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Dissertations / Theses on the topic 'Anomaly Behavior Detection'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

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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2

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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Madhu, Abheyraj Singh, and Sreemayi Rapolu. "Anomaly Detection in Wait Reports and its Relation with Apache Cassandra Statistics." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21145.

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Background: Apache Cassandra is a highly scalable distributed system that can handle large amounts of data through several nodes / virtual machines grouped together as Apache Cassandra clusters. When one such node in an Apache Cassandra cluster is down, there is a need for a tool or an approach that can identify this failed virtual machine by analyzing the data generated from each of the virtual machines in the cluster. Manual analysis of this data is tedious and can be quite strenuous. Objectives: The objective of the thesis is to identify, build and evaluate a solution that can detect and re

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Roberts, James Frank. "Automatic Detection of Abnormal Behavior in Computing Systems." UKnowledge, 2013. http://uknowledge.uky.edu/cs_etds/11.

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I present RAACD, a software suite that detects misbehaving computers in large computing systems and presents information about those machines to the system administrator. I build this system using preexisting anomaly detection techniques. I evaluate my methods using simple synthesized data, real data containing coerced abnormal behavior, and real data containing naturally occurring abnormal behavior. I find that the system adequately detects abnormal behavior and significantly reduces the amount of uninteresting computer health data presented to a system administrator.

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Abed, Amr Sayed Omar. "Securing Cloud Containers through Intrusion Detection and Remediation." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/87730.

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Linux containers are gaining increasing traction in both individual and industrial use. As these containers get integrated into mission-critical systems, real-time detection of malicious cyber attacks becomes a critical operational requirement. However, a little research has been conducted in this area. This research introduces an anomaly-based intrusion detection and remediation system for container-based clouds. The introduced system monitors system calls between the container and the host server to passively detect malfeasance against applications running in cloud containers. We sta

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Rook, Jayson Carr. "Detecting Anomalous Behavior in Radar Data." Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1622205414960438.

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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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Berger, Victor. "Anomaly detection in user behavior of websites using Hierarchical Temporal Memories : Using Machine Learning to detect unusual behavior from users of a web service to quickly detect possible security hazards." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206979.

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This Master's Thesis focuses on the recent Cortical Learn-ing Algorithm (CLA), designed for temporal anomaly detection. It is here applied to the problem of anomaly detec-tion in user behavior of web services, which is getting moreand more important in a network security context. CLA is here compared to more traditional state-of-the-art algorithms of anomaly detection: Hidden Markov Models (HMMs) and t-stide (an N-gram-based anomaly detector), which are among the few algorithms compatible withthe online processing constraint of this problem. It is observed that on the synthetic dataset used fo

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Ghiasvand, Siavash. "Toward Resilience in High Performance Computing:: A Prototype to Analyze and Predict System Behavior." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A72457.

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Following the growth of high performance computing systems (HPC) in size and complexity, and the advent of faster and more complex Exascale systems, failures became the norm rather than the exception. Hence, the protection mechanisms need to be improved. The most de facto mechanisms such as checkpoint/restart or redundancy may also fail to support the continuous operation of future HPC systems in the presence of failures. Failure prediction is a new protection approach that is beneficial for HPC systems with a short mean time between failure. The failure prediction mechanism extends the existi

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Petrovič, Lukáš. "Analýza anomálií v uživatelském chování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403133.

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The aim of this work is to create an application that allows modeling of user behavior and subsequent search for anomalies in this behavior. An application entry is a list of actions the user has executed on his workstation. From this information and from information about the events that occurred on this device the behavioral model for a specific time is created. Subsequently, this model is compared to models in different time periods or with other users' models. From this comparison, we can get additional information about user behavior and also detect anomalous behavior. The information abo

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Leach, Michael Jeremy Vincent. "Automatic human behaviour anomaly detection in surveillance video." Thesis, Heriot-Watt University, 2015. http://hdl.handle.net/10399/3014.

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This thesis work focusses upon developing the capability to automatically evaluate and detect anomalies in human behaviour from surveillance video. We work with static monocular cameras in crowded urban surveillance scenarios, particularly air- ports and commercial shopping areas. Typically a person is 100 to 200 pixels high in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo- ple at any given time. Our procedure evaluates human behaviour unobtrusively to determine outlying behavioural events, agging abnormal events to the operator. In order to achieve automatic hu

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Fink, Wolfgang, Alexander J. W. Brooks, Mark A. Tarbell, and James M. Dohm. "Tier-scalable reconnaissance: the future in autonomous C4ISR systems has arrived: progress towards an outdoor testbed." SPIE-INT SOC OPTICAL ENGINEERING, 2017. http://hdl.handle.net/10150/626010.

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Autonomous reconnaissance missions are called for in extreme environments, as well as in potentially hazardous (e.g., the theatre, disaster-stricken areas, etc.) or inaccessible operational areas (e.g., planetary surfaces, space). Such future missions will require increasing degrees of operational autonomy, especially when following up on transient events. Operational autonomy encompasses: (1) Automatic characterization of operational areas from different vantages (i.e., spaceborne, airborne, surface, subsurface); (2) automatic sensor deployment and data gathering; (3) automatic feature extrac

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Salazar, González Fernando. "A machine learning based methodology for anomaly detection in dam behaviour." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/405808.

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Dam behaviour is difficult to predict with high accuracy. Numerical models for structural calculation solve the equations of continuum mechanics, but are subject to considerable uncertainty as to the characterisation of materials, especially with regard to the foundation. As a result, these models are often incapable to calculate dam behaviour with sufficient precision. Thus, it is difficult to determine whether a given deviation between model results and monitoring data represent a relevant anomaly or incipient failure. By contrast, there is a tendency towards automatising dam monitoring de

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Majeed, Khurram. "Behaviour based anomaly detection system for smartphones using machine learning algorithm." Thesis, London Metropolitan University, 2015. http://repository.londonmet.ac.uk/1199/.

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In this research, we propose a novel, platform independent behaviour-based anomaly detection system for smartphones. The fundamental premise of this system is that every smartphone user has unique usage patterns. By modelling these patterns into a profile we can uniquely identify users. To evaluate this hypothesis, we conducted an experiment in which a data collection application was developed to accumulate real-life dataset consisting of application usage statistics, various system metrics and contextual information from smartphones. Descriptive statistical analysis was performed on our datas

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Isupova, Olga. "Machine learning methods for behaviour analysis and anomaly detection in video." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/17771/.

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Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modeling and change point detection methodologies, respectively, are employed to achieve these objectives. The thesis starts with development of novel learning algorithms for a dynamic topic model. Topics extracted by the learning algorithms represent typical activities happening within an observed scene. These typical activities are

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Westerlind, Simon. "Anomaly Detection for Portfolio Risk Management : An evaluation of econometric and machine learning based approaches to detecting anomalous behaviour in portfolio risk measures." Thesis, KTH, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232131.

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Financial institutions manage numerous portfolios whose risk must be managed continuously, and the large amounts of data that has to be processed renders this a considerable effort. As such, a system that autonomously detects anomalies in the risk measures of financial portfolios, would be of great value. To this end, the two econometric models ARMA-GARCH and EWMA, and the two machine learning based algorithms LSTM and HTM, were evaluated for the task of performing unsupervised anomaly detection on the streaming time series of portfolio risk measures. Three datasets of returns and Value-at-Ris

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Hamid, Muhammad Raffay. "A computational framework for unsupervised analysis of everyday human activities." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24765.

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Thesis (Ph.D.)--Computing, Georgia Institute of Technology, 2009.<br>Committee Chair: Aaron Bobick; Committee Member: Charles Isbell; Committee Member: David Hogg; Committee Member: Irfan Essa; Committee Member: James Rehg

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Chapčák, David. "Behaviorální analýza síťového provozu a detekce útoků (D)DoS." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-317014.

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The semestral thesis deals with the analysis of the modern open-source NIDPS tools for monitoring and analyzing the network traffic. The work rates these instruments in terms of their network location and functions. Also refers about more detailed analysis of detecting and alerting mechanisms. Further analyzes the possibilities of detection of anomalies, especially in terms of statistical analysis and shows the basics of other approaches, such as approaches based on data mining and machine learning. The last section presents specific open-source tools, deals with comparison of their activities

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Homoliak, Ivan. "Detekce Útoků v Síťovém Provozu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-261231.

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Tato práce se zabývá problematikou anomální detekce síťových útoků s využitím technik strojového učení. Nejdříve jsou prezentovány state-of-the-art datové kolekce určené pro ověření funkčnosti systémů detekce útoků a také práce, které používají statistickou analýzu a techniky strojového učení pro nalezení síťových útoků. V další části práce je prezentován návrh vlastní kolekce metrik nazývaných Advanced Security Network Metrics (ASNM), který je součástí konceptuálního automatického systému pro detekci průniků (AIPS). Dále jsou navrženy a diskutovány dva různé přístupy k obfuskaci - tunelování

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Tsai, Wan-yi, and 蔡宛宜. "Malicious Web Page Detection Based on Anomaly Behavior." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/zd9vz6.

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碩士<br>國立中山大學<br>資訊管理學系研究所<br>97<br>Because of the convenience of the Internet, we rely closely on the Internet to do information searching and sharing, forum discussion, and online services. However, most of the websites we visit are developed by people with limited security knowledge, and this condition results in many vulnerabilities in web applications. Unfortunately, hackers have successfully taken advantage of these vulnerabilities to inject malicious JavaScript into compromised web pages to trigger drive-by download attacks. Based on our long time observation of malicious web pages, mali

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Hsiao, Shun-Wen, and 蕭舜文. "Service Behavior Profiling and Probabilistic Inference for Anomaly Detection." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/37434761568581780626.

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博士<br>國立臺灣大學<br>資訊管理學研究所<br>100<br>Network attacks that exploit network service vulnerabilities become popular in recent years. An attacker can remotely send malicious messages to a vulnerable service and gain the execution right to control the victim. Most of the Internet worms and part of the Botnet fall into this attack category, and such attacks often cause severe damages to our computers and network systems. As we know, benign software would perform normal procedure to communication with a server to accomplish a network task collaboratively via predefined network protocols. Although malwa

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Yang, Yi-Hsiang, and 楊逸翔. "User Behavior Anomaly Detection via System Usage and Active Process Patterns." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/ry9jw7.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>100<br>Cloud computing is a hot topic in the global IT industry, which is considered as the main part of the network and computing service providers in recent years. Some security issues will be more threatening in cloud computing, such as account theft and insider threat. In a cloud service, the attacker can steal all the data of the account owner. We proposed a framework to utilize anomaly detection techniques for pro ling user's behavior via two feature sets, and the user's pro le is used for detecting all the suspicious behaviors. The rst feature set is extracte

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Mao, Ching-Hao, and 毛敬豪. "Sequence-based Anomaly Detection for Analyzing and Identifying Malicious Network Behavior." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/52423721360737997609.

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博士<br>國立臺灣科技大學<br>資訊工程系<br>99<br>Malicious behaviors with similar intent or purpose often possess different data sequence patterns which increase the difficulty in malicious behavior identification. These data sequence variations originated from the following three sources: (1) the multiplicity of causal relationships appearing in data sequence patterns, (2) the injection of noises in the attack sequence, and (3) the interwoven of various malicious behaviors. These problems make the current intrusion detection systems perform poorly in capturing the causal relation of malicious behaviors, or p

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Jian, Ru-Yu, and 簡如玉. "User Behavior-based Anomaly Detection for SMS Fraud on Mobile Devices." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/02661796997275814024.

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碩士<br>國立宜蘭大學<br>資訊工程研究所碩士班<br>101<br>Smart phones can be equipped with many applications. The security threats from malicious programs are increasing with more and more applications. SMS Trojans is the most popular smart phone threats that is accounting for 63.6% percent of all attacks. This thesis aims at the SMS fraud issue and proposes an anomaly detection mechanism based on the user behavior. User's behavior is not easy imitated by malicious program. Mobile phone users send text messages during the screen backlight is on. Basically, users send fewer messages to the foreign countries. Smart

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Lin, Wei-Cheng, and 林韋成. "The Design of User-based Behavior Miner and Anomaly Detection Mechanism." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/w5zxws.

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碩士<br>國立雲林科技大學<br>電子與資訊工程研究所<br>96<br>Recently, Internet applications have been developed rapidly that bring people new life style. At the same time, maliciocus activities occur on the Internet that many defense mechanisms are proposed to prevent the enterprise systems from illegal intrusion. Many anomaly detection systems assume a baseline of normal network to detect intrusion activities, but such a baseline represents the behavior of a group not individual. We implement the following four functions to make the anomaly detection based on behavior of individual user to cause high wrong. (a)Ide

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Li, Guan-Yi, and 李冠儀. "An Anomaly Detection System of User Behavior based on Windows Registry." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/60090614559891690514.

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碩士<br>國立中央大學<br>資訊管理研究所<br>94<br>As the number of computers is getting higher recently, the importance of computer security is recognized bye more and more computer users. Windows series are the most popular OS in the world, and their popularity triggers lots of security issues. If the user of a computer can understand the state of his/her computer, he/she may detect the malicious behavior and protect his/her computer. Our research takes use of Windows Registry and Support Vector Machine (SVM) to probe the state of a computer in order to determine whether the behavior of user is normal or abno

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Lin, Po-Yu, and 林柏宇. "Abnormal Crowded Behavior Detection using Ensemble Kernel Based Direct Density Ratio Estimation and Consistent Anomaly Detection." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/53691156889083837420.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>103<br>This thesis proposes a novel inlier-based outlier detection scheme for anomaly detection in crowded scenes based on the Kullback-Leibler importance estimation procedure (KLIEP). The video is first segmented into a set of local spatio-temporal cubes, from which three feature descriptors such as the histograms of oriented gradient (HOG), the motion direction descriptors, and the motion magnitude descriptors are then extracted. Thereafter, for each feature descriptor KLIEP is invoked, which computes the ratio of test and training densities, called the importance

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Alvarez, Cid-Fuentes Javier. "Adaptive anomalous behavior identification in large-scale distributed systems." Thesis, 2017. http://hdl.handle.net/2440/112593.

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Distributed systems have become pervasive in current society. From laptops and mobile phones, to servers and data centers, most computers communicate and coordinate their activities through some kind of network. Moreover, many economic and commercial activities of today’s society rely on distributed systems. Examples range from widely used large-scale web services such as Google or Facebook, to enterprise networks and banking systems. However, as distributed systems become larger, more complex, and more pervasive, the probability of failures or malicious activities also increases, to the point

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Karim, A., Salleh R. Bin, M. Shiraz, S. A. A. Shah, Irfan U. Awan, and N. B. Anuar. "Botnet detection techniques: review, future trends, and issues." 2014. http://hdl.handle.net/10454/10659.

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No<br>In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange

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Leng, Geng-Weng, and 連建文. "Design Behavior Anomaly Detection Modules on an Open Knowledge Management Platform:Using Elementary School Examples." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/80272390308049601505.

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碩士<br>華梵大學<br>工業工程與經營資訊學系碩士班<br>93<br>With the rapid changes of the social environment and educational system, cases of students’ disordered behaviors happen frequently. It is very important to effectively detect and prevent them in time. This research design and implement alert models on a knowledge management platform to automatically detect the disordered symptoms that helps educators to prevent the disordered behaviors of the students at the beginning. We first review the papers and documents, gather data, and analyze cases to understand the meaning of anomaly behavior and the bases of t

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Lai, Meng-Sheng, and 賴孟昇. "Behavior Anomaly Detection in SDN Control Plane: A Case Study of Topology Discovery Attacks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/smbt8h.

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碩士<br>國立中央大學<br>資訊工程學系<br>107<br>With the rapid development of information technology and the popularity of smart devices, users' demand for instant processing of network services and diversified services has also increased significantly, making the architecture of traditional network services unable to meet the rapidly changing network architecture of emerging services Demand. Software-defined Networking (SDN) and Network Function Virtualization (NFV) have therefore been proposed to transform complex network architectures into virtual and programmable architectures to reduce network complexit

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Cardoso, Nuno André de Matos Lopes. "User behavior analytics in the contact center: Insider threat assessment and fraud detection." Master's thesis, 2021. http://hdl.handle.net/10316/96092.

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Dissertação de Mestrado em Segurança Informática apresentada à Faculdade de Ciências e Tecnologia<br>Com o crescimento contínuo do cibercrime em anos recentes, a necessidade de estratégias de cibersegurança inovadoras e eficazes tornam-se cada vez mais importantes para todo o tipo de organizações. Fundada em 2011, a Talkdesk está a desenvolver um produto de contact-center na nuvem, que chegou aos 1800 clientes em 2020. As carreiras em contactcenter são conhecidas pela precariedade e alta rotatividade de agentes que, agravada pela tendência de trabalho remoto imposta pela pandemia de COVID-19,

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Gama, José Martim Mendes de Vasconcellos Rebelo da. "Implementing machine learning for data breach detection." Master's thesis, 2020. http://hdl.handle.net/10362/111618.

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Privata. ai is a User and Entity Behavior Analytics (UEBA) application used for the detection of data breaches in an organization. By tracking down the usual access to personal and sensitive data, it becomes much easier to detect an outlier. These anomalies could result in a real threat to the company’s data security and must, therefore, be promptly detected and addressed. This paper focuses on the managerial challenges that arise from the increasing threat of data breaches and how machine learning could help in protecting organizations from them. For this purpose, large part of the challenge

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KUMAR, AMIT. "VIDEO BEHAVIOUR PROFILING AND ANOMALY DETECTION." Thesis, 2012. http://dspace.dtu.ac.in:8080/jspui/handle/repository/13903.

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M.TECH<br>Public security has become a major issue in public places such as subway stations, banks, malls, airports, etc. Recently we have seen that terrorist activities are growing all over the world. To monitor these kinds of activities, there is an increasing demand of automatic video surveillance systems. In a surveillance system, we need to study the behaviour of the environment whether there is any abnormality in the video or not, in real time. Due to this for real time application in surveillance systems, video behaviour profiling has been a topic of great interest in real time. In

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Biswas, Sovan. "Motion Based Event Analysis." Thesis, 2014. http://etd.iisc.ernet.in/2005/3502.

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Motion is an important cue in videos that captures the dynamics of moving objects. It helps in eﬀective analysis of various event related tasks such as human action recognition, anomaly detection, tracking, crowd behavior analysis, traffic monitoring, etc. Generally, accurate motion information is computed using various optical flow estimation techniques. On the other hand, coarse motion information is readily available in the form of motion vectors in compressed videos. Utilizing these encoded motion vectors reduces the computational burden involved in flow estimation and enables rapid analys

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Lin, Hsiao-Chung, and 林孝忠. "Detecting Centralized Botnets based on Anomaly Traffic Behaviors." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/11379309391077389363.

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博士<br>國立中山大學<br>資訊管理學系研究所<br>101<br>Cybercrime presents great security challenges for Internet and makes Internet security gain a lot of attention by academic domain. Cybercrime takes advantage of Internet for gaining revenue and profit, sending malicious mail, compromising computer hosts, stealing confidential information and launching distributed denial of service (DDoS) attacks. Cyber-attacks which compromise the security such as confidentiality, integrity, availability of a computer and network system are often carried out by botnets. Botnets have become a serious threat to the stability o

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Pelizzari, Andrea. "Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications." Master's thesis, 2016. http://hdl.handle.net/10362/17346.

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Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence<br>Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range

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Biswas, Sovan. "Motion Based Event Analysis." Thesis, 2014. http://etd.iisc.ac.in/handle/2005/3502.

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Abstract:

Motion is an important cue in videos that captures the dynamics of moving objects. It helps in eﬀective analysis of various event related tasks such as human action recognition, anomaly detection, tracking, crowd behavior analysis, traffic monitoring, etc. Generally, accurate motion information is computed using various optical flow estimation techniques. On the other hand, coarse motion information is readily available in the form of motion vectors in compressed videos. Utilizing these encoded motion vectors reduces the computational burden involved in flow estimation and enables rapid analys

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Fernandes, Letícia Maria Sousa. "Learning Human Behaviour Patterns by Trajectory and Activity Recognition." Master's thesis, 2019. http://hdl.handle.net/10362/87075.

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The world’s population is ageing, increasing the awareness of neurological and behavioural impairments that may arise from the human ageing. These impairments can be manifested by cognitive conditions or mobility reduction. These conditions are difficult to be detected on time, relying only on the periodic medical appointments. Therefore, there is a lack of routine screening which demands the development of solutions to better assist and monitor human behaviour. The available technologies to monitor human behaviour are limited to indoors and require the installation of sensors around the

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