Real-time Anomaly Detection Framework via System Calls Based on Integrated Learning [PDF]
Jisuanji gongcheng, 2023 Anomaly detection based on system calls data cannot complete the synchronous perception task of intrusion behavior within the process lifecycle，and there is a problem of low real-time anomaly detection accuracy.
CHEN Zhonglei, YI Peng, CHEN Xiang, HU Tao
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Improving SIEM for critical SCADA water infrastructures using machine learning [PDF]
, 2019 Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems.
A Bujari +17 more
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Multivariate Time Series Anomaly Detection Algorithm in Missing Value Scenario [PDF]
Jisuanji kexueTime series anomaly detection is an important research field in industry.Current methods of time series anomaly detection focus on anomaly detection for complete time series data,without considering the time series anomaly detection task containing ...
ZENG Zihui, LI Chaoyang, LIAO Qing
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Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System
Sensors, 2021 The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals.
Hoon Ko, Kwangcheol Rim, Isabel Praça
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A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks
Sensors, 2023 Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly detection (AD). With the rapid increase in the number of Internet-connected devices, the growing desire for Internet of Things (IoT) devices in the home, on our ...
Kyle DeMedeiros +2 more
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E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
IEEE Access, 2021 Industrial Control Systems (ICS) are evolving into smart environments with increased interconnectivity by being connected to the Internet. These changes increase the likelihood of security vulnerabilities and accidents. As the risk of cyberattacks on ICS
Chanwoong Hwang, Taejin Lee
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Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern
Information, 2019 Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user ...
Ying Zhao +6 more
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Machine Learning for Anomaly Detection: A Systematic Review
IEEE Access, 2021 Anomaly detection has been used for decades to identify and extract anomalous components from data. Many techniques have been used to detect anomalies. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role
Ali Bou Nassif +3 more
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Machine Learning-Driven Predictive Analytics for Real-Time Supply Chain Risk Management
Proceedings of the International Conference on Applied Innovations in ITA resilient and efficient supply chain requires real-time risk management in an increasingly volatile global marketplace. This study examines a supply chain risk management system based on machine learning-driven prediction analytics.
Nagham Ja’far Hussein +1 more
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A Multi-Dimensional approach towards Intrusion Detection System
, 2012 In this paper, we suggest a multi-dimensional approach towards intrusion detection. Network and system usage parameters like source and destination IP addresses; source and destination ports; incoming and outgoing network traffic data rate and number of ...
Sanyal, Sugata +1 more
core +3 more sources