TransKS: An Anomaly Detection Method for Telecommunication Networks Based on Deep Learning by Jiahuan Zheng, Dongdong Feng, Zhiming Yang, Yong Xiang, Haiping Zhang, Siyao Li

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LSTM-Based Stacked Autoencoders for Early Anomaly Detection in Induction Heating Systems by Mohammed H. Qais, Seema Kewat, Ka Hong Loo, Cheung-Ming Lai, Aldous Leung

Subjects: “…anomaly detection…”
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GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos by Vladimir Monakhov, Vajira Thambawita, Pål Halvorsen, Michael A. Riegler

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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data by Syahirah Suboh, Izzatdin Abdul Aziz, Shazlyn Milleana Shaharudin, Saidatul Akmar Ismail, Hairulnizam Mahdin

Subjects: “…anomaly detection…”
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RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection by Ayyaz-Ul-Haq Qureshi, Hadi Larijani, Nhamoinesu Mtetwa, Abbas Javed, Jawad Ahmad

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Incremental Learning-Based Algorithm for Anomaly Detection Using Computed Tomography Data by Hossam A. Gabbar, Oluwabukola Grace Adegboro, Abderrazak Chahid, Jing Ren

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A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection by Alessio Staffini, Thomas Svensson, Ung-il Chung, Akiko Kishi Svensson

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Enhanced Anomaly Detection in Manufacturing Processes Through Hybrid Deep Learning Techniques by Kyung Sung Lee, Seong Beom Kim, Hee-Woong Kim

Subjects: “…Anomaly detection…”
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Application of Knowledge Graph Technology with Integrated Feature Data in Spacecraft Anomaly Detection by Xiaojian Yi, Peizheng Huang, Shangjie Che

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Machine learning for Internet of things anomaly detection under low-quality data by Shangbin Han, Qianhong Wu, Yang Yang

“…With the popularization of Internet of things, its network security has aroused widespread concern. Anomaly detection is one of the important technologies to protect network security. …”
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Network Anomaly Detection Based on Wavelet Fuzzy Neural Network with Modified QPSO by Ruhui Ma, Yuan Liu, Xing Lin

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Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency by Shashank Chauhan, Sudong Lee

Subjects: “…Anomaly detection…”
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An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest by Junxiang Chen, Jilin Zhang, Ruixiang Qian, Junfeng Yuan, Yongjian Ren

Subjects: “…anomaly detection…”
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Anomaly Detection and Early Warning Model for Latency in Private 5G Networks by Jingyuan Han, Tao Liu, Jingye Ma, Yi Zhou, Xin Zeng, Ying Xu

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A Method for Anomaly Detection in Big Data based on Support Vector Machine by Masoud Harimi, Mohammad Javad Shayegan

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Anomaly Detection in Power Generation Plants Using Machine Learning and Neural Networks by Jecinta Mulongo, Marcellin Atemkeng, Theophilus Ansah-Narh, Rockefeller Rockefeller, Gabin Maxime Nguegnang, Marco Andrea Garuti

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An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks by Qin Yu, Lyu Jibin, Lirui Jiang

“…Traffic anomaly detection is emerging as a necessary component as wireless networks gain popularity. …”
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Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model by Xueshan Wu, Song Huang, Min Li, Yufeng Deng

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Utility Analysis about Log Data Anomaly Detection Based on Federated Learning by Tae-Ho Shin, Soo-Hyung Kim

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Anomaly Detection Framework of System Call Trace Based on Sequence and Frequency Patterns by WEI Hui, CHEN Ze-mao, ZHANG Li-qiang

“…The existing system call-based anomaly intrusion detection methods can’t accurately describe the behavior of the process by a single trace pattern.In this paper,the process behavior is modeled based on the sequence and frequency patterns of system call trace,and a data-driven anomaly detection framework is designed.The framework could detect both sequential and quantitative anomalies of the system call trace simultaneously.With the help of combinational window mechanism,the framework could realize offline fine-grained learning and online anomaly real-time detection by meeting different requirements of offline trai-ning and online detection for extracting trace information.Performance comparison experiments of unknown anomalies detection are conducted on the ADFA-LD intrusion detection standard dataset.The results show that,compared with the four traditional machine learning methods and four deep learning methods,the comprehensive detection performance of the framework improves by about 10%.…”
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