A Recursive Bayesian Filter for Anomalous Behavior Detection in Trajectory Data

2014 ◽  
pp. 91-104 ◽  
Author(s):  
Hai Huang ◽  
Lijuan Zhang ◽  
Monika Sester
Keyword(s):  
Anomalous Behavior ◽  
Trajectory Data ◽  
Bayesian Filter ◽  
Behavior Detection ◽  
Recursive Bayesian Filter ◽  
Recursive Bayesian

scholarly journals An anomalous behavior detection model in cloud computing

2016 ◽  
Vol 21 (3) ◽  
pp. 322-332 ◽  
Author(s):  
Xiaoming Ye ◽  
Xingshu Chen ◽  
Haizhou Wang ◽  
Xuemei Zeng ◽  
Guolin Shao ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Anomalous Behavior ◽  
Behavior Detection ◽  
Detection Model

scholarly journals Research on Anomalous Behavior Detection and Dangerousness Assessment among Civil Aviation Passengers at Checkpoints

2021 ◽  
Author(s):  
Haibin Shen ◽  
yan Zhang
Keyword(s):  
Neural Network ◽  
Index System ◽  
Evaluation Method ◽  
Assessment Method ◽  
Anomalous Behavior ◽  
Civil Aviation ◽  
Risk Level ◽  
Neural Network Method ◽  
Behavior Detection ◽  
Network Method

Abstract Traditional civil aviation security check measures are focused on baggage rather than passengers. The goal of this study is to enhance the level and effectiveness of security measures. We propose an anomalous behavior detection technique for civil aviation passengers and a passenger risk-assessment method based on a neural network method. A large number of real cases were analyzed and summarized to extract indicators of anomalous behavior of civil aviation passengers, and an index system was developed to detect anomalous behavior of passengers at checkpoints. A neural network method was used to evaluate the passengers and classify the risk level to detect potentially dangerous personnel, monitor people, and create an emergency warning system. The synthetic minority oversampling technique (SMOTE), the conjugate gradient method, and a multilayer perceptron neural network were used to classify the risk level of passengers at checkpoints. The results demonstrated that the proposed index system and evaluation method were well suited to deal with the ambiguity and uncertainty in the recognition process. The anomalous behavior of civil aviation passengers at checkpoints and the associated threat level were accurately identified.

scholarly journals Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hamid Masood Khan ◽  
Fazal Masud Khan ◽  
Aurangzeb Khan ◽  
Muhammad Zubair Asghar ◽  
Daniyal M. Alghazzawi
Keyword(s):  
Language Learning ◽  
Data Stream ◽  
Learning Rule ◽  
Anomalous Behavior ◽  
New Normal ◽  
Behavior Detection ◽  
Hierarchical Temporal Memory ◽  
Proposed Model ◽  
Single Pattern ◽  
Standard Normal

Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.

Anomalous Behavior Detection in Galaxies and Exoplanets using ML & DL Techniques

2021 ◽  
Author(s):  
Shivani Dere ◽  
Maziya Fatima ◽  
Rutuja Jagtap ◽  
Unzela Inamdar ◽  
Nikhilkumar Shardoor
Keyword(s):  
Anomalous Behavior ◽  
Behavior Detection
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