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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Pragatheeswari, B. Mary, Jameela Priya, S. Roshni Dhivyaa, C.R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331540821

Healthcare providers and researchers that work with patients who have cervical cancer face a significant challenge because it is one of the world's most common causes of death. Worldwide, cervical cancer is one of the major causes of death among women according to the World Health Organization (WHO). Pap smear, also called the Pap Test, is used to examine the cells present in the cervix for abnormal cells. However, manual examination of these pap smears is prone to human error. This work aims to explore the use of deep learning models to detect cancer from pap smear images and develop an ensemble model to improve the accuracy. Our proposed model uses pretrained CNN models through transfer learning to extract features, perform feature concatenation followed by dimensionality reduction and hyperparameter tuning, which are then fed into a neural network for multiclass classification. Hyperparameters are fine-tuned with the help of Bayesian optimization to improve the model's performance. Our proposed model has achieved an accuracy of 96.4%, sensitivity of 96.41% and specificity of 96.66%. © 2024 IEEE.

关键词： Dimensionality reduction

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An Unified Approach for Traffic Sign Classificationand Flow Prediction using Deep Learning Technique 15

An Unified Approach for Traffic Sign Classificationand Flow ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, D. Lekhana, Kommareddy Rajeshri, Vigrahala Rushitha, Chennareddygari School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331300579

Efficient and safe traffic management is an important concern in modern urban environments. To address this challenge, we propose an integrated deep learning based solution that unifies traffic sign classification and traffic flow prediction using the LeNet architecture. The seamless fusion of these two critical tasks enables a holistic approach to traffic management, benefiting autonomous vehicles, traffic control systems, and road safety. In this unified approach, LeNet, a seminal convolutional neural network (CNN), serves as the backbone for traffic sign classification. Lenet Network is a very famous kind of configuration of convolutional neural networks that can be used to classify *** this paper, we have used Lenet to classify traffic signs, mainly used for selfdriving cars. Leveraging LeNet's capability to recognize traffic signs with high accuracy, we train it on a comprehensive dataset comprising 43 different classes of traffic signs. This dataset encompasses a wide range of shapes, colors, and conditions, allowing the LeNetbased classifier not only to identify individual traffic signs but also to provide valuable context for downstream traffic flow prediction. Extensive experiments on diverse datasets validate the effectiveness of our unified approach. We demonstrate superior traffic sign classification accuracy using LeNet, surpassing previous state-of- art methods. Additionally, our traffic flow prediction capabilities exhibit impressive accuracy and robustness across various traffic scenarios. Traffic flow prediction helps people for effective route planning so that the people can chose their routes to save and fuel and it helps to reduce traffic congestion. This research represents a significant step toward enhancing traffic management systems efficiency and safety by leveraging deep learning techniques. Our unified approach, combining LeNet- based traffic sign classification with traffic flow prediction, holds great promise for smarter and more r

关键词： Convolutional neural networks

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Selected Replay with Distillation for Continual Semantic Segmentation 3

Selected Replay with Distillation for Continual Semantic Seg...

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3rd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Luo, Yuan Liu, Jun Zeng, Shuqi Hu, Zhuoya Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798331506582

Although deep neural networks have demonstrated exceptional performance in various fields, especially in image processing, they still face some significant unresolved challenges. Catastrophic forgetting is one of the typical challenges commonly encountered in deep learning models. This issue becomes apparent when new training data introduces new categories to adapt to environmental changes, while the old training data is no longer available. As a result, the model tends to forget previously learned knowledge. In recent years, a variety of solutions and incremental learning frameworks have been proposed to address this issue, particularly showing effectiveness in classification and detection tasks. In this paper, we extend the problem to semantic segmentation, which involves pixel-level classification. We conducted an in-depth analysis of the root causes of catastrophic forgetting and identified two key issues arising from the unavailability of old data samples during the new training phase. First, previously learned knowledge is often mistakenly labeled as background in the new training phase, causing a 'background shift' phenomenon. Second, the data distribution of old and new classes becomes highly imbalanced. To tackle these issues, we propose a strategy that combines replay and knowledge distillation to effectively alleviate catastrophic forgetting. © 2024 IEEE.

关键词： Deep reinforcement learning

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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DFNet: Dense fusion convolution neural network for plant leaf disease classification

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Agronomy Journal 2024年第3期116卷 826-838页

作者： Faisal, Muhamad Leu, Jenq-Shiou Avian, Cries Prakosa, Setya Widyawan Köppen, Mario Department of Electronic and Computer Engineering (ECE) National Taiwan University of Science and Technology Taipei City Taiwan Department of Computer Science and Systems Engineering (CSSE) Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka-shi Japan

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single-CNN model, DFNet relies on a double-pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases. © 2023 The Authors. Agronomy Journal © 2023 American Society of Agronomy.

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Formal Verification of Distributed Self-Adaptive Systems Under Environmental Uncertainty 3

Formal Verification of Distributed Self-Adaptive Systems Und...

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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Wang, Zihao Liu, Wei Guo, Kao Xu, Fan School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350365443

Distributed self-adaptive system is a multi-component collaborative system that automatically adjusts its behavior and structure through adaptive mechanisms to maintain system performance and stability in dynamic environments. The adaptability and robustness of these systems depend on the correctness of self-adaptive decisions in real-world scenarios. However, environmental uncertainty makes it difficult for the systems to accurately perceive their operating environments, which may result in misjudgments and consequently erroneous self-adaptive decisions. To address this issue, we propose a Stochastic Timed Automata-based Adaptive Verification method. By collecting data during system operation and using Bayesian methods to transform it into transition probabilities between system states, a stochastic timed automata model is constructed to simulate the system's operational process. Key properties are verified using statistical model checking to ensure the correctness of self-adaptive decisions. The effectiveness and efficiency of the method are demonstrated by verifying the adaptability and robustness of a multi-roadside unit collaborative system in intelligent transportation vehicle-road coordination. © 2024 IEEE.

关键词： Stochastic systems

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LTC-AE: Liquid Time Constant Autoencoders for Time Series Anomaly Detection

LTC-AE: Liquid Time Constant Autoencoders for Time Series An...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Sailesh, Riya Srisailam Madhu Shree, K. Preethan, J. Sivaprakash, S. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350382693

This research introduces a novel class of autoencoders, termed Liquid Time-Constant Autoencoder (LTC-AEs), for anomaly detection in time series data. Anomaly detection in real-time data streams is an extremely crucial task for various domains like healthcare, finance, and cyber security but it comes with a few challenges including missing values, class imbalances and noise. Recent research has proved optimal in addressing these challenges using sequential Autoencoders. Our proposed algorithm having the ability to capture temporal dynamics, learn from irregular sequences, improve interpretability and facilitate scalability, propose to help resolve some problems that delimits the potential of existing Autoencoder models. The experiments conducted in the study are utilized to emphasize the potential of our proposed model in handling multifaceted anomalies. The workflow follows semi-supervised learning to leverage the strengths of both supervised and unsupervised approaches. The utility of LTC-AEs are evaluated on various datasets and performance comparison is done with respect to architectures like Long Short-Term Memory (LSTM-AE), Gated Recurrent Unit (GRU-AE) and Temporal Convolutional Network (TCN-AE) Autoencoders. © 2024 IEEE.

关键词： Network security

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PPaxos: An Adaptive Pull-Based Group Consensus Protocol for Edge Networks 30

PPaxos: An Adaptive Pull-Based Group Consensus Protocol for ...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Hao, Wei Xu, Guangping Wang, Jianshe Wen, Yanyan Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798331515966

Distributed applications are increasingly deployed at the edge to provide low-latency user access. The hierarchical and localized distribution of edge nodes presents challenges for traditional consensus protocols. The push-based replication method, where the leader pushes updates to followers, can become a performance bottleneck in bandwidth-constrained edge environments. Furthermore, the variation in resources and processing capabilities among edge nodes makes it difficult for this replication method to adapt to the specific conditions of each node, thereby affecting system performance. This paper proposes a new consensus protocol, PPaxos, which employs a pull-based replication method. In this approach, follower nodes within a group proactively pull data from relay nodes, and the relay nodes pull data from the leader node. We have designed an adaptive load-aware pull strategy that allows each node to dynamically adjust its pull frequency based on its own state and to dynamically select relay nodes to reduce performance fluctuations and instability factors. Additionally, a cross-group multi-link communication mechanism ensures that the system remains stable even during relay node failures. Experimental results indicate that PPaxos outperforms other protocols in terms of throughput and latency. Specifically, under conditions of limited bandwidth in edge networks, PPaxos achieves approximately a 2 9. 6% improvement in throughput and a 3 4. 8 8% reduction in latency. © 2024 IEEE.

关键词： Bandwidth

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GN: Guided Noise Eliminating Backdoors in Federated Learning

GN: Guided Noise Eliminating Backdoors in Federated Learning

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Huang, Siquan Gao, Ying Chen, Chong Shi, Leyu School of Computer Science and Engineering South China University of Technology China

ISBN: (纸本)9781665410205

Federated learning (FL) trains a model collaboratively but is susceptible to backdoor attacks for its privacy-preserving nature. Existing defenses against backdoor attacks in FL always make specific assumptions on data distributions among clients and are ineffective against sophisticated attacks. Although adding noise mitigates backdoors injected in the model, it simultaneously negatively impacts the main performance. To address the aforementioned issues, we propose a novel defense mechanism, Guided Noise (GN), that eliminates backdoors without compromising the model's main performance. GN achieves this by utilizing conductance to evaluate the importance of neurons and subsequently adding guided noise to suspected backdoor neurons selected by voting, which only disturbs the backdoor task. Extensive experimental evaluations of GN show its significant superiority over traditional noising-based defenses, making it a valuable replacement for existing noising to enhance the robustness of existing defenses against backdoor attacks in FL. © 2024 IEEE.

关键词： Federated learning

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GBERT: A hybrid BERT and GRU model for enhanced encrypted traffic classification 5

GBERT: A hybrid BERT and GRU model for enhanced encrypted tr...

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5th International Conference on computer Communication and Network Security, CCNS 2024

作者： Zhou, Yucheng Yang, Wenjun School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9781510681842

As encrypted traffic becomes increasingly prevalent in modern network communications, effective classification of encrypted traffic is crucial for network security and management. However, existing classification methods face significant challenges when dealing with encrypted traffic, especially in capturing the complex temporal features and contextual information of traffic data. For this reason, this study proposes the GBERT model, a novel hybrid neural network structure that integrates the contextual understanding capabilities of the BERT model with the temporal processing advantages of Gated Recurrent Unit (GRU). Validated across several public encrypted traffic datasets, the GBERT model demonstrates exceptional performance, notably achieving classification accuracies of 99.03% on the ISCX-VPN-Service dataset and 99.32% on the USTC-TFC dataset. Additionally, this study explores various GRU configurations, offering valuable insights for selecting the most effective model architecture for specific tasks. © 2024 SPIE.

关键词： Network security

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