Relevant bibliographies by topics / Anomaly Behavior Detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Anomaly Behavior Detection'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Anomaly Behavior Detection"

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Zhang, Haiyan, Yonglong Luo, Qingying Yu, Liping Sun, Xuejing Li, and Zhenqiang Sun. "A Framework of Abnormal Behavior Detection and Classification Based on Big Trajectory Data for Mobile Networks." Security and Communication Networks 2020 (December 22, 2020): 1–15. http://dx.doi.org/10.1155/2020/8858444.

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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 ov
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Garcia, Olivia W., and James C. Brown. "LEVERAGING CONTEXT DISCOVERY FOR EFFECTIVE ANOMALY DETECTION IN COMPLEX SYSTEMS." Pinnacle Research Journal of Scientific and Management Sciences 2, no. 4 (2025): 1–7. https://doi.org/10.55640/tprjsms-v02i04-01.

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Anomaly detection is a fundamental task in various domains, such as cybersecurity, finance, healthcare, and sensor networks. Traditional methods often struggle to distinguish between normal and anomalous behaviors when contextual information is not properly considered. This paper explores context discovery as a key strategy for enhancing anomaly detection. By identifying and utilizing relevant contextual information, anomaly detection systems can more effectively differentiate between benign and anomalous patterns, improving both the accuracy and robustness of detection. We present an approach
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Feng, Wenying, Yu Cao, Yilu Chen, et al. "Multi-Granularity User Anomalous Behavior Detection." Applied Sciences 15, no. 1 (2024): 128. https://doi.org/10.3390/app15010128.

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Insider threats pose significant risks to organizational security, often going undetected due to their familiarity with the systems. Detection of insider threats faces challenges of imbalanced data distributions and difficulties in fine-grained detection. Specifically, anomalous users and anomalous behaviors take up a very small fraction of all insider behavior data, making precise detection of anomalous users challenging. Moreover, not all behaviors of anomalous users are anomalous, so it is difficult to detect their behaviors by standardizing with single rules or models. To address these cha
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Parres-Peredo, Alvaro, Ivan Piza-Davila, and Francisco Cervantes. "Unexpected-Behavior Detection Using TopK Rankings for Cybersecurity." Applied Sciences 9, no. 20 (2019): 4381. http://dx.doi.org/10.3390/app9204381.

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Anomaly-based intrusion detection systems use profiles to characterize expected behavior of network users. Most of these systems characterize the entire network traffic within a single profile. This work proposes a user-level anomaly-based intrusion detection methodology using only the user’s network traffic. The proposed profile is a collection of TopK rankings of reached services by the user. To detect unexpected behaviors, the real-time traffic is organized into TopK rankings and compared to the profile using similarity measures. The experiments demonstrated that the proposed methodology wa
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Aarthi, G., S. Sharon Priya, and W. Aisha Banu. "KRF-AD: Innovating anomaly detection with KDE-KL and random forest fusion." Intelligent Decision Technologies 18, no. 3 (2024): 2275–87. http://dx.doi.org/10.3233/idt-240628.

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Anomaly detection in Intrusion Detection System (IDS) data refers to the process of identifying and flagging unusual or abnormal behavior within a network or system. In the context of IoT, anomaly detection helps in identifying any abnormal or unexpected behavior in the data generated by connected devices. Existing methods often struggle with accurately detecting anomalies amidst massive data volumes and diverse attack patterns. This paper proposes a novel approach, KDE-KL Anomaly Detection with Random Forest Integration (KRF-AD), which combines Kernel Density Estimation (KDE) and Kullback-Lei
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Shayegan, Mohammad Javad, Hamid Reza Sabor, Mueen Uddin, and Chin-Ling Chen. "A Collective Anomaly Detection Technique to Detect Crypto Wallet Frauds on Bitcoin Network." Symmetry 14, no. 2 (2022): 328. http://dx.doi.org/10.3390/sym14020328.

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The popularity and remarkable attractiveness of cryptocurrencies, especially Bitcoin, absorb countless enthusiasts every day. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to use the Blockchain technology of symmetry and asymmetry in computer and engineering science to present a new method for detecting anomalies in Bitcoin with more appropriate
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Zhao, Ying, Junjun Chen, Di Wu, et al. "Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern." Information 10, no. 8 (2019): 262. http://dx.doi.org/10.3390/info10080262.

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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 anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distingu
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Tao Xiang and Shaogang Gong. "Video Behavior Profiling for Anomaly Detection." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 5 (2008): 893–908. http://dx.doi.org/10.1109/tpami.2007.70731.

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Zhu, Xudong, and Zhijing Liu. "Human behavior clustering for anomaly detection." Frontiers of Computer Science in China 5, no. 3 (2011): 279–89. http://dx.doi.org/10.1007/s11704-011-0080-4.

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Kumar, Sandeep, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Abdul Khader Jilani Saudagar, Abdullah AlTameem, and Mohammed AlKhathami. "An Anomaly Detection Framework for Twitter Data." Applied Sciences 12, no. 21 (2022): 11059. http://dx.doi.org/10.3390/app122111059.

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An anomaly indicates something unusual, related to detecting a sudden behavior change, and is also helpful in detecting irregular and malicious behavior. Anomaly detection identifies unusual events, suspicious objects, or observations that differ significantly from normal behavior or patterns. Discrepancies in data can be observed in different ways, such as outliers, standard deviation, and noise. Anomaly detection helps us understand the emergence of specific diseases based on health-related tweets. This paper aims to analyze tweets to detect the unusual emergence of healthcare-related tweets
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Dissertations / Theses on the topic "Anomaly Behavior Detection"

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Buthpitiya, Senaka. "Modeling Mobile User Behavior for Anomaly Detection." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/362.

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As ubiquitous computing (ubicomp) technologies reach maturity, smart phones and context-based services are gaining mainstream popularity. A smart phone accompanies its user throughout (nearly) all aspects of his life, becoming an indispensable assistant the busy user relies on to help navigate his life, using map applications to navigate the physical world, email and instant messaging applications to keep in touch, media player applications to be entertained, etc. As a smart phone is capable of sensing the physical and virtual context of the user with an array of “hard” sensors (e.g., GPS, acc
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Xu, Kui. "Anomaly Detection Through System and Program Behavior Modeling." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/51140.

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Various vulnerabilities in software applications become easy targets for attackers. The trend constantly being observed in the evolution of advanced modern exploits is their growing sophistication in stealthy attacks. Code-reuse attacks such as return-oriented programming allow intruders to execute mal-intended instruction sequences on a victim machine without injecting external code. Successful exploitation leads to hijacked applications or the download of malicious software (drive-by download attack), which usually happens without the notice or permission from users. In this dissertation,
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Carlsson, Oskar, and Daniel Nabhani. "User and Entity Behavior Anomaly Detection using Network Traffic." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14636.

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Ullah, Habib. "Crowd Motion Analysis: Segmentation, Anomaly Detection, and Behavior Classification." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/369001.

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The objective of this doctoral study is to develop efficient techniques for flow segmentation, anomaly detection, and behavior classification in crowd scenes. Considering the complexities of occlusion, we focused our study on gathering the motion information at a higher scale, thus not associating it to single objects, but considering the crowd as a single entity. Firstly,we propose methods for flow segmentation based on correlation features, graph cut, Conditional Random Fields (CRF), enthalpy model, and particle mutual influence model. Secondly, methods based on deviant orientation informati
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Ullah, Habib. "Crowd Motion Analysis: Segmentation, Anomaly Detection, and Behavior Classification." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1406/1/PhD_Thesis_Habib.pdf.

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The objective of this doctoral study is to develop efficient techniques for flow segmentation, anomaly detection, and behavior classification in crowd scenes. Considering the complexities of occlusion, we focused our study on gathering the motion information at a higher scale, thus not associating it to single objects, but considering the crowd as a single entity. Firstly,we propose methods for flow segmentation based on correlation features, graph cut, Conditional Random Fields (CRF), enthalpy model, and particle mutual influence model. Secondly, methods based on deviant orientation informati
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Alipour, Hamid Reza. "An Anomaly Behavior Analysis Methodology for Network Centric Systems." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/305804.

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Information systems and their services (referred to as cyberspace) are ubiquitous and touch all aspects of our life. With the exponential growth in cyberspace activities, the number and complexity of cyber-attacks have increased significantly due to an increase in the number of applications with vulnerabilities and the number of attackers. Consequently, it becomes extremely critical to develop efficient network Intrusion Detection Systems (IDS) that can mitigate and protect cyberspace resources and services against cyber-attacks. On the other hand, since each network system and application has
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Satam, Pratik. "An Anomaly Behavior Analysis Intrusion Detection System for Wireless Networks." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/595654.

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Wireless networks have become ubiquitous, where a wide range of mobile devices are connected to a larger network like the Internet via wireless communications. One widely used wireless communication standard is the IEEE 802.11 protocol, popularly called Wi-Fi. Over the years, the 802.11 has been upgraded to different versions. But most of these upgrades have been focused on the improvement of the throughput of the protocol and not enhancing the security of the protocol, thus leaving the protocol vulnerable to attacks. The goal of this research is to develop and implement an intrusion detection
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Larsson, Frans. "Algorithmic trading surveillance : Identifying deviating behavior with unsupervised anomaly detection." Thesis, Uppsala universitet, Matematiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-389941.

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The financial markets are no longer what they used to be and one reason for this is the breakthrough of algorithmic trading. Although this has had several positive effects, there have been recorded incidents where algorithms have been involved. It is therefore of interest to find effective methods to monitor algorithmic trading. The purpose of this thesis was therefore to contribute to this research area by investigating if machine learning can be used for detecting deviating behavior. Since the real world data set used in this study lacked labels, an unsupervised anomaly detection approach wa
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Pan, Zhiwen, and Zhiwen Pan. "A Context Aware Anomaly Behavior Analysis Methodology for Building Automation Systems." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625624.

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Advances in mobile and pervasive computing, electronics technology, and the exponential growth in Internet of Things (IoT) applications and services has led to Building Automation System (BAS) that enhanced the buildings we live by delivering more energy-saving, intelligent, comfortable, and better utilization. Through the use of integrated protocols, a BAS can interconnects a wide range of building assets so that the control and management of asset operations and their services can be performed in one protocol. Moreover, through the use of distributed computing and IP based communication, a B
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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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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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Books on the topic "Anomaly Behavior Detection"

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Isupova, Olga. Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75508-3.

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Isupova, Olga. Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video. Springer, 2018.

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Isupova, Olga. Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video. Springer, 2019.

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Book chapters on the topic "Anomaly Behavior Detection"

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Zhu, Xudong, Hui Li, and Zhijing Liu. "Behavior Clustering for Anomaly Detection." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23602-0_2.

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Soukup, Dominik, Tomas Cejka, and Karel Hynek. "Behavior Anomaly Detection in IoT Networks." In Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43192-1_53.

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Aran, Oya, Dairazalia Sanchez-Cortes, Minh-Tri Do, and Daniel Gatica-Perez. "Anomaly Detection in Elderly Daily Behavior in Ambient Sensing Environments." In Human Behavior Understanding. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46843-3_4.

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Wang, Cheng, and Hangyu Zhu. "Enhancing Data for Hard Anomaly Detection." In Universal Behavior Computing for Security and Safety. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-9014-2_2.

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Kim, Misun, Minsoo Kim, and JaeHyun Seo. "Network Anomaly Behavior Detection Using an Adaptive Multiplex Detector." In Computational Science and Its Applications - ICCSA 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11751595_17.

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Zhang, Yuanzhe, Qiqiang Jin, Maohan Liang, Ruixin Ma, and Ryan Wen Liu. "Vessel Behavior Anomaly Detection Using Graph Attention Network." In Neural Information Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8073-4_23.

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Li, Yuanyuan, Michael Thomason, and Lynne E. Parker. "Sequential Anomaly Detection Using Wireless Sensor Networks in Unknown Environment." In Human Behavior Understanding in Networked Sensing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10807-0_5.

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Wang, Sheng, Jiaming Song, and Ruixu Guo. "Char-Level Neural Network for Network Anomaly Behavior Detection." In Human Centered Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15127-0_6.

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Seleznyov, Alexandr, Oleksiy Mazhelis, and Seppo Puuronen. "Learning Temporal Regularities of User Behavior for Anomaly Detection." In Information Assurance in Computer Networks. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45116-1_16.

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Wang, Jinfeng, and Xiongshen Xie. "Anomaly Behavior Detection in Crowd via Lightweight 3D Convolution." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5615-5_11.

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Conference papers on the topic "Anomaly Behavior Detection"

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Ahmad, Intekhab, Sandhya Rani Sahoo, and Ratnakar Dash. "Anomaly Detection in Video Surveillance for Unusual Behavior Identification." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692100.

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Gao, Dongying, Caiwei Guo, Wen Ji, et al. "Anomaly Detection System for Terminal-Level Data Access Behavior." In 2024 9th International Conference on Signal and Image Processing (ICSIP). IEEE, 2024. http://dx.doi.org/10.1109/icsip61881.2024.10671411.

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Guo, Jinglong, Yanyun Xu, Meng Zhang, Weiqing Huang, and Huadong Guo. "Behavior Recognition and Anomaly Detection Utilizing Memory Electromagnetic Emanation." In MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM). IEEE, 2024. https://doi.org/10.1109/milcom61039.2024.10773719.

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Ma, Tianfu, Jian Bao, Hao Yang, Xuejiao Zhao, Qingwang Zhang, and Wanting Lv. "Network Anomaly Behavior Detection and Security Protection based on Clustering Algorithm." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934590.

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Gupta, Swati, Snehal Borji, and Prof Pallavi Thakur. "Behavior Prediction and Anomaly Detection: Leveraging ML for Online Child Safety." In 2025 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2025. https://doi.org/10.1109/esci63694.2025.10988020.

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Wang, Jiayu, Yun Chen, Ran Geng, et al. "An Efficient Anomaly Detection Model of Power Consumption Behavior for Industrial Customers." In 2024 11th International Conference on Behavioural and Social Computing (BESC). IEEE, 2024. https://doi.org/10.1109/besc64747.2024.10780628.

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Liu, Zhen, Changzhen Hu, Chun Shan, and Junkai Yi. "BedIDS: An Effective Network Anomaly Detection Method by Fusing Behavior Evolution characteristics." In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2024. https://doi.org/10.1109/trustcom63139.2024.00212.

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Feizi, A., A. Aghagolzadeh, and H. Seyedarabi. "Behavior recognition and anomaly behavior detection using clustering." In 2012 Sixth International Symposium on Telecommunications (IST). IEEE, 2012. http://dx.doi.org/10.1109/istel.2012.6483112.

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Smith, I. F. C. "Anomaly Detection without Structural Behavior Models." In Structures Congress 2011. American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41171(401)5.

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Zhang, Facun, Weiwei Xue, Lijun Cui, and Guangrui Zhu. "A Crowd Anomaly Behavior Detection Algorithm." In 2018 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2018. http://dx.doi.org/10.1109/icalip.2018.8455412.

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Reports on the topic "Anomaly Behavior Detection"

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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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Alonso-Robisco, Andrés, Andrés Alonso-Robisco, José Manuel Carbó, et al. Empowering financial supervision: a SupTech experiment using machine learning in an early warning system. Banco de España, 2025. https://doi.org/10.53479/39320.

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New technologies have made available a vast amount of new data in the form of text, recording an exponentially increasing share of human and corporate behavior. For financial supervisors, the information encoded in text is a valuable complement to the more traditional balance sheet data typically used to track the soundness of financial institutions. In this study, we exploit several natural language processing (NLP) techniques as well as network analysis to detect anomalies in the Spanish corporate system, identifying both idiosyncratic and systemic risks. We use sentiment analysis at the cor
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