Relevant bibliographies by topics / Drive-by download

Contents

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

Academic literature on the topic 'Drive-by download'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Drive-by download"

1

ZHANG, Hui-Lin, Wei ZOU, and Xin-Hui HAN. "Drive-by-Download Mechanisms and Defenses." Journal of Software 24, no. 4 (January 14, 2014): 843–58. http://dx.doi.org/10.3724/sp.j.1001.2013.04376.

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Sood, Aditya K., and Sherali Zeadally. "Drive-By Download Attacks: A Comparative Study." IT Professional 18, no. 5 (September 2016): 18–25. http://dx.doi.org/10.1109/mitp.2016.85.

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Javed, Amir, Pete Burnap, and Omer Rana. "Prediction of drive-by download attacks on Twitter." Information Processing & Management 56, no. 3 (May 2019): 1133–45. http://dx.doi.org/10.1016/j.ipm.2018.02.003.

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Javed, Amir, Pete Burnap, Matthew L. Williams, and Omer F. Rana. "Emotions Behind Drive-by Download Propagation on Twitter." ACM Transactions on the Web 14, no. 4 (September 4, 2020): 1–26. http://dx.doi.org/10.1145/3408894.

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Nag, Amruth, and Sowmya M S. "Solution for Deceptive Download Buttons and Drive-By Installation." International Journal of System Modeling and Simulation 3, no. 4 (December 30, 2018): 1. http://dx.doi.org/10.24178/ijsms.2018.3.4.01.

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Deceptive download buttons are those on the internet which may look like it is meant to do an action but actually does something else which it is not intended by the user. Drive-by installations are those extra software which is installed in a user's device without him noticing it. This paper gives the information about the types of deceptive download buttons and the ways a common user can avoid clicking on such button while browsing the web. A few methods the advertiser may commonly use for making the end user a victim of the deceptive download buttons is discussed here. The methods by which someone can avoid these kinds of buttons are also given in this paper. An idea for the development of an extension to the user's browser, that runs in the background. This warns the user about those buttons prior to the button being clicked. This paper also includes information about drive-by installations of bloatware and the ways in which the end users are being affected by this, how the end user can avoid being a victim of drive-by installations and an idea to develop a tool which runs in the background as a small script in the operating system which will automatically warn the user about the drive-by installations.
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Aldwairi, Monther, Musaab Hasan, and Zayed Balbahaith. "Detection of Drive-by Download Attacks Using Machine Learning Approach." International Journal of Information Security and Privacy 11, no. 4 (October 2017): 16–28. http://dx.doi.org/10.4018/ijisp.2017100102.

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Drive-by download refers to attacks that automatically download malwares to user's computer without his knowledge or consent. This type of attack is accomplished by exploiting web browsers and plugins vulnerabilities. The damage may include data leakage leading to financial loss. Traditional antivirus and intrusion detection systems are not efficient against such attacks. Researchers proposed plenty of detection approaches mostly passive blacklisting. However, a few proposed dynamic classification techniques, which suffer from clear shortcomings. In this paper, we propose a novel approach to detect drive-by download infected web pages based on extracted features from their source code. We test 23 different machine learning classifiers using data set of 5435 webpages and based on the detection accuracy we selected the top five to build our detection model. The approach is expected to serve as a base for implementing and developing anti drive-by download programs. We develop a graphical user interface program to allow the end user to examine the URL before visiting the website. The Bagged Trees classifier exhibited the highest accuracy of 90.1% and reported 96.24% true positive and 26.07% false positive rate.
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TAKATA, Yuta, Mitsuaki AKIYAMA, Takeshi YAGI, Takeo HARIU, and Shigeki GOTO. "MineSpider: Extracting Hidden URLs Behind Evasive Drive-by Download Attacks." IEICE Transactions on Information and Systems E99.D, no. 4 (2016): 860–72. http://dx.doi.org/10.1587/transinf.2015icp0013.

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Jayasinghe, Gaya K., J. Shane Culpepper, and Peter Bertok. "Efficient and effective realtime prediction of drive-by download attacks." Journal of Network and Computer Applications 38 (February 2014): 135–49. http://dx.doi.org/10.1016/j.jnca.2013.03.009.

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Hsu, Fu-Hau, Chang-Kuo Tso, Yi-Chun Yeh, Wei-Jen Wang, and Li-Han Chen. "BrowserGuard: A Behavior-Based Solution to Drive-by-Download Attacks." IEEE Journal on Selected Areas in Communications 29, no. 7 (August 2011): 1461–68. http://dx.doi.org/10.1109/jsac.2011.110811.

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Nappa, Antonio, M. Zubair Rafique, and Juan Caballero. "The MALICIA dataset: identification and analysis of drive-by download operations." International Journal of Information Security 14, no. 1 (June 21, 2014): 15–33. http://dx.doi.org/10.1007/s10207-014-0248-7.

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Dissertations / Theses on the topic "Drive-by download"

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Puttaroo, Mohammad Ally Rehaz. "A behavioural study in runtime analysis environments and drive-by download attacks." Thesis, University of West London, 2017. https://repository.uwl.ac.uk/id/eprint/4751/.

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In the information age, the growth in availability of both technology and exploit kits have continuously contributed in a large volume of websites being compromised or set up with malicious intent. The issue of drive-by-download attacks formulate a high percentage (77%) of the known attacks against client systems. These attacks originate from malicious web-servers or compromised web-servers and attack client systems by pushing malware upon interaction. Within the detection and intelligence gathering area of research, high-interaction honeypot approaches have been a longstanding and well-established technology. These are however not without challenges: analysing the entirety of the world wide web using these approaches is unviable due to time and resource intensiveness. Furthermore, the volume of data that is generated as a result of a run-time analysis of the interaction between website and an analysis environment is huge, varied and not well understood. The volume of malicious servers in addition to the large datasets created as a result of run-time analysis are contributing factors in the difficulty of analysing and verifying actual malicious behaviour. The work in this thesis attempts to overcome the difficulties in the analysis process of log files to optimise malicious and anomaly behaviour detection. The main contribution of this work is focused on reducing the volume of data generated from run-time analysis to reduce the impact of noise within behavioural log file datasets. This thesis proposes an alternate approach that uses an expert lead approach to filtering benign behaviour from potentially malicious and unknown behaviour. Expert lead filtering is designed in a risk-averse method that takes into account known benign and expected behaviours before filtering the log file. Moreover, the approach relies upon behavioural investigation as well as potential for 5 system compromisation before filtering out behaviour within dynamic analysis log files. Consequently, this results in a significantly lower volume of data that can be analysed in greater detail. The proposed filtering approach has been implemented and tested in real-world context using a prudent experimental framework. An average of 96.96% reduction in log file size has been achieved which is transferable to behaviour analysis environments. The other contributions of this work include the understanding of observable operating system interactions. Within the study of behaviour analysis environments, it was concluded that run-time analysis environments are sensitive to application and operating system versions. Understanding key changes in operating systems behaviours within Windows is an unexplored area of research yet Windows is currently one of the most popular client operating system. As part of understanding system behaviours for the creation of behavioural filters, this study undertakes a number of experiments to identify the key behaviour differences between operating systems. The results show that there are significant changes in core processes and interactions which can be taken into account in the development of filters for updated systems. Finally, from the analysis of 110,000 potentially malicious websites, typical attacks are explored. These attacks actively exploited the honeypot and offer knowledge on a section of the active web-based attacks faced in the world wide web. Trends and attack vectors are identified and evaluated.
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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, we address the problem of host-based system anomaly detection, specifically by predicting expected behaviors of programs and detecting run-time deviations and anomalies. We first introduce an approach for detecting the drive-by download attack, which is one of the major vectors for malware infection. Our tool enforces the dependencies between user actions and system events, such as file-system access and process execution. It can be used to provide real time protection of a personal computer, as well as for diagnosing and evaluating untrusted websites for forensic purposes. We perform extensive experimental evaluation, including a user study with 21 participants, thousands of legitimate websites (for testing false alarms), 84 malicious websites in the wild, as well as lab reproduced exploits. Our solution demonstrates a usable host-based framework for controlling and enforcing the access of system resources. Secondly, we present a new anomaly-based detection technique that probabilistically models and learns a program's control flows for high-precision behavioral reasoning and monitoring. Existing solutions suffer from either incomplete behavioral modeling (for dynamic models) or overestimating the likelihood of call occurrences (for static models). We introduce a new probabilistic anomaly detection method for modeling program behaviors. Its uniqueness is the ability to quantify the static control flow in programs and to integrate the control flow information in probabilistic machine learning algorithms. The advantage of our technique is the significantly improved detection accuracy. We observed 11 up to 28-fold of improvement in detection accuracy compared to the state-of-the-art HMM-based anomaly models. We further integrate context information into our detection model, which achieves both strong flow-sensitivity and context-sensitivity. Our context-sensitive approach gives on average over 10 times of improvement for system call monitoring, and 3 orders of magnitude for library call monitoring, over existing regular HMM methods. Evaluated with a large amount of program traces and real-world exploits, our findings confirm that the probabilistic modeling of program dependences provides a significant source of behavior information for building high-precision models for real-time system monitoring. Abnormal traces (obtained through reproducing exploits and synthesized abnormal traces) can be well distinguished from normal traces by our model.
Ph. D.
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Canali, Davide. "Plusieurs axes d'analyse de sites web compromis et malicieux." Thesis, Paris, ENST, 2014. http://www.theses.fr/2014ENST0009/document.

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L'incroyable développement du World Wide Web a permis la création de nouveaux métiers, services, ainsi que de nouveaux moyens de partage de connaissance. Le web attire aussi des malfaiteurs, qui le considèrent comme un moyen pour gagner de l'argent en exploitant les services et la propriété d'autrui. Cette thèse propose une étude des sites web compromis et malicieux sous plusieurs axes d'analyse. Même si les attaques web peuvent être de nature très compliquées, on peut quasiment toujours identifier quatre acteurs principaux dans chaque cas. Ceux sont les attaquants, les sites vulnérables hébergés par des fournisseurs d'hébergement, les utilisateurs (souvent victimes des attaques), et les sociétés de sécurité qui parcourent Internet à la recherche de sites web compromis à être bloqués. Dans cette thèse, nous analysons premièrement les attaques web du point de vue des hébergeurs, en montrant que, même si des outils gratuits permettent de détecter des signes simples de compromission, la majorité des hébergeurs échouent dans cette épreuve. Nous passons en suite à l'analyse des attaquants et des leurs motivations, en étudiant les attaques web collectés par des centaines de sites web vulnérables. Ensuite, nous étudions le comportement de milliers de victimes d'attaques web, en analysant leurs habitudes pendant la navigation, pour estimer s'il est possible de créer des "profils de risque", de façon similaire à ce que les compagnies d'assurance font aujourd'hui. Enfin, nous adoptons le point de vue des sociétés de sécurité, en proposant une solution efficace pour la détection d'attaques web convoyées par sites web compromis
The incredible growth of the World Wide Web has allowed society to create new jobs, marketplaces, as well as new ways of sharing information and money. Unfortunately, however, the web also attracts miscreants who see it as a means of making money by abusing services and other people's property. In this dissertation, we perform a multidimensional analysis of attacks involving malicious or compromised websites, by observing that, while web attacks can be very complex in nature, they generally involve four main actors. These are the attackers, the vulnerable websites hosted on the premises of hosting providers, the web users who end up being victims of attacks, and the security companies who scan the Internet trying to block malicious or compromised websites. In particular, we first analyze web attacks from a hosting provider's point of view, showing that, while simple and free security measures should allow to detect simple signs of compromise on customers' websites, most hosting providers fail to do so. Second, we switch our point of view on the attackers, by studying their modus operandi and their goals in a distributed experiment involving the collection of attacks performed against hundreds of vulnerable web sites. Third, we observe the behavior of victims of web attacks, based on the analysis of their browsing habits. This allows us to understand if it would be feasible to build risk profiles for web users, similarly to what insurance companies do. Finally, we adopt the point of view of security companies and focus on finding an efficient solution to detecting web attacks that spread on compromised websites, and infect thousands of web users every day
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Nelms, Terry Lee. "Improving detection and annotation of malware downloads and infections through deep packet inspection." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54941.

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Malware continues to be one of the primary tools employed by attackers. It is used in attacks ranging from click fraud to nation state espionage. Malware infects hosts over the network through drive-by downloads and social engineering. These infected hosts communicate with remote command and control (C&C) servers to perform tasks and exfiltrate data. Malware's reliance on the network provides an opportunity for the detection and annotation of malicious communication. This thesis presents four main contributions. First, we design and implement a novel incident investigation system, named WebWitness. It automatically traces back and labels the sequence of events (e.g., visited web pages) preceding malware downloads to highlight how users reach attack pages on the web; providing a better understanding of current attack trends and aiding in the development of more effective defenses. Second, we conduct the first systematic study of modern web based social engineering malware download attacks. From this study we develop a categorization system for classifying social engineering downloads and use it to measure attack properties. From these measurements we show that it is possible to detect the majority of social engineering downloads using features from the download path. Third, we design and implement ExecScent, a novel system for mining new malware C&C domains from live networks. ExecScent automatically learns C&C traffic models that can adapt to the deployment network's traffic. This adaptive approach allows us to greatly reduce the false positives while maintaining a high number of true positives. Lastly, we develop a new packet scheduling algorithm for deep packet inspection that maximizes throughput by optimizing for cache affinity. By scheduling for cache affinity, we are able to deploy our systems on multi-gigabit networks.
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Barwinski, Mark Andrei. "Taxonomy of spyware and empirical study of network drive-by-downloads." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Sep%5FBarwinski.pdf.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, September 2005.
Thesis Advisor(s): Cynthia E. Irvine, Tim E. Levin. Includes bibliographical references (p. 115-120). Also available online.
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Canali, Davide. "Plusieurs axes d'analyse de sites web compromis et malicieux." Electronic Thesis or Diss., Paris, ENST, 2014. http://www.theses.fr/2014ENST0009.

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L'incroyable développement du World Wide Web a permis la création de nouveaux métiers, services, ainsi que de nouveaux moyens de partage de connaissance. Le web attire aussi des malfaiteurs, qui le considèrent comme un moyen pour gagner de l'argent en exploitant les services et la propriété d'autrui. Cette thèse propose une étude des sites web compromis et malicieux sous plusieurs axes d'analyse. Même si les attaques web peuvent être de nature très compliquées, on peut quasiment toujours identifier quatre acteurs principaux dans chaque cas. Ceux sont les attaquants, les sites vulnérables hébergés par des fournisseurs d'hébergement, les utilisateurs (souvent victimes des attaques), et les sociétés de sécurité qui parcourent Internet à la recherche de sites web compromis à être bloqués. Dans cette thèse, nous analysons premièrement les attaques web du point de vue des hébergeurs, en montrant que, même si des outils gratuits permettent de détecter des signes simples de compromission, la majorité des hébergeurs échouent dans cette épreuve. Nous passons en suite à l'analyse des attaquants et des leurs motivations, en étudiant les attaques web collectés par des centaines de sites web vulnérables. Ensuite, nous étudions le comportement de milliers de victimes d'attaques web, en analysant leurs habitudes pendant la navigation, pour estimer s'il est possible de créer des "profils de risque", de façon similaire à ce que les compagnies d'assurance font aujourd'hui. Enfin, nous adoptons le point de vue des sociétés de sécurité, en proposant une solution efficace pour la détection d'attaques web convoyées par sites web compromis
The incredible growth of the World Wide Web has allowed society to create new jobs, marketplaces, as well as new ways of sharing information and money. Unfortunately, however, the web also attracts miscreants who see it as a means of making money by abusing services and other people's property. In this dissertation, we perform a multidimensional analysis of attacks involving malicious or compromised websites, by observing that, while web attacks can be very complex in nature, they generally involve four main actors. These are the attackers, the vulnerable websites hosted on the premises of hosting providers, the web users who end up being victims of attacks, and the security companies who scan the Internet trying to block malicious or compromised websites. In particular, we first analyze web attacks from a hosting provider's point of view, showing that, while simple and free security measures should allow to detect simple signs of compromise on customers' websites, most hosting providers fail to do so. Second, we switch our point of view on the attackers, by studying their modus operandi and their goals in a distributed experiment involving the collection of attacks performed against hundreds of vulnerable web sites. Third, we observe the behavior of victims of web attacks, based on the analysis of their browsing habits. This allows us to understand if it would be feasible to build risk profiles for web users, similarly to what insurance companies do. Finally, we adopt the point of view of security companies and focus on finding an efficient solution to detecting web attacks that spread on compromised websites, and infect thousands of web users every day
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Šulák, Ladislav. "Detekce škodlivých webových stránek pomocí strojového učení." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385990.

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Táto práca sa zaoberá problematikou škodlivého kódu na webe so zameraním na analýzu a detekciu škodlivého JavaScriptu umiestneného na strane klienta s využitím strojového učenia. Navrhnutý prístup využíva známe i nové pozorovania s ohľadom na rozdiely medzi škodlivými a legitímnymi vzorkami. Tento prístup má potenciál detekovať nové exploity i zero-day útoky. Systém pre takúto detekciu bol implementovaný a využíva modely strojového učenia. Výkon modelov bol evaluovaný pomocou F1-skóre na základe niekoľkých experimentov. Použitie rozhodovacích stromov sa podľa experimentov ukázalo ako najefektívnejšia možnosť. Najefektívnejším modelom sa ukázal byť Adaboost klasifikátor s dosiahnutým F1-skóre až 99.16 %. Tento model pracoval s 200 inštanciami randomizovaného rozhodovacieho stromu založeného na algoritme Extra-Trees. Viacvrstvový perceptrón bol druhým najlepším modelom s dosiahnutým F1-skóre 97.94 %.
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Huang, Jhe-Jhun, and 黃哲諄. "Detecting Drive-by Download Based on Reputation System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/76445739504789070296.

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碩士
國立中山大學
資訊管理學系研究所
100
Drive-by download is a sort of network attack which uses different techniques to plant malicious codes in their computers. It makes the traditional intrusion detection systems and firewalls nonfunctional in the reason that those devices could not detect web-based threats. The Crawler-based approach has been proposed by many studies to discover drive-by download sites. However, the Crawler-based approach could not simulate the real user behavior of web browsing when drive-by download attack happens. Therefore, this study proposes a new approach to detect drive-by download by sniffing HTTP flow. This study uses reputation system to improve the efficiency of client honeypots, and adjusts client honeypots to process the raw data of HTTP flow. In the experiment conducted in real network environment, this study show the performance of a single client honeypot could reach average 560,000 HTTP success access log per day. Even in the peak traffic, this mechanism reduced the process time to 22 hours, and detected drive-by download sites that users were actually browsing. Reputation system in this study is applicable to varieties of domain names because it does not refer to online WHOIS database. It established classification model on machine learning in 12 features. The correct classification rate of the reputation system applied in this study is 90.9%. Compared with other Reputation System studies, this study not only extract features from DNS A-Type but also extract features from DNS NS-Type. The experiment results show the Error Rate of the new features from DNS NS-Type is only 19.03%.
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Tso, Chang-kuo, and 左昌國. "A Browser-side Solution to Drive-by-Download-Based Malicious Web Pages." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/39600613133264153315.

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碩士
國立中央大學
資訊工程研究所
97
As Internet plays an important role for more people in their life, more malicious attackers have changed the targets from web servers of enterprises or organizations to personal computer users by infecting computers with malware or adware for financial gains. In order to compromise the computers of end users which usually don’t provide popular services for traditional infection routine, web-based attack has become an effective method to infect personal computers. In recently years, a notorious web-based attack mechanism, called “drive-by downloads”, makes numbers of hosts infected by malware. Attackers inject malicious contents into webpage stored in vulnerable web server via common attacking techniques like SQL injection. Victims then visit these webpage without alertness because these malicious contents are invisible to them except that they check the source code carefully. When vulnerable browsers read these malicious contests, they secretly download and automatically install harmful binaries in background. This paper introduces a browser-side solution to prevent web browsers from executing binaries downloaded by drive-by downloads. We do not have to analyze the source code of webpage but focus on blocking browsers from executing the binary which has the “secretly download” characteristic. This solution currently works on Internet Explorer 7.0 on Microsoft Windows with low overhead and low false rate.
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Chiang, Ming-Chung, and 江明駿. "A Hierarchical Classifier on Web Proxies for Detecting Drive-by Download Attacks." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/01860436264285021593.

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Book chapters on the topic "Drive-by download"

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Boggs, Nathaniel, Senyao Du, and Salvatore J. Stolfo. "Measuring Drive-by Download Defense in Depth." In Research in Attacks, Intrusions and Defenses, 172–91. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11379-1_9.

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Joshi, Gireesh, R. Padmavathy, Anil Pinapati, and Mani Bhushan Kumar. "BrowserGuard2: A Solution for Drive-by-Download Attacks." In Lecture Notes in Electrical Engineering, 739–50. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8234-4_59.

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Egele, Manuel, Engin Kirda, and Christopher Kruegel. "Mitigating Drive-By Download Attacks: Challenges and Open Problems." In iNetSec 2009 – Open Research Problems in Network Security, 52–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05437-2_5.

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Ghafir, Ibrahim, and Vaclav Prenosil. "Malicious File Hash Detection and Drive-by Download Attacks." In Advances in Intelligent Systems and Computing, 661–69. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2517-1_63.

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Lu, Long, Vinod Yegneswaran, Phillip Porras, and Wenke Lee. "BLADE: Slashing the Invisible Channel of Drive-by Download Malware." In Lecture Notes in Computer Science, 350–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04342-0_20.

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Endicott-Popovsky, Barbara, Julia Narvaez, Christian Seifert, Deborah A. Frincke, Lori Ross O’Neil, and Chiraag Aval. "Use of Deception to Improve Client Honeypot Detection of Drive-by-Download Attacks." In Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience, 138–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02812-0_17.

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Vyawahare, Madhura, and Madhumita Chatterjee. "Survey on Detection and Prediction Techniques of Drive-by Download Attack in OSN." In Algorithms for Intelligent Systems, 453–63. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_42.

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Nappa, Antonio, M. Zubair Rafique, and Juan Caballero. "Driving in the Cloud: An Analysis of Drive-by Download Operations and Abuse Reporting." In Detection of Intrusions and Malware, and Vulnerability Assessment, 1–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39235-1_1.

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Poornachandran, Prabaharan, S. Praveen, Aravind Ashok, Manu R. Krishnan, and K. P. Soman. "Drive-by-Download Malware Detection in Hosts by Analyzing System Resource Utilization Using One Class Support Vector Machines." In Advances in Intelligent Systems and Computing, 129–37. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3156-4_13.

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Ibrahim, Saeed, Nawwaf Al Herami, Ebrahim Al Naqbi, and Monther Aldwairi. "Detection and Analysis of Drive-by Downloads and Malicious Websites." In Communications in Computer and Information Science, 72–86. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4825-3_6.

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Conference papers on the topic "Drive-by download"

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Kikuchi, Hiroaki, Hiroaki Matsumoto, and Hiroshi Ishii. "Automated Detection of Drive-By Download Attack." In 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS). IEEE, 2015. http://dx.doi.org/10.1109/imis.2015.71.

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Singhal, Mohit, and David Levine. "Analysis and Categorization of Drive-by Download Malware." In 2019 4th International Conference on Computing, Communications and Security (ICCCS). IEEE, 2019. http://dx.doi.org/10.1109/cccs.2019.8888147.

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Tyagi, Akshay, Laxmi Ahuja, Sunil Kumar Khatri, and Subhranil Som. "Prevention of Drive by Download Attack (URL Malware Detector)." In 2019 Third International Conference on Inventive Systems and Control (ICISC). IEEE, 2019. http://dx.doi.org/10.1109/icisc44355.2019.9036341.

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Takada, Tetsuji, and Katsuhiro Amako. "A Visual Approach to Detecting Drive-by Download Attacks." In VINCI '15: The 8th International Symposium on Visual Information Communication and Interaction. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2801040.2801070.

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Song, Chengyu, Jianwei Zhuge, Xinhui Han, and Zhiyuan Ye. "Preventing drive-by download via inter-module communication monitoring." In the 5th ACM Symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1755688.1755705.

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Takata, Yuta, Mitsuaki Akiyama, Takeshi Yagi, Takeo Hariu, and Shigeki Goto. "MineSpider: Extracting URLs from Environment-Dependent Drive-by Download Attacks." In 2015 IEEE 39th Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2015. http://dx.doi.org/10.1109/compsac.2015.76.

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Cherukuri, Manoj, Srinivas Mukkamala, and Dongwan Shin. "Similarity Analysis of Shellcodes in Drive-by Download Attack Kits." In 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing. IEEE, 2012. http://dx.doi.org/10.4108/icst.collaboratecom.2012.250507.

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Cherukuri, Manoj, Srinivas Mukkamala, and Dongwan Shin. "Detection of Plugin Misuse Drive-By Download Attacks using Kernel Machines." In 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing. ICST, 2014. http://dx.doi.org/10.4108/icst.collaboratecom.2014.257749.

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Priya M, Sandhya L., and Ciza Thomas. "A static approach to detect drive-by-download attacks on webpages." In 2013 International Conference on Control Communication and Computing (ICCC). IEEE, 2013. http://dx.doi.org/10.1109/iccc.2013.6731668.

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Adachi, Takashi, and Kazumasa Omote. "An Approach to Predict Drive-by-Download Attacks by Vulnerability Evaluation and Opcode." In 2015 10th Asia Joint Conference on Information Security (AsiaJCIS). IEEE, 2015. http://dx.doi.org/10.1109/asiajcis.2015.17.

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Reports on the topic "Drive-by download"

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Unknown, Author. L51658 Subsea Pig Recovery Concepts. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), October 1991. http://dx.doi.org/10.55274/r0010603.

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Abstract:
The technology and procedures for the inspection of pipelines from offshore production and process facilities to shore or other offshore installations has developed rapidly in recent years. This rapid development can be attributed to several factors including pipeline operators protecting their investment in their lines, heightened environmental protection concerns, and increasing regulatory requirements. A limited number of the offshore pipeline systems in the U.S. currently include the ability to inspect the trunkline using either an ultrasonic or magnetic particle inspection pig. This pig is moved through the pipeline with the product flow from the trunkline's point of origin offshore to an onshore or nearshore facility. As the pig moves through the line, it takes and stores measurement readings which can be downloaded and interpreted after the pig is removed from the line. While inspection pig technology can provide the pipeline operator with valuable information, several factors can prohibit development of a successful trunkline inspection pigging program. Because inspection pigs have onboard data measurement and storage facilities, they are significantly longer and heavier than foam or scraper pigs, and must be articulated to negotiate pipe bends. The minimum negotiable bend radius varies among inspection pig vendors, but is typically between 3 - 5 pipe diameters. The drive cups on an articulated pig are located in the front to prevent buckling. The cups must maintain a close fit with the inside pipe diameter to propel the pig through the pipeline and can stall or jam in a multi-diameter pipeline. The scope of the project includes the development of several basic concepts which, in principle, meet the project objectives. These concepts have been evaluated from several technical viewpoints and two primary concepts selected for further development. A preliminary design effort, carried out on both primary concepts, includes: Structure stress calculations; Detailed layouts of major assemblies and components Step by step installation, operation, and maintenance procedures; Detailed capital and operating cost development. A basic scenario of a 12"� ANSI 900# rated system in 400 ft. water depth is used for both concepts. A sensitivity analysis for the effects of greater and lesser water depths (800 ft. and 100 ft.) and a change in pipe size to 8"� nominal is also included.
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