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Дисертації з теми "Behavior-based Detection"

Автор: Grafiati
Опубліковано: 7 червня 2025
Оновлено: 2 серпня 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Sadeghian, Paria. "Human mobility behavior : Transport mode detection by GPS data." Licentiate thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-36346.

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Анотація:
GPS tracking data are widely used to understand human travel behavior and to evaluate the impact of travel. A major advantage with the usage of GPS tracking devices for collecting data is that it enables the researcher to collect large amounts of highly accurate and detailed human mobility data. However, unlabeled GPS tracking data does not easily lend itself to detecting transportation mode and this has given rise to a range of methods and algorithms for this purpose. The algorithms used vary in design and functionality, from defining specific rules to advanced machine learning algorithms. Th
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12

Tamersoy, Acar. "Graph-based algorithms and models for security, healthcare, and finance." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54986.

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Graphs (or networks) are now omnipresent, infusing into many aspects of society. This dissertation contributes unified graph-based algorithms and models to help solve large-scale societal problems affecting millions of individuals' daily lives, from cyber-attacks involving malware to tobacco and alcohol addiction. The main thrusts of our research are: (1) Propagation-based Graph Mining Algorithms: We develop graph mining algorithms to propagate information between the nodes to infer important details about the unknown nodes. We present three examples: AESOP (patented) unearths malware lurking
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13

Myles, Kimberly. "Activity-Based Target Acquisition Methods for Use in Urban Environments." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28422.

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Many military conflicts are fought in urban environments that subject the U.S. soldier to a number of challenges not otherwise found in traditional battle. In the urban environment, the soldier is subject to threatening attacks not only from the organized army but also from civilians who harbor hostility. U.S. enemies use the civilian crowd as an unconventional tactic to blend in and look like civilians, and in response to this growing trend, soldiers must detect and identify civilians as a threat or non-threat. To identify a civilian as a threat, soldiers must familiarize themselves with b
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14

Rhod, Eduardo Luis. "Proposal of two solutions to cope with the faulty behavior of circuits in future technologies." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/16086.

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A diminuição no tamanho dos dispositivos nas tecnologias do futuro traz consigo um grande aumento na taxa de erros dos circuitos, na lógica combinacional e seqüencial. Apesar de algumas potenciais soluções começarem a ser investigadas pela comunidade, a busca por circuitos tolerantes a erros induzidos por radiação, sem penalidades no desempenho, área ou potência, ainda é um assunto de pesquisa em aberto. Este trabalho propõe duas soluções para lidar com este comportamento imprevisível das tecnologias futuras: a primeira solução, chamada MemProc, é uma arquitetura baseada em memória que propõe
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15

Sao, Pedro Michael A. "Real-time Assessment, Prediction, and Scaffolding of Middle School Students’ Data Collection Skills within Physical Science Simulations." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-dissertations/168.

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Анотація:
Despite widespread recognition by science educators, researchers and K-12 frameworks that scientific inquiry should be an essential part of science education, typical classrooms and assessments still emphasize rote vocabulary, facts, and formulas. One of several reasons for this is that the rigorous assessment of complex inquiry skills is still in its infancy. Though progress has been made, there are still many challenges that hinder inquiry from being assessed in a meaningful, scalable, reliable and timely manner. To address some of these challenges and to realize the possibility of formative
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16

Cheng, Yu-Lin, and 鄭郁霖. "Unusual Behavior Detection Based on Pedestrian Behavior Modeling." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/41738020024258829806.

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碩士<br>國立交通大學<br>電子研究所<br>100<br>In this paper, we propose a surveillance system which is able to detect abnormal behaviors automatically. Unlike other approaches, we do not detect abnormal behaviors by constructing a scene model with normal behaviors. Instead, we emphasize the important aspect of pedestrian since it is the pedestrian who performs abnormal behaviors. There are actually various pedestrian behaviors and it is difficult to express every pedestrian behavior in detail. However, it is still possible to model pedestrian behavior since pedestrian behavior, in general, always reflects s
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17

徐源隆. "Behavior-based Internet Auction Fraud Detection Techniques." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/a8c2ek.

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18

Wu, Chun-chih, and 吳濬志. "Malware Detection Based on Dynamic Behavior Analysis." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/n9898x.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>100<br>Malware detection has been one of the most important research topics since the time we start to use computers and the situation is expected to continue for years to come. In recent years, malware evolves into different forms with differ- ent intrusion intentions and the newly developed technologies like virtualization and cloud computing give a new aspect of malware detection. The basic cate- gorization of malware detection consists of static analysis and dynamic analysis. Both has different aspects and effectiveness: the static analysis can be done of- fline,
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19

Lin, Yi-Fang, and 林弋舫. "P2P Bot Detection based on Behavior Analysis." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/79745803301252112184.

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碩士<br>中原大學<br>資訊工程研究所<br>98<br>Botnet has become one of the primary threats to the security of cyberspace. Comprised computers implanted with bot malwares are controlled by bot herders to launch all kinds of cyber attack. In recent years, the operation model of botnet control has evolved from the centralized IRC or HTTP botnets to the decentralized P2P botnet. Botnets adopting the new P2P communication model allow bot herders the capability to deliver attack command from any node in a P2P botnet. Hence, the existing botnet detection or defense mechanisms based on the centralized operation
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20

Chen, Chun-Yang, and 陳君暘. "Smoke Behavior Detection Based on Human Face." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/23460545253950027883.

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碩士<br>真理大學<br>資訊工程學系碩士班<br>99<br>Since the implementation of Tobacco Hazards Prevention Act, the prohibition against smoking in public places tends to be stricter in Taiwan. Its emphasis level can be inferred from the frequent law amendments in recent years. In order to effectively improve the prevention effect and bring down the costs of human surveillance, this paper proposes a smoking behavior detection system based on the human face analysis, which can quickly and accurately detect whether the person in the image is smoking or not. First, the system makes use of the existing facial detecti
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21

Lee, Ting-Yu, and 李庭宇. "Packed Malware Detection Based on Behavior Classification." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/35905677235653065749.

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碩士<br>國立臺灣大學<br>電子工程學研究所<br>99<br>Anti-malware companies receive thousands of malware samples every day. And the malware increase kept surging in 2009 for historical new high. So, how to let the antivirus program more effective is an important and urgent problem. Traditionally, people detect malware by signature. However, if the malware is packed or the signature is changed, the antivirus program will not be able to find the malware. So we want to provide a new way to solve this problem. By Cisco’s research, 70%-80% malwares are packed. In this thesis, we provide a new way for detecting packed
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22

Lin, Ting-Hong, and 林庭弘. "Malicious code behavior detection based on reverse engineering." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/09643577328460372959.

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碩士<br>國立雲林科技大學<br>資訊管理系碩士班<br>99<br>In the past few years, the amount of the malicious program and the capability of destruction have become more and more. Malicious programs and their writers are also staring to use the packed technology of encryption and code obfuscation to avoid the detection from anti-virus software. Therefore, the packed technology has become a challenging problem to the anti-virus company. How to detect the malicious packed program is also the important issue of researches of the information security. This study uses the encryption and the other assistant feature to help
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23

Tsai, Li-Yang, and 蔡禮陽. "Behavior-based botnet detection in cloud computing environments." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/61669410226200214981.

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碩士<br>國立交通大學<br>網路工程研究所<br>100<br>In recent years, botnets become a major issue to Internet security; however, existing string signature-based matching methods usually lead to high false positive rates (FPR) and low true positive rates (TPR) for botnet detection. In this paper, we proposed a behavior-based botnet detection mechanism in cloud computing environments (BBDC). Our BBDC algorithm is divided into five stages: (1) traffic reduction: removing unwanted packets from an input trace for speeding up bot detection; (2) feature extraction: extracting features from the reduced input trace; (3)
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24

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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25

Yan, Meng-Jia, and 顏孟加. "Itus: Behavior-based Spamming Groups Detection on Facebook." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/2kpbk3.

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碩士<br>國立中央大學<br>資訊工程學系<br>102<br>Facebook is the largest online social network, and total number of daily active users on Facebook is more than 802 million in March 2014. Unfortunately, attackers are also expanding their territory to Facebook to propagate spam. One of the ways to propagate spam on Facebook is using Facebook Groups. Group’s members can invite their friends to join the Group without invitees’ permission. However, questions then arise about the friendly invitation mechanism. Using fake or compromised accounts, attackers can spread invitation to all friends, that is, not only the
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26

Chang, Wei-Ling, and 張瑋玲. "An Android Behavior-Based Malware Detection using Machine Learning." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/h5ukqd.

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碩士<br>國立清華大學<br>資訊工程學系<br>103<br>In recent years, smart phones become very popular. Lots of people use smart phones instead of traditional phones and almost everyone has one. More and more functional mobile applications are released and it makes user’s life more convenient. The popularity of Android attracts many developers to build not only useful and creative applications, but also some malicious software. Malware installed in user’s smart phone probably not merely steal some privacy information such as phone number, IMEI, credit card number among other but cause some property loss. Therefor
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27

蔡薰儀. "Suspicious Behavior-based Malware Detection Using Artificial Neural Network." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/80779298373258676583.

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碩士<br>國立交通大學<br>網路工程研究所<br>100<br>In the recent years, malware has been widely spread and has caused severe threats against cyber security. Although malware may be made some changes to evade the traditional signature-based detection, the malware and its variations still have some similar behaviors, which most of the malware also intent to do. In order to detect unknown malware and variations of known ones, we propose a behavioral artificial neural network-based malware detection (ANN-MD) system. By observing runtime behaviors of some known malware samples using three sandboxes, we listed 13 su
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28

YANG, SHAN-YI, and 楊善壹. "P2P Botnet Detection based on Network Behavior Similarity Evaluation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/49006421710006240805.

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碩士<br>逢甲大學<br>資訊工程學系<br>105<br>Recently, many devices are hacked and become bot. Hackers use C & C servers to control these bot. Bots usually hide their information in specific network traffic such as P2P traffic. It is not easy for network administrators to find these malicious traffics in P2P traffic. For the P2P botnet, if a bot is found and block, the hacker can issue commands from another bot and the botnet still works. In order to update the status of the entire P2P botnet, the bot master periodically sends commands to the bot. Bots also regularly download the peer list from other bots.
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29

Wu, Tsung-Yu, and 吳宗育. "IRC-Based Botnet Detection Using Nickname Pattern and Command Behavior." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/kq94p2.

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碩士<br>國立東華大學<br>資訊工程學系<br>100<br>A so-called botnet is a collection of vulnerable computers (bot) that infected with malware bot via social engineering, each of which can be remotely controlled by its originator (bot master) through Command-and-Control (C&C) infrastructure. The C&C can be IRC-based, HTTP-based or P2P-based, which allows bot master to disperse commands to its botnet for illegal purpose, e.g., such as launching distributed denial-of-service (DDoS) attack, stealing sensitive information, or sending spam emails. These protocols are widespread communication protocols on Internet, s
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30

Lin, Huan-Wei, and 林洹緯. "An Integrated Approach for System Call Based Malware Behavior Detection." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/98734646961608314340.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>102<br>Malware refers to the software that can harmuser’s computer such as virus, trojan and worm. The growth and metamorphic rate of new types of malware have been accelerated due to the de- velopment of the internet and the widespread of automated tools. Malware usually use encryp- tion, polymorphic and metamorphic to achieve the obfuscation of their signature, causing the traditional signature-based antivirus cannot detect them in real-time. Therefore, some system call based methods have been proposed in order to achieve real-time detection. These methods focus
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31

Wu, Chang-Huan, and 吳昌桓. "An Intrusion Detection System Based On Network Protocol Behavior Monitoring." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/31312189469420405535.

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碩士<br>臺灣大學<br>資訊管理學研究所<br>98<br>As Internet becomes more and more popular, it makes our life more colorful and convenient. In the other hand, more and more attacks happened on the Internet. Attacks by computer worms often make enormous impacts and damages. Besides, as attacks become more sophisticated, traditional intrusion detection approaches, like payload signature matching and network traffic pattern monitoring are not sufficient to detect new attacks. Computer worms made huge impact and damage due to its auto-attacking and spreading characteristic. Worms often attack vulnerable programs
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32

Sun, Chien-Hsing, and 孫建興. "A Behavior-Based Malware Detection Study on the Android Devices." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/03616967944178705782.

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碩士<br>大同大學<br>資訊經營學系(所)<br>100<br>Thanks to the vigorous development on mobile broadband in recent years, smart phones have gradually replaced traditional cell phones and become the main stream in cell phone market. Due to abounding private information can be stored up inside smart phones and according to the cell phone malware reports from some information security companies, Trojan can stole private information and has become the most infamous malware among malicious software. This research will focus on information and communication issues from the smart mobile device. This research will ai
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33

Ying, Nien-Ting, and 應念廷. "The Vision-Based Recognition and Detection for Indoor Smoking Behavior." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/emrr5w.

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碩士<br>國立臺北科技大學<br>資訊工程系研究所<br>97<br>This paper introduces a method based on vision image processing to detect smoke in the indoor environment. This smoking surveillance system captures stable video with a fixed camera and detect smoke in real time. One feature of smoke which generated from cigarettes is translucent. According to this feature, system finds smoke in images by translating RGB color space into HSI color space. Face detection in the system discovers the smokers and locates their faces. Finally, this paper define the relationship between smoke and faces according to the results of s
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34

Chien, Tzu-Chih, and 簡子智. "Deep learning based driver smoking behavior detection for driving safety." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5441094%22.&searchmode=basic.

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碩士<br>國立中興大學<br>電機工程學系所<br>107<br>Researches and analyses from the US bureau of safety experts reveals that the probability of car accidents for smokers is 1.5 times larger than that for non-smokers. The United Kingdom and Germany experts believe that the 5% of car accidents are related to smoking while driving. According to the US experts, the smoking behavior leads to three driving hazards: reduced vision, distracting, and odors stimulations. People need a high degree of attention and a clear vision while driving, and the smoking behavior may irritate eyes and respiratory tract, and then th
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35

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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36

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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SU, YUNG-HU, and 蘇永護. "Detection and Blocking of Botnet Packet Behavior Based on DNS Packet." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/18310195884754462430.

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碩士<br>大同大學<br>資訊工程學系(所)<br>98<br>Botnet is one of the most dangerous and fastest growing network threats at present. No matter it is individual, school, company or organization, the Botnet have constituted a serious threat. How to detect computers inside the LAN to determine if they are being affected by Botnet virus has become a major challenge. In this study, we focus on the HTTP Based Botnet detection and blocking mechanism. We develop an algorithm to measure the Botnet Packet behavior to detect Botnet infection. By this algorithm, we can effectively detect one of the Botnet viruses. By
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38

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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39

Peng, Chien-Fu, and 彭建福. "Using Aggregation Technology to Improve System Call Based Malware Behavior Detection." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/18515256366677728449.

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碩士<br>國立中央大學<br>資訊管理研究所<br>99<br>Malware is one kind of software which has intention to attack computer systems. In recent years there has significant increase in the number of malware, in addition malware also use polymorphism, obfuscation and packing technologies to protect itself. For the above reason, the effect of traditional static malware detection technology is restricted, as a result in recent years many studies focused on dynamic malware detection technology. However most of the previous studies are process center oriented, which mean these studies only monitor one process’s behavior
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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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41

Sen-ChengYang and 楊森鎮. "A Status-Transition-Based Sleeper’s Behavior Detection System with Multi-sensors." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/47460577807436181655.

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碩士<br>國立成功大學<br>電腦與通信工程研究所<br>101<br>The body behavior of sleeper during night plays an important role for personal health. Because of that, we proposed a status-transition-based sleeper’s behavior detection system with multi-sensors to detect the body behavior during night, including body-turning and sleeping pose on bed, get out of bed, standing and walking. The detected results not only make user understand the daily behavior himself, but also can help doctor to diagnose the user’s disease. The proposed system adopted the contacted accelerometer sensors, and utilized a status transiting mec
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42

Lee, Kai-Ren, and 李鎧任. "A Study of Fall Detection and Prediction Based on Behavior Analysis." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/y3ygbx.

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碩士<br>國立虎尾科技大學<br>資訊工程研究所<br>102<br>Because of the gradual degradation of health, lots of risks occur in daily living of aging people. Among of them, the injuries caused by falls are the most common serious accident. In recent years, the topic about fall detection has been widely discussed in the area of elder health care. In literature reviews, there are three main possible approaches which include image-based technique, context aware method, and acceleration-based detection approach. Nowadays, due to the rapid growing of population using smartphone and the development of hardware technique,
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43

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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Chen, Yu-jie, and 陳裕傑. "Early Detection for Botnet Traffic Behavior Based on Hybrid Hidden Markov Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/00204155574061189231.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>96<br>Identifying Botnet in Command and Control (C&C) channel from network traffic still remains a main difficulty due to ambiguous traffics between Botnet and normal communications. In the early stage of Botnet C&C, however, the diverse behaviors are hard to be detected and difficult to be distinguished from usual fluctuations. In this study, we propose a novel approach for Botnet early detection by modeling the phenomena of bursty traffic within different phases of behaviors of bot. Since communication of Botnet is temporal interdependencies, we consider that netwo
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Hsieh, Wei-Yung, and 謝維揚. "MalCatcher: Private and Network Data Leakage Behavior-Based Malware Detection on Android." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/36430407192563924213.

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碩士<br>國立交通大學<br>網路工程研究所<br>101<br>More and more people use smartphones in the world. People put more and more their own personal private information into smartphones, so it is important to secure the mobile system, especially Android. Due to the fact that Android is an open-source system, it is easier to develop malwares on Android. In recent years, the number of malwares is dramatically increasing and evolving on Android. We need a effective approach to keep up the speed of malwares’ changes. In this paper, we propose a new dynamic analysis scheme for malware detection on Android, we monitor
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46

Hsu, Peng-Kai, and 徐鵬凱. "Three-phase Behavior-based Detection and Classification of Known and Unknown Malware." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/82426559073938778368.

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碩士<br>國立交通大學<br>網路工程研究所<br>101<br>In recent years, many anti-malware solutions have been proposed. To improve both detection accuracy and time efficiency for known and even unknown malware, we propose a three-phase behavior-based malware detection and classification approach, with a fast detector in the 1st-phase to filter most programs, a slow detector in the 2nd-phase, and then a classifier at the 3rd to tell the malware type. The fast detector runs programs in a sandbox to extract external behaviors fed into a trained artificial neural network (ANN) to evaluate their maliciousness, while th
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47

Weng, Shi-hao, and 翁世豪. "The Behavior-based Intrusion Detection and Response System for the Internet worm." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/44185936348522195286.

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碩士<br>淡江大學<br>資訊工程學系<br>90<br>With the popularization of the Internet and diversification of network service, the malicious worm is one of the major damages in network security issue currently. The worm can attack a large number of computers via network in very short time, especially distributed damage via the network services. Those worms always enter or attack computer by backdoor or under-channel. There is no effective solution to prevent from these damages occurred by worms. In this paper, we proposed a behavior-based intrusion detection and response system for the Internet worm. Our syste
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Chun-Sheng, Wang, and 王俊聖. "SBModel : Social Behavior based Modeling to Spear Phishing Detection with Cosine Theory." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/94734645453003592090.

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碩士<br>國立中正大學<br>通訊工程研究所<br>103<br>Email applications provide an efficient communication way between people for both personal and business purposes. Due to the convenience and popularity of email, hackers can use spear phishing techniques to trick the target users into revealing sensitive information by either clicking a malicious web site link or opening malicious attachments. In the past, many researchers proposed phishing prevention technologies, but they were not successful in fighting spear phishing emails. In order to reduce the threat of spear phishing, we propose a system, called SBMode
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49

Jhang, Zong-Cyuan, and 張宗銓. "An Network Behavior-Based Botnet Detection Mechanism Using PSO and K-means." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/11251804354229107211.

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碩士<br>大同大學<br>事業經營學系(所)<br>100<br>Nowadays, Botnet has become one of the greatest threats to the network security. Network attackers can use Botnet to launch the distributed denial of service (DDoS) to paralyze the important websites or to steal the confidential data from infected computer, use fishing attacking to steal the sensitive information such as the account and password, send bulk email advertising or conduct click fraud. Even though the detection technology has got improved and some solutions to Internet security have been proposed, the threat of Botnet still exists. Most previous st
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50

Lin, Cyun-Yi, and 林群益. "Machine Learning and Gradient Statistics Based Real-Time Driver Drowsiness and Behavior Detection." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/45018109574447682059.

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碩士<br>國立中興大學<br>電機工程學系所<br>105<br>For the past few years, many accidents caused by the fatigue driving have occurred frequently. Therefore, many researchers and experts all over the world have paid great efforts in this issue. For the fatigue detection issue, the driver’s spirit status can be evaluated through the eye blinking condition. In this thesis, by recognizing the accurate eye position, the eye detection methodology is proposed to enhance the accuracy of fatigue detections. The proposed system includes four parts, which are the face detection, the eye-glasses bridge detection, the eye
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