Готові списки джерел за темами / Behavior-based Detection

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Добірка наукової літератури з теми "Behavior-based Detection"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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Статті в журналах з теми "Behavior-based Detection"

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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Дисертації з теми "Behavior-based Detection"

Stafford, John, and John Stafford. "Behavior-based Worm Detection." Thesis, University of Oregon, 2012. http://hdl.handle.net/1794/12341.

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The Internet has become a core component of our lives and businesses. Its reliability and availability are of paramount importance. There are many types of malware that impact the availability of the Internet, including network worms, bot-nets, viruses, etc. Detecting such attacks is a critical component of defending against them. This dissertation focuses on detecting and understanding self-propagating network worms, a type of malware with a proven record of disrupting the Internet. According to

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Morales, Jose Andre. "A Behavior Based Approach to Virus Detection." FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/41.

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Fast spreading unknown viruses have caused major damage on computer systems upon their initial release. Current detection methods have lacked capabilities to detect unknown virus quickly enough to avoid mass spreading and damage. This dissertation has presented a behavior based approach to detecting known and unknown viruses based on their attempt to replicate. Replication is the qualifying fundamental characteristic of a virus and is consistently present in all viruses making this approach applicable to viruses belonging to many classes and executing under several conditions. A form of replic

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Burguera, Hidalgo Iker. "Behavior-based malware detection system for the Android platform." Thesis, Linköpings universitet, RTSLAB - Laboratoriet för realtidssystem, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-73647.

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Malware in smartphones is growing at a significant rate. There are currently more than 250 million smartphone users in the world and this number is expected to grow in coming years. In the past few years, smartphones have evolved from simple mobile phones into sophisticated computers. This evolution has enabled smartphone users to access and browse the Internet, to receive and send emails, SMS and MMS messages and to connect devices in order to exchange information. All of these features make the smartphone a useful tool in our daily lives, but at the same time they render it more vulnerable

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Wüchner, Tobias [Verfasser]. "Behavior-based Malware Detection with Quantitative Data Flow Analysis / Tobias Wüchner." Berlin : epubli, 2016. http://d-nb.info/1120172470/34.

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Zhou, Mian. "Network Intrusion Detection: Monitoring, Simulation and Visualization." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4063.

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Анотація:

This dissertation presents our work on network intrusion detection and intrusion sim- ulation. The work in intrusion detection consists of two different network anomaly-based approaches. The work in intrusion simulation introduces a model using explicit traffic gen- eration for the packet level traffic simulation. The process of anomaly detection is to first build profiles for the normal network activity and then mark any events or activities that deviate from the normal profiles as suspicious. Based on the different schemes of creating the normal activity profiles, we introduce two approaches

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MARIANI, LEONARDO. "Behavior Capture and Test: Dynamic Analysis of Component-Based Systems}." Doctoral thesis, Università degli Studi di Milano Bicocca, 2005. http://hdl.handle.net/10281/57184.

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This PhD thesis describes how to check the correctness of interactions among software components by collecting information about components’ behavior during testing time, using the collected information to mine behavioral models, and then exploiting the models for checking the compatibility of components when updated or reused in new products. Empirical results demonstrate the effectiveness of the approach.

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Takeda, Kazuya, Norihide Kitaoka, and Sunao Hara. "Detection of task-incomplete dialogs based on utterance-and-behavior tag N-gram for spoken dialog systems." ISCA(International Speech Communication Association), 2011. http://hdl.handle.net/2237/15499.

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Pasdar, Amir Mehdi. "Real-Time Health Monitoring of Power Networks Based on High Frequency Behavior." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1415873192.

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Wüchner, Tobias [Verfasser], Alexander [Akademischer Betreuer] [Gutachter] Pretschner, and Felix [Gutachter] Freiling. "Behavior-based Malware Detection with Quantitative Data Flow Analysis / Tobias Wüchner. Betreuer: Alexander Pretschner. Gutachter: Alexander Pretschner ; Felix Freiling." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/110876682X/34.

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Khasgiwala, Jitesh. "Analysis of Time-Based Approach for Detecting Anomalous Network Traffic." Ohio University / OhioLINK, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1113583042.

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Книги з теми "Behavior-based Detection"

K, Kokula Krishna Hari, ed. Internet Worm Detection based on Traffic Behavior Monitoring with Improved C4.5: ICCS 2014. Association of Scientists, Developers and Faculties, 2014.

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Real Time Detection Of Anomalous Satellite Behavior from Ground-Based Telescope Images. Storming Media, 1998.

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Massimini, Marcello, and Giulio Tononi. Brain Islands. Translated by Frances Anderson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198728443.003.0003.

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This chapter examines the clinical problem of detecting consciousness in brain-injured patients who emerge from coma in a state of behavioral unresponsiveness. Intensive care medicine is artificially producing, as a by-product of saving many lives, brains that may remain isolated, split, or fragmented. In extreme cases, large cortical islands or an archipelago of islands may survive totally dissociated from the world outside. Can these islands sustain consciousness? Does it feel like anything to be a big chunk of isolated human cortex? Scientific and philosophical doubts aside, we need to urge

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Walsh, Bruce, and Michael Lynch. Evolution and Selection of Quantitative Traits. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.001.0001.

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Quantitative traits—be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene—usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, show

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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.

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The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research

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Частини книг з теми "Behavior-based Detection"

Anisetti, Marco, Claudio A. Ardagna, Nicola Bena, Vincenzo Giandomenico, and Gabriele Gianini. "Lightweight Behavior-Based Malware Detection." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51643-6_17.

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Cetnarowicz, Krzysztof, and Gabriel Rojek. "Behavior Based Detection of Unfavorable Resources." In Computational Science - ICCS 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24688-6_79.

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Guo, Lixiang, Zhaoyun Ding, and Hui Wang. "Behavior-Based Twitter Overlapping Community Detection." In Database Systems for Advanced Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32055-7_31.

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Niu, Weina, Xiaosong Zhang, Ran Yan, and Jiacheng Gong. "Behavior-Based Detection Method for Android Malware." In Android Malware Detection and Adversarial Methods. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1459-9_3.

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Yoon, Soojin, Hyun-lock Choo, Hanchul Bae, and Hwankuk Kim. "Behavior-Based Detection for Malicious Script-Based Attack." In Advances in Computer Science and Ubiquitous Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3023-9_15.

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Lu, Dang-Nhac, Thuy-Binh Tran, Duc-Nhan Nguyen, Thi-Hau Nguyen, and Ha-Nam Nguyen. "Abnormal Behavior Detection Based on Smartphone Sensors." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77818-1_19.

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Sihag, Vikas, Ashawani Swami, Manu Vardhan, and Pradeep Singh. "Signature Based Malicious Behavior Detection in Android." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6648-6_20.

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Mosli, Rayan, Rui Li, Bo Yuan, and Yin Pan. "A Behavior-Based Approach for Malware Detection." In Advances in Digital Forensics XIII. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67208-3_11.

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Zhang, Pengyuan, and Baojiang Cui. "Network Scanning Detection Based on Spatiotemporal Behavior." In Advances in Internet, Data & Web Technologies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53555-0_11.

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Chen, Shaoming, Yiyang Wang, and Yajun Du. "Detection of malware based on apps’ behavior." In Advances in Energy Science and Equipment Engineering II. CRC Press, 2017. http://dx.doi.org/10.1201/9781315116174-155.

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Тези доповідей конференцій з теми "Behavior-based Detection"

Wang, Huiyu, Ming Li, and Qin Lu. "Event-based human behavior detection." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762458.

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Tanana, Dmitry D. "Behavior-Based Detection of GPU Cryptojacking." In 2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE). IEEE, 2024. https://doi.org/10.1109/piere62470.2024.10804931.

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Qiu, Quanyuan, and Qingbing Sang. "Improved smoking behavior detection algorithm based on YOLOv5s." In Third International Conference on Machine Vision, Automatic Identification and Detection, edited by Renchao Jin. SPIE, 2024. http://dx.doi.org/10.1117/12.3035746.

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Zhou, Yuting, Lijuan Sun, Jingchen Wu, Yutong Gao, and Xu Wu. "An Abnormal Behavior Detection Method Based on User Behavior Correlation Feature Sequence Modeling." In 2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC). IEEE, 2024. https://doi.org/10.1109/dsc63484.2024.00027.

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Rahin, Saima Ahmed, Bo Hui, and Wanwan Li. "Location-Aware Context Detection Based-On Behavior Sensors." In 2024 6th International Conference on Computer Communication and the Internet (ICCCI). IEEE, 2024. http://dx.doi.org/10.1109/iccci62159.2024.10674531.

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Srivastava, Vivek, Manya Khare, Manasvi Kansal, and Harsh. "Behavior-Based Machine Learning Approaches for Malware Detection." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932606.

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Chen, Yanhao, Wenjie Dai, Likai Ju, and Cheng Zhou. "Detection of human fall behavior based on YOLOv5." In Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), edited by Hui Yuan and Lu Leng. SPIE, 2025. https://doi.org/10.1117/12.3055749.

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Jiang, Helong, Junyang Li, and Haitao Tian. "Algorithm for Student Behavior Detection Based on YOLOv8." In 2024 3rd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE). IEEE, 2024. https://doi.org/10.1109/cbase64041.2024.10824462.

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Li, Menghao, and Miao Liu. "Deep-learning-based algorithm for classifying pedestrian behavior at crosswalks." In 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), edited by Bin Liu and Lu Leng. SPIE, 2024. http://dx.doi.org/10.1117/12.3050750.

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Cao, Cheng, Zhengzhang Chen, James Caverlee, Lu-An Tang, Chen Luo, and Zhichun Li. "Behavior-based Community Detection." In CIKM '18: The 27th ACM International Conference on Information and Knowledge Management. ACM, 2018. http://dx.doi.org/10.1145/3269206.3272022.

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Pennington, Adam G., John D. Strunk, John L. Griffin, Craig A. Soules, Garth R. Goodson, and Gregory R. Ganger. Storage-based Intrusion Detection: Watching storage activity for suspicious behavior. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada461142.

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Christopher, Lauren, Stanley Chien, Yaobin Chen, Mei Qiu, William Reindl, and Liya Koshy. Anomaly Detection in Traffic Patterns Using the INDOT Camera System. Purdue University, 2025. https://doi.org/10.5703/1288284317778.

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The Transportation and Autonomous Systems Institute (TASI) of Purdue University Indianapolis (PUI) and the INDOT Traffic Management Center worked together to develop a system that monitors traffic conditions using INDOT CCTV video feeds. Computer vision-based traffic anomaly detection has been studied for the past 20 years, and a thorough state-of-the-art analysis was produced in a recent survey paper. Although AI has contributed to improving anomaly detection, several major challenges remain, such as tracking errors, illumination, weather, occlusion handling, camera pose, and perspective. In

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Schipaunboord, W. N., M. A. Lont, and A. H. M. Kron. JTM-00-01 NDE Acceptance Criteria for Girth Defects Linked with Welding and Inspection Technique. Pipeline Research Council International, Inc. (PRCI), 2001. http://dx.doi.org/10.55274/r0011796.

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Experimental work was conducted on two girth welds in large diameter steel pipes. By a proper selection of welding consumables, yield strength mismatch was obtained from 5 to 12% undermatching and 45% overmatching. The girth welds were non-destructively tested using the time of flight diffraction technique by three NDE companies. The verification of the detection and sizing capabilities of TOFD inspection techniques has shown that the performance levels varied widely. This finding confirms the need to validate the NDE techniques. Tensile specimens, Charpy and CTOD specimens, and curved wide pl

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Tan, Peng, and Nicholas Sitar. Parallel Level-Set DEM (LS-DEM) Development and Application to the Study of Deformation and Flow of Granular Media. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2023. http://dx.doi.org/10.55461/kmiz5819.

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We present a systematic investigation of computational approaches to the modeling of granular materials. Granular materials are ubiquitous in everyday life and in a variety of engineering and industrial applications. Despite the apparent simplicity of the laws governing particle-scale interactions, predicting the continuum mechanical response of granular materials still poses extraordinary challenges. This is largely due to the complex history dependence resulting from continuous rearrangement of the microstructure of granular material, as well as the mechanical interlocking due to grain morph

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Brydie, Dr James, Dr Alireza Jafari, and Stephanie Trottier. PR-487-143727-R01 Modelling and Simulation of Subsurface Fluid Migration from Small Pipeline Leaks. Pipeline Research Council International, Inc. (PRCI), 2017. http://dx.doi.org/10.55274/r0011025.

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The dispersion and migration behavior of hydrocarbon products leaking at low rates (i.e. 1bbl/day and 10 bbl/day) from a pipeline have been studied using a combination of experimental leakage tests and numerical simulations. The focus of this study was to determine the influence of subsurface engineered boundaries associated with the trench walls, and the presence of a water table, upon the leakage behavior of a range of hydrocarbon products. The project numerically modelled three products including diesel, diluted bitumen (dilbit) and gasoline; which were chosen to span a range of fluid types

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Zhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7587220.bard.

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Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. The

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Saldivar-Carranza, Enrique D., Howell Li, Jijo K. Mathew, et al. Next Generation Traffic Signal Performance Measures: Leveraging Connected Vehicle Data. Purdue University Press, 2023. http://dx.doi.org/10.5703/1288284317625.

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High-resolution connected vehicle (CV) trajectory and event data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, these data sets provide unique opportunities to build on and expand previous advances on traffic signal performance measures and safety evaluation. This report is a synthesis of research focused on the development of CV-based performance measures. A discussion is provided on data requirements, such as acquisition, storage, and access. Subsequently, techniques to reference vehicle trajectories to re

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