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Journal articles on the topic 'Behavior-based Detection'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Wang, Yonghui, Mengjie Wang, and Qingquan Meng. "Video based behavior detection algorithm." Journal of Physics: Conference Series 2504, no. 1 (2023): 012024. http://dx.doi.org/10.1088/1742-6596/2504/1/012024.

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Abstract Action recognition based on video surveillance becomes possible because of the rapid development of action recognition, temporal action recognition and spatial-temporal action recognition technology. A video-based behavior detection algorithm designed to find information of interest from videos. In the process of video detection, feature extraction is often carried out from space and time dimensions. However, the calculation amount of videos sent into the deep convolutional network is much higher than that of pictures. Therefore, the design of lightweight convolutional network is cond

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Kwon, Hee-Yong, Taesic Kim, and Mun-Kyu Lee. "Advanced Intrusion Detection Combining Signature-Based and Behavior-Based Detection Methods." Electronics 11, no. 6 (2022): 867. http://dx.doi.org/10.3390/electronics11060867.

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Recently, devices in real-time systems, such as residential facilities, vehicles, factories, and social infrastructure, have been increasingly connected to communication networks. Although these devices provide administrative convenience and enable the development of more sophisticated control systems, critical cybersecurity concerns and challenges remain. In this paper, we propose a hybrid anomaly detection method that combines statistical filtering and a composite autoencoder to effectively detect anomalous behaviors possibly caused by malicious activity in order to mitigate the risk of cybe

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Cheoi, Kyung Joo. "Temporal Saliency-Based Suspicious Behavior Pattern Detection." Applied Sciences 10, no. 3 (2020): 1020. http://dx.doi.org/10.3390/app10031020.

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The topic of suspicious behavior detection has been one of the most emergent research themes in computer vision, video analysis, and monitoring. Due to the huge number of CCTV (closed-circuit television) systems, it is not easy for people to manually identify CCTV for suspicious motion monitoring. This paper is concerned with an automatic suspicious behavior detection method using a CCTV video stream. Observers generally focus their attention on behaviors that vary in terms of magnitude or gradient of motion and behave differently in rules of motion with other objects. Based on these facts, th

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Wang, Kuochen, Chun-Ying Huang, Li-Yang Tsai, and Ying-Dar Lin. "Behavior-based botnet detection in parallel." Security and Communication Networks 7, no. 11 (2013): 1849–59. http://dx.doi.org/10.1002/sec.898.

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BOBROVNIKOVA, K., and D. DENYSIUK. "METHOD FOR MALWARE DETECTION BASED ON THE NETWORK TRAFFIC ANALYSIS AND SOFTWARE BEHAVIOR IN COMPUTER SYSTEMS." Herald of Khmelnytskyi National University. Technical sciences 287, no. 4 (2020): 7–11. https://doi.org/10.31891/2307-5732-2020-287-4-7-11.

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The paper presents a method for malware detection by analyzing network traffic and software behavior in computer systems. The method is based on the classification of API call sets extracted from the constructed control flow graphs for software applications, and based on the analysis of DNS traffic of the computer network. As a classifier a combination of deep neural network and recurrent neural network is used. The proposed method consists of two stages: the deep neural network and the recurrent neural network learning stage and the malware detecting stage. The steps of the malware detecting

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Nkiru, Ezefosie, and Ohemu Monday Fredrick. "A Data Driven Anomaly Based Behavior Detection Method for Advanced Persistent Threats (APT)." International Journal of Science and Research (IJSR) 10, no. 8 (2021): 663–67. https://doi.org/10.21275/sr21726172522.

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LU, Zhengqing, Jiajie Zhou, ChaoWei Wang, Zhihong Zhou, Guoliang Shi, and Ying Yin. "Delivery Garbage Behavior Detection Based on Deep Learning." International Journal of Information Technologies and Systems Approach 17, no. 1 (2024): 1–15. http://dx.doi.org/10.4018/ijitsa.343632.

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In the context of rapid urbanization, the challenge of effective garbage disposal has become increasingly significant. Traditional methods for addressing illegal littering by pedestrians are not only inefficient but also resource-intensive, demanding considerable manpower and materials. This study introduces a deep learning-based approach for detecting improper garbage disposal behavior. Leveraging advanced deep learning technologies, this approach focuses on object detection, tracking, and human posture analysis to identify and alert against illegal dumping activities captured in video footag

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Qi, Haixia, Zihong Chen, Guangsheng Liang, Riyao Chen, Jinzhuo Jiang, and Xiwen Luo. "Broiler Behavior Detection and Tracking Method Based on Lightweight Transformer." Applied Sciences 15, no. 6 (2025): 3333. https://doi.org/10.3390/app15063333.

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Detecting the daily behavior of broiler chickens allows early detection of irregular activity patterns and, thus, problems in the flock. In an attempt to resolve the problems of the slow detection speed, low accuracy, and poor generalization ability of traditional detection models in the actual breeding environment, we propose a chicken behavior detection method called FCBD-DETR (Faster Chicken Behavior Detection Transformer). The FasterNet network based on partial convolution (PConv) was used to replace the Resnet18 backbone network to reduce the computational complexity of the model and to i

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Xu, Yalei, Jing Nie, Honglei Cen, et al. "Spatio-Temporal-Based Identification of Aggressive Behavior in Group Sheep." Animals 13, no. 16 (2023): 2636. http://dx.doi.org/10.3390/ani13162636.

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In order to solve the problems of low efficiency and subjectivity of manual observation in the process of group-sheep-aggression detection, we propose a video streaming-based model for detecting aggressive behavior in group sheep. In the experiment, we collected videos of the sheep’s daily routine and videos of the aggressive behavior of sheep in the sheep pen. Using the open-source software LabelImg, we labeled the data with bounding boxes. Firstly, the YOLOv5 detects all sheep in each frame of the video and outputs the coordinates information. Secondly, we sort the sheep’s coordinates using

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Liu, Yanbing, Shousheng Jia, and Congcong Xing. "A Novel Behavior-Based Virus Detection Method for Smart Mobile Terminals." Discrete Dynamics in Nature and Society 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/262193.

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The security of smart mobile terminals has been an increasingly important issue in recent years. While there are extensive researches on virus detections for smart mobile terminals, most of them share the same framework of virus detection as that for personal computers, and few of them tackle the problem from the standpoint of detection methodology. In this paper, we propose a behavior-based virus detection method for smart mobile terminals which signals the existence of malicious code through identifying the anomaly of user behaviors. We first propose a model to collect and analyze user behav

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Chen, Haiwei, Guohui Zhou, and Huixin Jiang. "Student Behavior Detection in the Classroom Based on Improved YOLOv8." Sensors 23, no. 20 (2023): 8385. http://dx.doi.org/10.3390/s23208385.

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Accurately detecting student classroom behaviors in classroom videos is beneficial for analyzing students’ classroom performance and consequently enhancing teaching effectiveness. To address challenges such as object density, occlusion, and multi-scale scenarios in classroom video images, this paper introduces an improved YOLOv8 classroom detection model. Firstly, by combining modules from the Res2Net and YOLOv8 network models, a novel C2f_Res2block module is proposed. This module, along with MHSA and EMA, is integrated into the YOLOv8 model. Experimental results on a classroom detection datas

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Kumrashan, Indranil Iyer. "From Signatures to Behavior: Evolving Strategies for Next-Generation Intrusion Detection." European Journal of Advances in Engineering and Technology 8, no. 6 (2021): 165–71. https://doi.org/10.5281/zenodo.15223002.

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Intrusion Detection Systems (IDS) have been a cornerstone in defending organizational networks from malicious activities. Traditionally, these systems have relied heavily on signature-based approaches to identify known threats. However, as cyber threats evolve to become more stealthy, polymorphic, and advanced, the reliance on signatures and known indicators of compromise are no longer sufficient. This paper provides an in-depth analysis of the shift from traditional signature-based intrusion detection to behavior-based methodologies utilizing machine learning (ML) and advanced analytics. We r

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Li, Nige, Ziang Lu, Yuanyuan Ma, Yanjiao Chen, and Jiahan Dong. "A Malicious Program Behavior Detection Model Based on API Call Sequences." Electronics 13, no. 6 (2024): 1092. http://dx.doi.org/10.3390/electronics13061092.

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To address the issue of low accuracy in detecting malicious program behaviors in new power system edge-side applications, we present a detection model based on API call sequences that combines rule matching and deep learning techniques in this paper. We first use the PrefixSpan algorithm to mine frequent API call sequences in different threads of the same program within a malicious program dataset to create a rule base for malicious behavior sequences. The API call sequences to be examined are then matched using the malicious behavior sequence matching model, and those that do not match are fe

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Gao, Jerry, Jingwen Shi, Priyanka Balla, et al. "Camera-Based Crime Behavior Detection and Classification." Smart Cities 7, no. 3 (2024): 1169–98. http://dx.doi.org/10.3390/smartcities7030050.

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Increasing numbers of public and private locations now have surveillance cameras installed to make those areas more secure. Even though many organizations still hire someone to monitor the cameras, the person hired is more likely to miss some unexpected events in the video feeds because of human error. Several researchers have worked on surveillance data and have presented a number of approaches for automatically detecting aberrant events. To keep track of all the video data that accumulate, a supervisor is often required. To analyze the video data automatically, we recommend using neural netw

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Ren, Jun. "A Review of Anomalous Behavior Detection in Internet of Vehicles." International Journal of Computer Science and Information Technology 3, no. 1 (2024): 73–81. http://dx.doi.org/10.62051/ijcsit.v3n1.11.

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The intelligent transportation system with the Internet of Vehicles as its core is gradually penetrating into the lives of urban residents, but it has also exposed security threats such as remote control of vehicles and leakage of personal information of car owners. Compared to the security issues at the level of vehicle end devices and vehicle networking service platforms, the article focuses on the security issues of abnormal behavior in vehicle networking. Based on this, the article reviews the relevant research on abnormal behavior detection mechanisms in the Internet of Vehicles environme

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Zhang, Xuguang, Qian Zhang, Shuo Hu, Chunsheng Guo, and Hui Yu. "Energy Level-Based Abnormal Crowd Behavior Detection." Sensors 18, no. 2 (2018): 423. http://dx.doi.org/10.3390/s18020423.

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Shatalin, R. A., V. R. Fidelman, and P. E. Ovchinnikov. "Abnormal behavior detection based on dense trajectories." Computer Optics 42, no. 3 (2018): 476–82. http://dx.doi.org/10.18287/2412-6179-2018-42-3-476-482.

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In this paper, we propose abnormal behavior detection algorithms based on dense trajectories and principal components for video surveillance applications. The result shows that the proposed algorithms are faster than an algorithm based on lengths of displacement vectors but the accuracy is only retained if the bag-of-features model is trained on a balanced sample of behavior features.

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Park, Sang-Hyun, Hye-Wuk Jung, Tae-Bok Yoon, and Jee-Hyong Lee. "Behavior Pattern Modeling based Game Bot detection." Journal of Korean Institute of Intelligent Systems 20, no. 3 (2010): 422–27. http://dx.doi.org/10.5391/jkiis.2010.20.3.422.

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Koch, Robert, Mario Golling, and Gabi Dreo Rodosek. "Behavior-based intrusion detection in encrypted environments." IEEE Communications Magazine 52, no. 7 (2014): 124–31. http://dx.doi.org/10.1109/mcom.2014.6852093.

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Akrout, Belhassen, and Walid Mahdi. "Hypovigilance Detection Based on Eyelids Behavior Study." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 1, no. 1 (2013): 39. http://dx.doi.org/10.3991/ijes.v1i1.2927.

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Han, Lansheng, Cai Fu, Deqing Zou, ChangHoon Lee, and Wenjing Jia. "Task-based behavior detection of illegal codes." Mathematical and Computer Modelling 55, no. 1-2 (2012): 80–86. http://dx.doi.org/10.1016/j.mcm.2011.01.052.

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Sheng, Jinfang, Jie Hu, Zejun Sun, et al. "Community detection based on human social behavior." Physica A: Statistical Mechanics and its Applications 531 (October 2019): 121765. http://dx.doi.org/10.1016/j.physa.2019.121765.

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Wang, M. S., N. T. Jeong, K. S. Kim, et al. "Drowsy behavior detection based on driving information." International Journal of Automotive Technology 17, no. 1 (2016): 165–73. http://dx.doi.org/10.1007/s12239-016-0016-y.

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Qin, Heng, and Jin Hui Zhao. "Insider Threat Detection with Behavior-Based Attestation." Applied Mechanics and Materials 568-570 (June 2014): 1370–75. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1370.

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Insiders, who have the lawful authority in network information system, formed a huge threat to security by abuse and misuse of authority. It has become one of huge challenge to the security of information system. Against the features of more subtle and more difficult to find, this paper study how to perceive the trusted behavior of insiders with behavior-based attestation. Taking into account the impact of various uncertainties in monitoring and perception process, dynamic awareness model of insider threat is presented based on subjective logic. In order to find the insider threats, monitoring

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Galal, Hisham Shehata, Yousef Bassyouni Mahdy, and Mohammed Ali Atiea. "Behavior-based features model for malware detection." Journal of Computer Virology and Hacking Techniques 12, no. 2 (2015): 59–67. http://dx.doi.org/10.1007/s11416-015-0244-0.

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Li, Jun, Wentao Jiang, Jianyi Zhang, Yanhua Shao, and Wei Zhu. "Fake User Detection Based on Multi-Model Joint Representation." Information 15, no. 5 (2024): 266. http://dx.doi.org/10.3390/info15050266.

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The existing deep learning-based detection of fake information focuses on the transient detection of news itself. Compared to user category profile mining and detection, transient detection is prone to higher misjudgment rates due to the limitations of insufficient temporal information, posing new challenges to social public opinion monitoring tasks such as fake user detection. This paper proposes a multimodal aggregation portrait model (MAPM) based on multi-model joint representation for social media platforms. It constructs a deep learning-based multimodal fake user detection framework by an

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Gao, Ronghua, Qihang Liu, Qifeng Li, et al. "Multi-Target Rumination Behavior Analysis Method of Cows Based on Target Detection and Optical Flow Algorithm." Sustainability 15, no. 18 (2023): 14015. http://dx.doi.org/10.3390/su151814015.

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Rumination behavior is closely associated with factors such as cow productivity, reproductive performance, and disease incidence. For multi-object scenarios of dairy cattle, ruminant mouth area images accounted for little characteristic information, which was first put forward using an improved Faster R-CNN target detection algorithm to improve the detection performance model for the ruminant area of dairy cattle. The primary objective is to enhance the model’s performance in accurately detecting cow rumination regions. To achieve this, the dataset used in this study is annotated with both the

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Mohan, Mr B. "AN ADVANCED APPROACH FOR DETECTING BEHAVIOR BASED INTRANET ATTACKS BY MACHINE LEARNING." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45158.

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In the realm of cybersecurity, the detection of intranet attacks poses a significant challenge due to the evolving nature of malicious behaviors. This paper proposes an advanced approach for detecting behavior-based intranet attacks utilizing machine learning techniques. By leveraging the power of machine learning algorithms, the proposed approach aims to effectively identify and mitigate intranet attacks based on their behavioral patterns. Through the analysis of network traffic and system logs, the model learns to distinguish between normal and anomalous behaviors, thereby enabling proactive

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Peng, Shuyun, Xiaopei Zhang, Luoyu Zhou, and Peng Wang. "YOLO-CBD: Classroom Behavior Detection Method Based on Behavior Feature Extraction and Aggregation." Sensors 25, no. 10 (2025): 3073. https://doi.org/10.3390/s25103073.

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Classroom behavior can effectively reflect learning states, and thus classroom behavior detection is crucial for improving teaching methods and enhancing teaching quality. To address issues such as severe occlusions and large scale variations in student behavior detection, this paper proposes a classroom behavior detection model, named YOLO-CBD (YOLOv10s Classroom Behavior Detection). Firstly, BiFormer attention is introduced to redesign the Efficientv2 network, leading to a novel backbone network for efficient feature extraction of student classroom behaviors. The proposed attention module en

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Dongping, Zhang, Pan Qi, Ma Daobin, Mi Hongmei, and Lin Lili. "Temporally enhanced abnormal behavior detection based on multi-channel coupling." Scientific Insights and Discoveries Review 5 (October 14, 2024): 175–85. http://dx.doi.org/10.59782/sidr.v5i1.156.

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Abnormal behavior detection in surveillance videos based on deep learning has becomea hot topic in current research. However, due to the complexity and variability of crowd movement and external environment, detecting abnormal behavior faces great challenges. Current abnormal behavior recognition models have limitations in feature extraction and pay insufficient attention to dynamic temporal features. To address these problems, a spatiotemporal enhancement anomaly detection method based on multi-channel coupling is proposed. Based on the SlowFast network, a multi-channel coupled temporal enhan

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Cai, Hao, Zhiguang Song, Jianlong Xu, Zhi Xiong, and Yuanquan Xie. "CUDM: A Combined UAV Detection Model Based on Video Abnormal Behavior." Sensors 22, no. 23 (2022): 9469. http://dx.doi.org/10.3390/s22239469.

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The widespread use of unmanned aerial vehicles (UAVs) has brought many benefits, particularly for military and civil applications. For example, UAVs can be used in communication, ecological surveys, agriculture, and logistics to improve efficiency and reduce the required workforce. However, the malicious use of UAVs can significantly endanger public safety and pose many challenges to society. Therefore, detecting malicious UAVs is an important and urgent issue that needs to be addressed. In this study, a combined UAV detection model (CUDM) based on analyzing video abnormal behavior is proposed

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Anub, A., and S. Sreelekshmy. "Dynamic Threshold-Based Algorithm for Client-Based HTTP Proxy Attack Detection through Spatial and Temporal Behavior Pattern Analysis." Recent Trends in Androids and IOS Applications 6, no. 3 (2024): 48–53. https://doi.org/10.5281/zenodo.13626561.

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<em>This paper provides a unique approach to client- based HTTP proxy attack detection using a Dynamic Spatiotem- poral Behavior Analysis (DSTBA) algorithm. Traditional meth- ods often lack adaptability to sophisticated cyberattacks. DSTBA addresses this by dynamically adjusting detection thresholds based on real-time analysis of spatial (network node distribution and interaction) and temporal (request timing and frequency) behavior patterns. This integration with machine learning tech- niques enhances attack identification accuracy while minimizing false positives. DSTBA’s core strength

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Jiang, Zhenxiang, and Jinping He. "Detection Model for Seepage Behavior of Earth Dams Based on Data Mining." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/8191802.

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Seepage behavior detecting is an important tool for ensuring the safety of earth dams. However, traditional seepage behavior detection methods have used insufficient monitoring data and have mainly focused on single-point measures and local seepage behavior. The seepage behavior of dams is not quantitatively detected based on the monitoring data with multiple measuring points. Therefore, this study uses data mining techniques to analyze the monitoring data and overcome the above-mentioned shortcomings. The massive seepage monitoring data with multiple points are used as the research object. Th

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Suo, Yongfeng, Yan Wang, and Lei Cui. "Ship Anomalous Behavior Detection Based on BPEF Mining and Text Similarity." Journal of Marine Science and Engineering 13, no. 2 (2025): 251. https://doi.org/10.3390/jmse13020251.

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Maritime behavior detection is vital for maritime surveillance and management, ensuring safe ship navigation, normal port operations, marine environmental protection, and the prevention of illegal activities on water. Current methods for detecting anomalous vessel behaviors primarily rely on single time series data or feature point analysis, which struggle to capture the relationships between vessel behaviors, limiting anomaly identification accuracy. To address this challenge, we proposed a novel vessel anomaly detection framework, which is called the BPEF-TSD framework. It integrates a ship

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Wuchner, Tobias, Aleksander Cislak, Martin Ochoa, and Alexander Pretschner. "Leveraging Compression-Based Graph Mining for Behavior-Based Malware Detection." IEEE Transactions on Dependable and Secure Computing 16, no. 1 (2019): 99–112. http://dx.doi.org/10.1109/tdsc.2017.2675881.

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Huang, Chiao Ching, Yi Ting Tsao, and Jane Yung Jen Hsu. "Behavior-Based Detection of Abnormal Power Consumption for Power Saving." Applied Mechanics and Materials 291-294 (February 2013): 674–78. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.674.

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Abnormalities are caused by incorrect or inappropriate behaviors or appliance malfunctions. They may lead to electricity waste and safety hazards. This paper describes a novel appliance management system for detecting abnormal power consumption in convenience stores based on power meters. Our system detects abnormal power consumption through historical behavior models. Generalized extreme studentized deviate (GESD) and regression methods are applied to build behavior models. The behavior based abnormal detection methods can assist in preventing these waste and safety problems and improve the a

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Fang, Meng-ting, Zhong-ju Chen, Krzysztof Przystupa, Tao Li, Michal Majka, and Orest Kochan. "Examination of Abnormal Behavior Detection Based on Improved YOLOv3." Electronics 10, no. 2 (2021): 197. http://dx.doi.org/10.3390/electronics10020197.

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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

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Huang, Jingui, Jingyi Li, and Wenya Wu. "A New Method of Pedestrian Abnormal Behavior Detection Based on Attention Guidance." Advances in Multimedia 2022 (December 20, 2022): 1–20. http://dx.doi.org/10.1155/2022/1038225.

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In public places, some behavior that violates public order and endangers public safety is defined as abnormal behavior. Moreover, it is a necessary auxiliary means to maintain public order and safety by detecting abnormal behavior in a large number of surveillance videos. However, due to the small proportion of abnormal behavior in video data, the extreme imbalance of data seriously restricts the effectiveness of detection. So, weakly supervised learning has become the most suitable and effective detection method. However, existing weakly supervised methods rarely take the locality and slightn

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N, Nitha, Rahana, Rahana,, Rizwan M, and Sajitha A S. "Behavioral based threat detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43633.

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Insider threats pose a significant risk to organizations as they exploit legitimate access to bypass traditional security measures, making them harder to detect than external attacks. This study addresses the challenge by utilizing deep learning to analyze user behavior and identify malicious activities through a carefully selected set of event- based features. By training on the CMU CERT r4.2 dataset, the proposed model effectively learns patterns of adversarial behavior, reducing false positives while maintaining high detection accuracy. The paper presents a deep learning-based approach for

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Guo, Junbo, Jintao Li, Yongdong Zhang, Dongming Zhang, and Xiao Wu. "Video Copy Detection Based on Trajectory Behavior Pattern." Journal of Computer-Aided Design & Computer Graphics 22, no. 6 (2010): 943–48. http://dx.doi.org/10.3724/sp.j.1089.2010.10842.

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Wu, Yin He, and Dai Ping Li. "Analysis Based on iOS Application Malicious Behavior Detection." Applied Mechanics and Materials 602-605 (August 2014): 2321–25. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2321.

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Due to the highly developed modern technology,Smart phones and other mobile devices are become more and more universal. Most of those devices are used to process or store sensitive and confidential data.Consequently,it may cause many problems,such as privacy disclosure,mobile phone virus,spyware,etc. In order to solve those issues,We need to monitor applications`s behaviour to tell those malicious ones. Here we use MobileSubstrate to hook every sensitive system API the application invokes in iOS planform,and send this invocation to our matching algorithm,the matching algorithm will evaluate if

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Ma, Xu Yan, Guo Mao Liang, Wei Yu, and Zhi Yi Qu. "Abnormal Behavior Detection Based on Global Motion Orientation." Advanced Materials Research 765-767 (September 2013): 2264–67. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2264.

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A novel approach is introduced in this paper to detect abnormal behavior based on global motion orientation. Compare to the normal behavior (walking, shaking hands etc.), abnormal behavior has different orientation. The method we introduced divides each frame into blocks, makes statistical analysis of the global motion direction histogram of all frame blocks and extracts characteristics. At last, behavior is detected with support vector machine (SVM). Experiment shows that the method proposed in the paper has certain robustness and can achieve real-time monitoring.

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Zou, Mengsong, Lansheng Han, Ming Liu, and Qiwen Liu. "Virus Detection Method based on Behavior Resource Tree." Journal of Information Processing Systems 7, no. 1 (2011): 173–86. http://dx.doi.org/10.3745/jips.2011.7.1.173.

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Shi, Yan, Cheng Long, Xuexi Yang, and Min Deng. "Abnormal Ship Behavior Detection Based on AIS Data." Applied Sciences 12, no. 9 (2022): 4635. http://dx.doi.org/10.3390/app12094635.

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With the development of navigation globalization and ship dehumanization, the contradiction between the increasing demand for ship behavior supervision and limited traffic service resources is obvious, and the frequent occurrence of accidents at sea is a problem. The monitoring of abnormal ship behavior is an important link in maritime transportation. With the popularization of the automatic identification system and increasing research in the maritime field, the AIS is widely used in the management of ship static information and the real-time sharing of dynamic information. The generated movi

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Isohara, Takamasa, Keisuke Takemori, and Iwao Sasase. "Anomaly Detection on Mobile Phone Based Operational Behavior." IPSJ Digital Courier 4 (2008): 9–17. http://dx.doi.org/10.2197/ipsjdc.4.9.

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Jebakumari, M. "Visualization and Detection based on User Behavior Typing." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 5087–89. http://dx.doi.org/10.22214/ijraset.2018.4830.

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SUN, Xiao-yan, Yue-fei ZHU, Qian HUANG, and Ning GUO. "Study of malware detection based on interactive behavior." Journal of Computer Applications 30, no. 6 (2010): 1489–92. http://dx.doi.org/10.3724/sp.j.1087.2010.01489.

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张, 树波. "Abnormal Vessel Behavior Detection Based on AIS Data." Artificial Intelligence and Robotics Research 04, no. 04 (2015): 23–31. http://dx.doi.org/10.12677/airr.2015.44004.

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Zhang, Dongping, Huailiang Peng, Yu Haibin, and Yafei Lu. "Crowd Abnormal Behavior Detection Based on Machine Learning." Information Technology Journal 12, no. 6 (2013): 1199–205. http://dx.doi.org/10.3923/itj.2013.1199.1205.

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Hu, Jie, Li Xu, Xin He, and Wuqiang Meng. "Abnormal Driving Detection Based on Normalized Driving Behavior." IEEE Transactions on Vehicular Technology 66, no. 8 (2017): 6645–52. http://dx.doi.org/10.1109/tvt.2017.2660497.

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