scholarly journals Characterizing and understanding ensemble-based anomaly-detection

2021 ◽  
Author(s):  
Gustavo de P. Avelar ◽  
Guilherme O. Campos ◽  
Wagner Meira Jr.
Keyword(s):  
Anomaly Detection ◽  
Data Storage ◽  
State Of The Art ◽  
Abnormal Behavior ◽  
Huge Amount ◽  
Step Method ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
Wide Range ◽  
Isolation Forest

Anomaly Detection (AD) has grown in importance in recent years, as a result of an increasing digitalization of services and data storage, and abnormal behavior detection has become a key task. However, discovering abnormal data that is mixed with the huge amount of data available is a daunting problem and the efficacy of the current methods depends on a wide range of assumptions. One effective strategy for detecting anomalies is to combine multiple models, which are called "ensembles", but the factors that determine their performance are often hard to determine, making their calibration and improvement a challenging task. In this paper we address these problems by employing a four-step method for the characterization and understanding of ensemble-based anomaly-detection task. We start by characterizing several datasets and analyzing the factors that make it hard to detect their anomalies. We then evaluate to what extent existing algorithms are able to detect anomalies in the same datasets. On the basis of both analyses, we propose a stacking-based ensemble that outperformed a state-of-the-art baseline, Isolation Forest. Finally, we examine the benefits and drawbacks of our proposal.

scholarly journals A Framework of Abnormal Behavior Detection and Classification Based on Big Trajectory Data for Mobile Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Haiyan Zhang ◽  
Yonglong Luo ◽  
Qingying Yu ◽  
Liping Sun ◽  
Xuejing Li ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Mobile Networks ◽  
Real Life ◽  
Spatial Location ◽  
Recall Rate ◽  
Abnormal Behavior ◽  
Trajectory Data ◽  
Detection Techniques ◽  
Behavior Detection ◽  
Abnormal Behavior Detection

Big trajectory data feature analysis for mobile networks is a popular big data analysis task. Due to the large coverage and complexity of the mobile networks, it is difficult to define and detect anomalies in urban motion behavior. Some existing methods are not suitable for the detection of abnormal urban vehicle trajectories because they use the limited single detection techniques, such as determining the common patterns. In this study, we propose a framework for urban trajectory modeling and anomaly detection. Our framework takes into account the fact that anomalous behavior manifests the overall shape of unusual locations and trajectories in the spatial domain as well as the way these locations appear. Therefore, this study determines the peripheral features required for anomaly detection, including spatial location, sequence, and behavioral features. Then, we explore sports behaviors from the three types of features and build a taxi trajectory model for anomaly detection. Anomaly detection, including sports behaviors, are (i) detour behavior detection using an algorithm for global router anomaly detection of trajectories having a pair of same starting and ending points; this method is based on the isolation forest algorithm; (ii) local speed anomaly detection based on the DBSCAN algorithm; and (iii) local shape anomaly detection based on the local outlier factor algorithm. Using a real-life dataset, we demonstrate the effectiveness of our methods in detecting outliers. Furthermore, experiments show that the proposed algorithms perform better than the classical algorithm in terms of high accuracy and recall rate; thus, the proposed methods can accurately detect drivers’ abnormal behavior.

scholarly journals A Novel Anomaly Behavior Detection Scheme for Mobile Ad Hoc Networks

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1635
Author(s):  
Neeraj Chugh ◽  
Geetam Singh Tomar ◽  
Robin Singh Bhadoria ◽  
Neetesh Saxena
Keyword(s):  
Anomaly Detection ◽  
Ad Hoc Networks ◽  
Mobile Ad Hoc Networks ◽  
Network Performance ◽  
Ad Hoc ◽  
Abnormal Behavior ◽  
Energy Awareness ◽  
Behavior Detection ◽  
Mobile Ad Hoc ◽  
Hoc Networks

To sustain the security services in a Mobile Ad Hoc Networks (MANET), applications in terms of confidentially, authentication, integrity, authorization, key management, and abnormal behavior detection/anomaly detection are significant. The implementation of a sophisticated security mechanism requires a large number of network resources that degrade network performance. In addition, routing protocols designed for MANETs should be energy efficient in order to maximize network performance. In line with this view, this work proposes a new hybrid method called the data-driven zone-based routing protocol (DD-ZRP) for resource-constrained MANETs that incorporate anomaly detection schemes for security and energy awareness using Network Simulator 3. Most of the existing schemes use constant threshold values, which leads to false positive issues in the network. DD-ZRP uses a dynamic threshold to detect anomalies in MANETs. The simulation results show an improved detection ratio and performance for DD-ZRP over existing schemes; the method is substantially better than the prevailing protocols with respect to anomaly detection for security enhancement, energy efficiency, and optimization of available resources.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

scholarly journals A Novel Detection Framework for Detecting Abnormal Human Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Chengfei Wu ◽  
Zixuan Cheng
Keyword(s):  
Human Behavior ◽  
Public Safety ◽  
Space Time ◽  
Abnormal Behavior ◽  
Detection Methods ◽  
Time Block ◽  
Safety Issues ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
Abnormal Behaviors

Public safety issues have always been the focus of widespread concern of people from all walks of life. With the development of video detection technology, the detection of abnormal human behavior in videos has become the key to preventing public safety issues. Particularly, in student groups, the detection of abnormal human behavior is very important. Most existing abnormal human behavior detection algorithms are aimed at outdoor activity detection, and the indoor detection effects of these algorithms are not ideal. Students spend most of their time indoors, and modern classrooms are mostly equipped with monitoring equipment. This study focuses on the detection of abnormal behaviors of indoor humans and uses a new abnormal behavior detection framework to realize the detection of abnormal behaviors of indoor personnel. First, a background modeling method based on a Gaussian mixture model is used to segment the background image of each image frame in the video. Second, block processing is performed on the image after segmenting the background to obtain the space-time block of each frame of the image, and this block is used as the basic representation of the detection object. Third, the foreground image features of each space-time block are extracted. Fourth, fuzzy C-means clustering (FCM) is used to detect outliers in the data sample. The contribution of this paper is (1) the use of an abnormal human behavior detection framework that is effective indoors. Compared with the existing abnormal human behavior detection methods, the detection framework in this paper has a little difference in terms of its outdoor detection effects. (2) Compared with other detection methods, the detection framework used in this paper has a better detection effect for abnormal human behavior indoors, and the detection performance is greatly improved. (3) The detection framework used in this paper is easy to implement and has low time complexity. Through the experimental results obtained on public and manually created data sets, it can be demonstrated that the performance of the detection framework used in this paper is similar to those of the compared methods in outdoor detection scenarios. It has a strong advantage in terms of indoor detection. In summary, the proposed detection framework has a good practical application value.

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