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arXiv 2024年

作者： Yuan, Yachao Huang, Yu Yuan, Yali Wang, Jin School of Future Science and Engineering Soochow University Jiangsu Suzhou China Independent Researcher Guangxi Nanning China School of Cyber Science and Engineering Southeast University Jiangsu Nanjing China the Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Bangladesh

The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats, thus developing Anomaly Detection Systems (ADSs) that can adapt to evolving or new attacks is critical. Previous studies primarily focused on offline unsupervised learning methods to safeguard ADSs, which is not applicable in practical real-world applications. Besides, most of them strongly rely on assumptions of known legitimates and fail to satisfy the interpretable requirements in security applications, creating barriers to the adoption in practice. In this paper, we design Adaptive NAD, a general framework to improve and interpret online and Adaptive unsupervised network Anomaly Detection in security domains. An interpretable two-layer anomaly detection strategy is proposed to generate reliable high-confidence pseudo-labels. Then, an online learning scheme is introduced to update Adaptive NAD by a novel threshold calculation technique to adapt to new threats. Experimental results demonstrate that Adaptive NAD achieves more than 5.4%, 23.0%, and 3.2% improvements in SPAUC compared with state-of-the-art solutions on the CIC-Darknet2020, CIC-DoHBrw-2020, and Edge-IIoTset datasets, respectively. The code is released at https://***/MyLearnCodeSpace/Adaptive-NAD. © 2024, CC BY-SA.

关键词： Contrastive Learning

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LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising

arXiv

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arXiv 2023年

作者： Wang, Dayang Xu, Yongshun Han, Shuo Wu, Zhan Zhou, Li Morovati, Bahareh Yu, Hengyong The Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States The Laboratory of Image Science and Technology Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In the fields of computer vision and natural language processing, masked autoencoders (MAE) have been recognized as an effective label-free self-pretraining method for transformers, due to their exceptional feature representation ability. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE’s robustness and generability across a variety of noise levels. Experiments results show that the proposed LoMAE can enhance the transformer’s denoising performance and greatly relieve the dependence on the ground truth clean data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels. © 2023, CC BY.

关键词： Image quality

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Improving User QoE via Joint Trajectory and Resource Optimization in Multi-UAV Assisted MEC

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IEEE Transactions on Services Computing 2025年

作者： Gao, Yang Tao, Jun Xu, Yifan Wang, Zuyan Gao, Yu Wang, Meiling Hainan University School of Cyberspace Security Haikou570228 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Southeast University School of Cyber Science and Engineering Nanjing211189 China ZTE Communications Co. Ltd Nanjing210012 China

As a promising network architecture, Mobile Edge Computing (MEC), has been proven that can effectively reduce the end-to-end latency and the energy consumption. The Unmanned Aerial Vehicle (UAV) assisted MEC network, where the UAV can provide the computation offloading services for the mobile users, can further alleviate the huge deployment cost of static edge servers. However, it remains unsolved how multiple cooperative flying UAVs serve the ground users, especially considering that these UAVs may share the same wireless channel and can communicate with the users while flying. In this paper, we first propose the Age of Task (AoT) metric to measure the quality of experience, and then formulate the joint optimization problem to minimize the worst AoT among all the users. Based on the block coordinate descent (BCD) method, this problem is transformed into three non-convex programming sub-problems (i.e., the UAV-user association sub-problem, the UAV trajectory planning sub-problem and the transmit power optimization sub-problem). Specifically, the successive convex approximation (SCA) technique is exploited iteratively to deal with the non-convexity in the UAV trajectory and transmit power optimization. Numerical results show that the proposed scheme outperforms the benchmark offloading schemes in terms of AoT. © 2008-2012 IEEE.

关键词： Drones

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Enhancing Unknown Encrypted Traffic Clustering with Self-Supervised Learning

Enhancing Unknown Encrypted Traffic Clustering with Self-Sup...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ke Ding Xia Zhou Xiaoyan Hu Guang Cheng Ruidong Li Hua Wu School of Cyber Science & Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Jiangsu Province Engineering Research Center of Security for Ubiquitous Network Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University) China Institute of Science and Engineering Kanazawa University Kanazawa Japan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Many malicious attacks are launched through encrypted traffic from unknown proprietary network protocols. Timely identification of such malicious unknown encrypted traffic is essential for the defense. However, it is challenging to acquire labels for unknown protocols in the context of encrypted traffic. Due to the lack of prior knowledge, unsupervised learning is adopted to cluster unknown encrypted traffic. The existing unsupervised encrypted traffic clustering methods do not customize feature extraction and representation of unknown encrypted traffic, resulting in imperfect clustering results. This work innovatively proposes BiFR-SSL to accurately cluster unknown encrypted traffic without prior knowledge. BiFRSSL extracts features of each unknown encrypted bidirectional network flow based on the lengths, arrival time, and directions of packets within the flow to construct a Bidirectional Flowpic Representation (BiFR). Subsequently, it exploits Self-Supervised Learning (SSL) to pre-train a feature extractor that produces feature representations from BiFRs, guaranteeing the closeness of encrypted traffic flows from the same protocol in the representation space. Finally, it clusters unknown encrypted traffic based on their feature representations generated by the pre-trained feature extractor. Our experimental studies demonstrate that BiFR-SSL can effectively cluster encrypted traffic of unknown protocols and outperforms state-of-the-art methods.

关键词： Protocols Clustering methods Clustering algorithms Self-supervised learning Telecommunication traffic Feature extraction Data augmentation Cryptography Global communication Unsupervised learning

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UAV Swarm Path Planning for Sensor Data Collection via Double Pre-Partitioned Deep Q-network

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IEEE Sensors Journal 2025年

作者： Zhang, Heng Gao, Yuan Wang, Xin He, Yunpeng Yang, Ming Wu, Xiaoming Yang, Chaoqun Cao, Xianghui Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China Jiangsu Ocean University School of Computer Engineering Lianyungang222000 China Southeast University School of Automation Nanjing210096 China

Collision-free path planning is a significant challenge in deploying unmanned aerial vehicle (UAV) swarms for sensor data collection tasks, particularly in uncertain environments. This paper addresses the problem of designing flight strategies for energy-constrained UAV swarms using deep reinforcement learning. We propose a locally-aware double pre-partitioned deep Q-network (DPPDQN) for flight planning of multiple UAVs in large-scale uncertain industrial environments. This algorithm aims to collect data from ground sensors with communication constraints while avoiding mutual collisions. In the proposed algorithm, we first introduce a node pre-partitioning scheme using an improved clustering algorithm, which efficiently reduces the number of access points for UAVs through effective data aggregation. Subsequently, we employ an area pre-partitioning algorithm that segments the detection area into several subregions based on the number of UAVs available for operation, thereby preventing collisions among the UAV swarm during task execution. Finally, we propose a deep Q-network algorithm incorporating a convolutional neural network, which utilizes the local perceptions of UAVs as state inputs to address the 'curse of dimensionality"occurring in large-scale environments. Systematic simulation experiments validate the effectiveness and advantages of the proposed method. © 2025 IEEE.

关键词： Convolutional neural networks

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Prior Knowledge Enhanced Topic Model For Research Topic Evolution Analysis

Prior Knowledge Enhanced Topic Model For Research Topic Evol...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Zhongyuan Kang Lu Zhang Zheng Chen Haiting Zhu Gaofeng He Mingwei Tang School of Computer Science Nanjing Audit University Nanjing China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China School of Engineering Audit Nanjing Audit University Nanjing China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Extracting and analyzing topics from the scientific literature is crucial for identifying the research hotspots, under-standing the developmental trajectory, and further revealing and predicting the evolutionary trends of a research field. Existing topic models like LDA and ETM can effectively extract topics from large text corpora. However, they struggle with incor-porating the domain-specific prior knowledge that is inherent and can be easily obtained in the scientific literature (e.g., the keywords in the paper), causing weekly targeted but over generic topic extraction and representation. To address this problem, we propose a Prior Attention Topic Model (PATM), which leverages inherent keywords in the scientific literature as prior knowledge for more targeted topic extracting and representation. Specifically, we first construct a prior attention weight matrix to guide the document embedding to pay more attention on the keywords, and then, a multi-view Variational Autoencoder (VAE) is utilized to separately encode different literature types, combining their unique characteristics while sharing one latent space. At last, the topic-word probability martix learned from the VAE are regarded as the topic representations. The experimental results and case study demonstrate that the proposed PATM is effective in extracting topics in the scientific literature, including papers and patents, and by analyzing these topics, we clearly reveal the thematic evolution of the research about emerging information technologies empowering auditing.

关键词： Patents Analytical models Solid modeling Big Data Solids Market research Data models Trajectory Data mining information technology

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On accuracy rate of community detection and pairing in mobile social network

On accuracy rate of community detection and pairing in mobil...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Jinbin Tu Qing Li Yun Wang School of Computer Science and Engineering Southeast University Key Lab of Computer Network and Information Integration Nanjing MOE China

Mobile Social network(MSN) is an opportunity network that considers the social attributes of nodes, and also uses the ”store-carry-forward” model to carry out data transfer between nodes. The community nature of nodes is an important reference in MSN to select messaging path. In this paper, a new community cluster problem based on our observed pickup strategies for car-hailing is proposed, which is involving community detection and community pairing. Then we formulate it by using T-Sorokin mobility model. The Cluster-First algorithm and Pair-First algorithm are proposed based on the defined community dependency metric to maximize the accuracy rate. Simulation experimental results show that both CFA and PFA can effectively solve this problem with an accuracy rate of over 70%. And it is concluded that cluster-first is superior to pair-first for this considered problem.

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TSdetector: Temporal-Spatial Self-correction Collaborative Learning for Colonoscopy Video Detection

arXiv

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arXiv 2024年

作者： Wang, Kai-Ni Wang, Haolin Zhou, Guang-Quan Wang, Yangang Yang, Ling Chen, Yang Li, Shuo School of Biological Science and Medical Engineering Southeast University Nanjing China Jiangsu Key Laboratory of Biomaterials and Devices Southeast University Nanjing China Laboratory of Image Science and Technology Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China TUGE Healthcare Nanjing China Institute of Medical Technology Peking University Health Science Center China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University United States

CNN-based object detection models that strike a balance between performance and speed have been gradually used in polyp detection tasks. Nevertheless, accurately locating polyps within complex colonoscopy video scenes remains challenging since existing methods ignore two key issues: intra-sequence distribution heterogeneity and precision-confidence discrepancy. To address these challenges, we propose a novel Temporal-Spatial self-correction detector (TSdetector), which first integrates temporal-level consistency learning and spatial-level reliability learning to detect objects continuously. Technically, we first propose a global temporal-aware convolution, assembling the preceding information to dynamically guide the current convolution kernel to focus on global features between sequences. In addition, we designed a hierarchical queue integration mechanism to combine multi-temporal features through a progressive accumulation manner, fully leveraging contextual consistency information together with retaining long-sequence-dependency features. Meanwhile, at the spatial level, we advance a position-aware clustering to explore the spatial relationships among candidate boxes for recalibrating prediction confidence adaptively, thus eliminating redundant bounding boxes efficiently. The experimental results on three publicly available polyp video dataset show that TSdetector achieves the highest polyp detection rate and outperforms other state-of-the-art methods. The code can be available at https://***/soleilssss/TSdetector. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Delving into the Estimation Shift of Batch Normalization in a network

arXiv

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arXiv 2022年

作者： Huang, Lei Zhou, Yi Wang, Tian Luo, Jie Liu, Xianglong SKLSDE Institute of Artificial Intelligence Beihang University Beijing China MOE Key Laboratory of Computer Network and Information Integration Southeast University China

Batch normalization (BN) is a milestone technique in deep learning. It normalizes the activation using mini-batch statistics during training but the estimated population statistics during inference. This paper focuses on investigating the estimation of population statistics. We define the estimation shift magnitude of BN to quantitatively measure the difference between its estimated population statistics and expected ones. Our primary observation is that the estimation shift can be accumulated due to the stack of BN in a network, which has detriment effects for the test performance. We further find a batch-free normalization (BFN) can block such an accumulation of estimation shift. These observations motivate our design of XBNBlock that replace one BN with BFN in the bottleneck block of residual-style networks. Experiments on the ImageNet and COCO benchmarks show that XBNBlock consistently improves the performance of different architectures, including ResNet and ResNeXt, by a significant margin and seems to be more robust to distribution shift. Copyright © 2022, The Authors. All rights reserved.

关键词： Population statistics

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Deep Video Cascade Clustering from Video Structures

SSRN

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SSRN 2024年

作者： Wang, Zihua Zhu, Hongyuan Mi, Siya Zhang, Yu Geng, Xin School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China A*STAR Singapore School of Cyber Science and Engineering Southeast University Nanjing211189 China Purple Mountain Laboratories Nanjing211189 China

Video clustering is a critical unsupervised learning task where, unlike in supervised video classification, category labels are not *** main challenge lies in learning an effective video representation without annotations to group similar *** this paper, we introduce a novel unsupervised video learning paradigm called Deep Video Cascade Clustering ($\text{DVC}^2$).Unlike existing image-based clustering methods, $\text{DVC}^2$ first learns an initial video representation through frame clustering, which serves as guidance. Subsequently, the video clustering results are aligned with both the long-term and short-term video structures and their nearest *** conduct a comprehensive evaluation of $\text{DVC}^2$ on benchmark video datasets, including UCF101 and Kinetic-400, and achieve state-of-the-art video clustering results. Additionally, we evaluate the performance of combining self-supervised learning with K-means *** findings validate the effectiveness of $\text{DVC}^2$ in unsupervised video clustering scenarios. © 2024, The Authors. All rights reserved.

关键词： Unsupervised learning

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