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34th Annual International Conference on Collaborative Advances in Software and Computing, CASCON 2024

作者： Gaudreault, Jean-Gabriel Branco, Paula School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada

ISBN: (纸本)9798331504830

Intrusion Detection Systems (IDSs) are an essential part of modern-day cybersecurity defenses, enabling the detection of potential malicious activities within network and computer systems. Although multiple techniques have been employed to implement IDSs, supervised machine learning approaches have historically proven particularly effective due to their capacity to leverage large amounts of data and maintain a generally low false alarm rate. However, a significant limitation of these approaches is their inability to detect novel threats, such as zero-day attacks, that were not present in the training data. In this study, we introduce a novel supervised approach based on binary classifiers that can detect these previously unseen attacks while also maintaining a high detection rate and low false alarm rate through the use of a filtering mechanism. We empirically demonstrate the effectiveness of our proposed technique on multiple intrusion detection datasets, showing that it is able to detect unseen attacks and perform multiclass classification while outperforming traditional supervised methods. © 2024 IEEE.

关键词： Network intrusion

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Near-field Source Localization in 3-D Using Two Parallel Centrally Symmetric Unfold Coprime Array

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4738-4749页

作者： Chen, Hua Li, Junjie Yang, Songjie Liu, Wei Eldar, Yonina C. Yuen, Chau Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu610054 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel Nanyang Technological University School of Electrical and Electronics Engineering Singapore639798 Singapore

Most near-field (NF) localization algorithms cannot deal with the underdetermined case, while those which can are computationally expensive due to employment of fourth-order cumulants. In this work, a low-complexity solution is provided for underdetermined three-dimensional (3-D) NF localization, by employing second-order statistics with a tailored array configuration named two parallel centrally symmetric unfold coprime (TPSC) array. Its implementation can be divided into three stages. Firstly, the proposed algorithm constructs two cross-correlation matrices based on the received array data, which eliminates the non-linear range-related information of NF signals. Secondly, covariance and vectorization operations are applied to these two cross-correlation matrices to form a virtual array with extended aperture. Finally, the two-dimensional (2-D) angle parameters are estimated by the sparse and parametric approach (SPA) and a phase retrieval operation, and then the one-dimensional (1-D) range parameter is achieved by the multiple signal classification (MUSIC) algorithm. One specific feature is that the estimated angle and range parameters are matched automatically. An analysis of the properties of the TPSC array is provided, and an optimal parameter configuration is derived, given that the total number of array elements is fixed. Simulation results demonstrate that the designed TPSC array can achieve underdetermined 3-D NF localization, and deliver enhanced estimation capabilities, surpassing those of established algorithms. © 2002-2012 IEEE.

关键词： Multiple signal classification

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A Grid-Based Random Erasing Approach for Enhanced Image Classification 4

A Grid-Based Random Erasing Approach for Enhanced Image Clas...

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4th IEEE International Conference on Data science and computer Application, ICDSCA 2024

作者： Li, Qingyi School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (纸本)9798350368239

In modern image classification tasks, the data is insufficient due to time or money cost constraints, which affects the generalization and accuracy of the model. This paper proposes a random erasure method based on gridded images, aiming to expand the dataset to improve the generalization ability and classification accuracy of the model while reducing the damage to the original structure of the image caused by continuous erasure. This paper uses YOLOv8n-cls, YOLOv8mcls, and ResNet50 as models and selects CIFAR-10, CIFAR-100, FER2013, and mini-ImageNet as datasets for comparative experiments. The Top-1 accuracy, Top-5 accuracy, and loss function curves on the validation set are used to analyze the effect of random erasing on each model's performance on various datasets. The findings demonstrate that lightweight models greatly increase accuracy on simple datasets like CIFAR-10. However, to effectively use the enhancing effect of random erasing on complicated datasets, more sophisticated models are required. This study shows the impact of random erasing based on gridded images on improving the generalization ability and classification accuracy of the model in image classification tasks. It points out the limitations of this method and potential future optimization directions to better handle more complex classification tasks. ©2024 IEEE.

关键词： Image classification

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JPEG Reversible Data Hiding via Block Sorting Optimization and Dynamic Iterative Histogram Modification

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IEEE Transactions on Multimedia 2025年 27卷 3729-3743页

作者： Li, Fengyong Wang, Qiankuan Cheng, Hang Zhang, Xinpeng Qin, Chuan Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Fuzhou University School of Mathematics and Statistics Fuzhou Fujian350108 China Fudan University School of Computer Science Shanghai200433 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

JPEG reversible data hiding (RDH) refers to covert communication technology to accurately extract secret data while also perfectly recovering the original JPEG image. With the development of cloud services, a large number of private JPEG images can be efficiently managed in cloud platforms by embedding user ID or authentication labels. Nevertheless, data embedding operations may inadvertently disrupt the encoding sequence of the original JPEG image, resulting in severe distortion of the host image when it is re-compressed to JPEG format. To address this problem, this paper proposes a new JPEG RDH scheme based on block sorting optimization and dynamic iterative histogram modification. We firstly design a block ordering optimization strategy by combining the number of zero coefficients and the quantization table values of non-zero coefficients in a DCT block. Subsequently, a dynamic iterative histogram modification scheme is proposed by considering the local features and embedding capability of histograms generated from different texture images. According to the given payloads, we introduce different parameters to control the iterations of two-dimensional histogram and then adaptively generate the optimal histogram modification mapping, which can realize low JPEG file size increments by guaranteeing most of the AC coefficients unchanged as much as possible. Numerous experiments have shown that our scheme can achieve an effective balance among embedding capacity, visual quality, file size increment, computational complexity, and outperforms the state-of-the-arts in terms of the above metrics. © 1999-2012 IEEE.

关键词： Embeddings

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A Custom Generative AI Chatbot as a Course Resource

A Custom Generative AI Chatbot as a Course Resource

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2024 ASEE Annual Conference and Exposition

作者： Ai, Yutong Baveja, Maya Girdhar, Akanksha O'Dell, Melina Deorio, Andrew Department of Electrical Engineering and Computer Science University of Michigan United States

Generative AI (GenAI) chatbots have taken the world by storm with their ability to summarize information and produce complex natural language *** the same time, the demand for computer science education has grown *** developed a custom GenAI chatbot and released it to over 700 students to assist with a course programming *** tool was trained on the project specification, lecture content, lab materials, and past course forum posts relevant to the *** course is a high-enrollment upper-level computer science elective at a large public research *** project is a simple distributed system using processes, threads, and *** goal of the tool was to provide an additional course resource that supplies valuable information to students comparable to advice they would receive from course *** measured the effectiveness of our tool with a student survey, evaluation by course instructors, and comparison with a state-of-the-art general purpose *** overall survey data indicated high rates of correctness and helpfulness in the Bot *** found that hallucination was not common, and most incorrect responses were identifiable by *** Bot also performed better than general purpose bots for project-specific *** experience can provide insights for faculty using GenAI to assist students in their courses.A customized chatbot can be helpful to students and augment traditional course resources. © American Society for engineering Education, 2024.

关键词： Students

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Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach 18

Contactless Fingerprint Biometric Anti-Spoofing: An Unsuperv...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Adami, Banafsheh Hosseinzadehketilateh, Mohammad Reza Karimian, Nima West Virginia University Computer Science and Electrical Engineering United States

ISBN: (纸本)9798350364132

Contactless fingerprint recognition offers a higher level of user comfort and addresses hygiene concerns more effectively. However, it is also more vulnerable to presentation attacks, such as photo-paper, paper printout, and various display attacks, making it more challenging to implement in biometric systems compared to contact-based modalities. Limited research has been conducted on presentation attacks in contactless fingerprint systems, and these studies have encountered challenges in terms of generalization and scalability since both bonafide samples and presentation attacks are utilized during the training model. Although this approach appears promising, it lacks the ability to handle unseen attacks, which is a crucial factor for developing PAD methods that can generalize effectively. We introduced an innovative anti-spoofing approach that combines an unsupervised autoencoder with a convolutional block attention module to address the limitations of existing methods. Our model is trained exclusively on bonafide images without exposure to any spoofed samples during the training phase. It is then evaluated against various types of presentation attack images in the testing phase. The scheme we proposed has achieved an average BPCER of 0.96% with an APCER of 1.6% for presentation attacks involving various types of spoofed samples. © 2024 IEEE.

关键词： Biometrics

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Closed form Solution of Moving Source Localization Using TDOA and FDOA Measurements Under Sensor Location Errors 16

Closed form Solution of Moving Source Localization Using TDO...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Sheng, Menglei Li, Youming Zhou, Yapeng Cai, Wanyuan Qi, Qinke Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

The moving source localization is very sensitive to the knowledge of sensor locations. In this paper, we propose a two-stage closed-form method for locating a moving source by using Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) measurements in the presence of random errors in sensor locations. In the first stage, we establish a series of pseudo-linear equations by introducing the auxiliary variables and obtain an initial location estimation based on the bias reduced criteria and the Lagrange multiplier theory. In the second stage,we propose a modified weighted least squares method to obtain the final estimation by exploring the relationship between the auxiliary variables and the moving source location and velocity. The proposed method is in closed form and therefore computationally efficient as well as free from the divergence problem unlike iterative techniques. Simulations show the better performance of the proposed method. © 2024 IEEE.

关键词： Iterative methods

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Ternary Memory Composed of Ferroelectric Field-Effect Transistor Based on Dynamic Logic 3

Ternary Memory Composed of Ferroelectric Field-Effect Transi...

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3rd International Conference on Electronics and Information Technology, EIT 2024

作者： Dong, Zhichao Xia, Yinshui Li, Yang Yang, Zhaohui Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350369151

Ternary Content Addressable Memory (TCAM) is widely used in development areas such as network routers and machine learning, which are receiving significant attention. Ferroelectric field-effect transistors (FeFETs) are among the most promising devices currently being researched in the field of advanced complementary metal-oxide-semiconductor (CMOS) technology. FeFETs also possess a unique hysteresis in the Ids-Vgs characteristics, which allows the device to function as both a switch and a non-volatile (NV) memory element. Traditional TCAM has the problems of VTH drop or search disturbance. We leverage FeFET property to construct a novel TCAM based on dynamic logic that can be integrated as efficient and compact logic elements into various levels of the memory hierarchy. Due to the slow trend of performance scaling based on Moore's Law and the fact that many computational tasks are now more data-centric than compute-centric, it is highly necessary to design efficient and compact logic elements based on emerging technologies to enhance performance and conserve energy consumption. © 2024 IEEE.

关键词： MOS devices

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Energy Detection-Based Spectrum Sensing over Shadowed UAV-to-Ground Channels 100

Energy Detection-Based Spectrum Sensing over Shadowed UAV-to...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Polus, Remon D'Amours, Claude University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (纸本)9798331517786

Recent research has focused on exploring uncrewed aerial vehicle (UAV)-to-ground communication channels, leading to the development of new fading channel models that incorporate both fading and shadowing phenomena. It is well established that the performance of sensing techniques is significantly impacted by fading effects. Therefore, this paper examines the performance of energy detection-based spectrum sensing in shadowed UAV-to-ground channels by deriving novel analytical expressions for the average detection probability. Subsequently, These expressions are then extended to include collaborative spectrum sensing. The analysis delves into the complementary receiver operating characteristics (ROC), taking into account factors such as average signal-to-noise ratio (SNR), multipath fading, shadowing, and the number of collaborating sensing nodes. The analytical results are validated by comparing them with those obtained through Monte Carlo simulations. © 2024 IEEE.

关键词： Antenna grounds

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Few-Shot Aviation Materials Surface Defection Classification: From Non-Standardized Image Acquisitions 4

Few-Shot Aviation Materials Surface Defection Classification...

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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Wu, Zewei Peng, Chengbin Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350364798

Defect classification in the aviation industry plays a crucial role in manufacturing. However, in non-standardized captured scenes, the object to be inspected and the background are often complex and variable, e.g., lighting, angle, occlusion, etc., which can affect the core features of the image data. Therefore, achieving accurate classification using a limited number of defect samples from products photographed in non-standardized environments has emerged as a challenging yet promising research focus. To address this issue, this study proposes a learning approach that can be trained using standardized industrial defect datasets to detect defects in few-shot, non-standardized industrial defect production. Specifically, this approach initially utilizes the knowledge acquired from a standardized pre-training dataset to crop the data within the query set. Subsequently, an embedding extraction is employed to obtain feature embedding and classification results. Our practice demonstrates a performance improvement of roughly 5% on non-standard datasets compared to few-shot classification methods. © 2024 IEEE.

关键词： Zero-shot learning

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