared. A sizable dataset with a variety of ASD-related variables, such as demographic data, medical histories, and behavioral assessments, is used to train the models. To take use of pre-learned weights from models trained on extensive generic image recognition tasks, transfer learning is used. With accuracy rates of 97% apiece, the experimental results show remarkable prediction performance for VGG16 and DenseNet121. These models have significant generalization abilities that make it possible to make reliable predictions for identifying those who are at risk for ASD. In contrast to the other architectures, MobileNetv2 only obtains an accuracy of 73%. The results show that deeper architectures like VGG16 and DenseNet121 capture the rich patterns and fine details of the input data, resulting in more precise predictions. Additionally, thorough investigations are carried out to look into the models' learned representations and pinpoint the primary features that influence ASD prediction. These revelations aid in a better comprehension of the underlying causes and potential biomarkers of ASD. The information gleaned from these studies can direct ongoing research projects and support the creation of individualized interventions and therapies. Overall, the study empha

关键词： Adversarial machine learning

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Utility-Privacy Aware Mobile Diminished Reality Framework for 3D Visual Privacy 6

Utility-Privacy Aware Mobile Diminished Reality Framework fo...

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6th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Tabet, Salam Kayssi, Ayman Elhajj, Imad H. American University of Beirut Department of Electrical and Computer Engineering Beirut Lebanon

ISBN: (纸本)9798350386745

Engaging in immersive technologies has obvious benefits, but the increasing pervasiveness of these technologies, specifically Augmented Reality (AR) and Mixed Reality poses privacy risks, especially since AR cameras are continually capturing information about users' surroundings, including people and personal places. Current privacy-preserving techniques are either designed for a two-dimensional setting where private information is always considered to be in the background, or they struggle to maintain good performance during significant camera motion. Moreover, existing techniques measure the privacy-utility tradeoff by relying on a utility measure specific to the task at hand. We propose a Utility-Privacy Tradeoff Algorithm (UPTA) on top of a real-time mobile privacy-preserving Diminished Reality system that enables the user to obfuscate sensitive objects in a three-dimensional environment. The UPTA adjusts the size and position of the obfuscation region such that it achieves a proper balance between privacy and utility during free camera motion. The algorithm obfuscates the necessary parts of the screen only, to preserve privacy while maintaining a proper level of utility. In addition, we propose a generic utility measure that is inversely proportional to the size of the obfuscation. We demonstrate our framework's ability to achieve a proper balance between privacy and utility, during translational and rotational motion patterns, where UPTA improves the privacy-utility tradeoff figure-of-merit by an order of magnitude (more than 8x) relative to existing methods. © 2024 IEEE.

关键词： Differential privacy

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Torque Pulsations in Variable-Flux Memory Motors

Torque Pulsations in Variable-Flux Memory Motors

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Aljehaimi, Akrem Mohamed Pillay, Pragasen Concordia University Department of Electrical and Computer Engineering Montréal Canada

ISBN: (纸本)9798350376067

This paper investigates the torque pulsations issue during magnetization in variable flux memory motors for traction applications. The paper proposes an algorithm to mitigate these torque pulsations and their resultant speed fluctuations. In normal saliency (Lp > Ld) variable-flux motors, and during demagnetization events, the reluctance torque is positive. This aids the magnet torque, causing an increase in motor speed. In contrast, during remagnetization events, the reluctance torque is negative, which results in an abrupt decrease in torque and motor speed. In this instance, the speed controller increases the q-axis current command to increase the speed to its reference value. This further decreases the motor torque during the pulse time. During remagnetization events, there are two instances where the developed electromagnetic torque goes to zero. This is because, at these two moments, the rotor flux linkage and the stator flux linkage are equal and opposite to each other. A control algorithm based on a q-axis current adjustment is proposed to mitigate the torque pulsations during magnetization processes. The torque pulsation issue and the proposed mitigation algorithm are illustrated via simulation and validated experimentally on a seven-horsepower series-hybrid variable-flux memory motor. © 2024 IEEE.

关键词： Reluctance motors

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A Gain Design Strategy for an Active Damping Controller of a MIDP Permanent Magnet Synchronous Motors Drive System

A Gain Design Strategy for an Active Damping Controller of a...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Nam, Gi-Jung Lee, Hyung-Woo Lee, Kyo-Beum Ajou University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350376067

This paper proposes a gain design strategy for an active damping controller in a mono-inverter dual parallel (MIDP) permanent magnet synchronous motors (PMSMs) drive system. Dual motors are connected in parallel with a single inverter. One motor is chosen as a master motor which is the main controlled motor and the other motor is chosen as a slave motor which is controlled simultaneously with a master motor. Since a single inverter controls two motors in a MIDP system, torque oscillation occurs in the slave motor. Therefore, an active damping controller that regulates torque oscillations is required. This paper provides a design strategy for a gain of an active damping controller. The performance of the proposed design strategy for a MIDP PMSMs drive system is verified by simulation results with two 500-W PMSMs. © 2024 IEEE.

关键词： Damping

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ENTITY-CENTRIC REINFORCEMENT LEARNING FOR OBJECT MANIPULATION FROM PIXELS 12

ENTITY-CENTRIC REINFORCEMENT LEARNING FOR OBJECT MANIPULATIO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Haramati, Dan Daniel, Tal Tamar, Aviv Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Israel

Manipulating objects is a hallmark of human intelligence, and an important task in domains such as robotics. In principle, Reinforcement Learning (RL) offers a general approach to learn object manipulation. In practice, however, domains with more than a few objects are difficult for RL agents due to the curse of dimensionality, especially when learning from raw image observations. In this work we propose a structured approach for visual RL that is suitable for representing multiple objects and their interaction, and use it to learn goal-conditioned manipulation of several objects. Key to our method is the ability to handle goals with dependencies between the objects (e.g., moving objects in a certain order). We further relate our architecture to the generalization capability of the trained agent, based on a theoretical result for compositional generalization, and demonstrate agents that learn with 3 objects but generalize to similar tasks with over 10 objects. Videos and code are available on the project website: https://***/view/entity-centric-rl. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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