Suchergebnisse - "Computing methodologies Machine learning Learning paradigms Unsupervised learning"

Twin Graph-Based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System von Huang, Jun, Yang, Yang, Yu, Hang, Li, Jianguo, Zheng, Xiao

“… Microservice architecture has sprung up over recent years for managing enterprise applications, due to its ability to independently deploy and scale services …”
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Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining von Shen, Weijun, Li, Yanhui, Chen, Lin, Han, Yuanlei, Zhou, Yuming, Xu, Baowen

“… With the increasing application of deep learning (DL) models in many safety-critical scenarios, effective and efficient DL testing techniques are much in …”
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Weakly-Supervised Log-Based Anomaly Detection with Inexact Labels via Multi-Instance Learning von He, Minghua, Jia, Tong, Duan, Chiming, Cai, Huaqian, Li, Ying, Huang, Gang

“… Log-based anomaly detection is essential for maintaining software availability. However, existing log-based anomaly detection approaches heavily rely on …”
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Enabling On-Device Self-Supervised Contrastive Learning with Selective Data Contrast von Wu, Yawen, Wang, Zhepeng, Zeng, Dewen, Shi, Yiyu, Hu, Jingtong

“… After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy. Contrastive …”
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Segmented Angular Pre-Processing for Accurate and Efficient In-Memory Vector Similarity Search von Huang, Chi-Tse, Wang, Jen-Chieh, Cheng, Hsiang-Yun, Wu, An-Yeu Andy

“… Vector similarity search (VSS) is a fundamental operation in modern AI applications, including few-shot learning (FSL) and approximate nearest neighbor search …”
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LogOnline: A Semi-Supervised Log-Based Anomaly Detector Aided with Online Learning Mechanism von Wang, Xuheng, Song, Jiaxing, Zhang, Xu, Tang, Junshu, Gao, Weihe, Lin, Qingwei

“… Logs are prevalent in modern cloud systems and serve as a valuable source of information for system maintenance. Over the years, a lot of research and …”
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Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion von Lee, Cheryl, Yang, Tianyi, Chen, Zhuangbin, Su, Yuxin, Lyu, Michael R.

“… Ensuring the reliability and user satisfaction of cloud services necessitates prompt anomaly detection followed by diagnosis. Existing techniques for anomaly …”
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AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection von Huo, Yintong, Li, Yichen, Su, Yuxin, He, Pinjia, Xie, Zifan, Lyu, Michael R.

“… The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have …”
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1+1>2: Integrating Deep Code Behaviors with Metadata Features for Malicious PyPI Package Detection von Sun, Xiaobing, Gao, Xingan, Cao, Sicong, Bo, Lili, Wu, Xiaoxue, Huang, Kaifeng

“… PyPI, the official package registry for Python, has seen a surge in the number of malicious package uploads in recent years. Prior studies have demonstrated …”
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MalWuKong: Towards Fast, Accurate, and Multilingual Detection of Malicious Code Poisoning in OSS Supply Chains von Li, Ningke, Wang, Shenao, Feng, Mingxi, Wang, Kailong, Wang, Meizhen, Wang, Haoyu

“… In the face of increased threats within software registries and management systems, we address the critical need for effective malicious code detection. In …”
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Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders von Ludmir, Jason Zev, Rebello, Sophia, Ruiz, Jacob, Patel, Tirthak

“… Detecting mission-critical anomalous events and data is a crucial challenge across various industries, including finance, healthcare, and energy. Quantum …”
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Detecting and Explaining Anomalies Caused by Web Tamper Attacks via Building Consistency-based Normality von Liao, Yifan, Xu, Ming, Lin, Yun, Teoh, Xiwen, Xie, Xiaofei, Feng, Ruitao, Liauw, Frank, Zhang, Hongyu, Dong, Jin Song

“… Web applications are crucial infrastructures in the modern society, which have high demand of reliability and security. However, their frontend can be …”
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BDefects4NN: A Backdoor Defect Database for Controlled Localization Studies in Neural Networks von Xiao, Yisong, Liu, Aishan, Zhang, Xinwei, Zhang, Tianyuan, Li, Tianlin, Liang, Siyuan, Liu, Xianglong, Liu, Yang, Tao, Dacheng

“… Pre-trained large deep learning models are now serving as the dominant component for downstream middleware users and have revolutionized the learning paradigm, …”
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How to Accurately and Privately Identify Anomalies von Asif, Hafiz, Papakonstantinou, Periklis A, Vaidya, Jaideep

“… Identifying anomalies in data is central to the advancement of science, national security, and finance. However, privacy concerns restrict our ability to …”
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CND-IDS: Continual Novelty Detection for Intrusion Detection Systems von Fuhrman, Sean, Gungor, Onat, Rosing, Tajana

“… Intrusion detection systems (IDS) play a crucial role in IoT and network security by monitoring system data and alerting to suspicious activities. Machine …”
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UniGenCoder: Merging SEQ2SEQ and SEQ2TREE Paradigms for Unified Code Generation von Shao, Liangying, Yan, Yanfu, Poshyvanyk, Denys, Su, Jinsong

“… Deep learning-based code generation has completely transformed the way developers write programs today. Existing approaches to code generation have focused …”
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Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation von Cheng, Jinglei, Zhou, Ruilin, Gan, Yuhang, Qian, Chen, Liu, Junyu

“… We present a quantum-inspired algorithm that utilizes Quantum Hamiltonian Descent (QHD) for efficient community detection. Our approach reformulates the …”
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A vision on a methodology for the application of an Intrusion Detection System for satellites von Gios, Sebastien, Van Ouytsel, Charles-Henry Bertrand, Caribe, Mark Diamantino, Legay, Axel

“… The security of satellites has become critical in recent years due to their important role in modern society. However, numerous challenges, including limited …”
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TacDroid: Detection of Illicit Apps Through Hybrid Analysis of UI-Based Transition Graphs von Lu, Yanchen, Lin, Hongyu, He, Zehua, Xu, Haitao, Li, Zhao, Hao, Shuai, Wang, Liu, Wang, Haoyu, Ren, Kui

“… Illicit apps have emerged as a thriving underground industry, driven by their substantial profitability. These apps either offer users restricted services …”
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Towards More Trustworthy Deep Code Models by Enabling Out-of-Distribution Detection von Yan, Yanfu, Duong, Viet, Shao, Huajie, Poshyvanyk, Denys

“… Numerous machine learning (ML) models have been developed, including those for software engineering (SE) tasks, under the assumption that training and testing …”
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