Relevant bibliographies by topics / Masquerade detection

Contents

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

Academic literature on the topic 'Masquerade detection'

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

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Journal articles on the topic "Masquerade detection"

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Maestre Vidal, Jorge, and Marco Antonio Sotelo Monge. "Obfuscation of Malicious Behaviors for Thwarting Masquerade Detection Systems Based on Locality Features." Sensors 20, no. 7 (2020): 2084. http://dx.doi.org/10.3390/s20072084.

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In recent years, dynamic user verification has become one of the basic pillars for insider threat detection. From these threats, the research presented in this paper focuses on masquerader attacks, a category of insiders characterized by being intentionally conducted by persons outside the organization that somehow were able to impersonate legitimate users. Consequently, it is assumed that masqueraders are unaware of the protected environment within the targeted organization, so it is expected that they move in a more erratic manner than legitimate users along the compromised systems. This fea
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Almanza-Ortega, Nelva Nely, Joaquin Perez-Ortega, Monterrubio Sergio Mauricio Martínez, and Juan A. Recio-Garcia. "An Explainable Clustering-Based Approach for Cyber Situational Awareness on Masquerade Attacks Detection." Journal of Artificial Intelligence and Computing Applications 2, no. 2 (2024): 48–59. https://doi.org/10.5281/zenodo.15098302.

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Masquerade attacks pose a significant challenge in cybersecurity, as intruders mimic legitimate user behavior to evade detection. In dynamic, data-intensive environments, traditional intrusion detection systems often struggle to provide both timely and interpretable results, limiting their usefulness for effective Cyber Situational Awareness (CSA). This article presents a clustering-based approach for detecting masquerade attacks using OK-Means—a variant of K-Means optimized for faster convergence—combined with a nearest neighbor classifier and noise reduction techniques. The propo
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Elmasry, Wisam, Akhan Akbulut, and Abdul Halim Zaim. "Deep Learning Approaches for Predictive Masquerade Detection." Security and Communication Networks 2018 (August 1, 2018): 1–24. http://dx.doi.org/10.1155/2018/9327215.

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In computer security, masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial factor for computer security. Although considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low false alarm rate is still a big challenge. In this paper, we present a comprehensive empirical study in the area of anomaly-based masquerade detection using three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LS
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Coull, Scott E., and Boleslaw K. Szymanski. "Sequence alignment for masquerade detection." Computational Statistics & Data Analysis 52, no. 8 (2008): 4116–31. http://dx.doi.org/10.1016/j.csda.2008.01.022.

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Maxion, R. A., and T. N. Townsend. "Masquerade Detection Augmented With Error Analysis." IEEE Transactions on Reliability 53, no. 1 (2004): 124–47. http://dx.doi.org/10.1109/tr.2004.824828.

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Maestre Vidal, Jorge, Ana Lucila Sandoval Orozco, and Luis Javier García Villalba. "Online masquerade detection resistant to mimicry." Expert Systems with Applications 61 (November 2016): 162–80. http://dx.doi.org/10.1016/j.eswa.2016.05.036.

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Min, Yang, Wang Li-na, Zhang Huan-guo, and Chen Wei. "Masquerade detection using support vector machine." Wuhan University Journal of Natural Sciences 10, no. 1 (2005): 103–6. http://dx.doi.org/10.1007/bf02828627.

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Almanza-Ortega, Nelva Nely, Joaquin Perez-Ortega, Monterrubio Sergio Mauricio Martínez, and Juan A. Recio-Garcia. "Clustering-Based Cyber Situational Awareness: A Practical Approach for Masquerade Attack Detection." Journal of Artificial Intelligence and Computing Applications 2, no. 1 (2024): 35–39. https://doi.org/10.5281/zenodo.14933955.

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Cyber Situational Awareness (CSA) is crucial for detecting and mitigating security threats in evolving digital environments. Traditional intrusion detection systems face challenges related to computational efficiency, scalability, and interpretability, particularly in the detection of masquerade attacks, where attackers mimic legitimate user behavior. This exploratory study conducts a preliminary investigation into a clustering-based approach that integrates OK-Means, an optimized variant of K-Means, with k-Nearest Neighbors (k-NN) to improve intrusion detection. The proposed approach is evalu
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Xiao, Xi, Xinguang Tian, Qibin Zhai, and Shutao Xia. "A variable-length model for masquerade detection." Journal of Systems and Software 85, no. 11 (2012): 2470–78. http://dx.doi.org/10.1016/j.jss.2012.05.049.

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Agrawal, Arshi, and Mark Stamp. "Masquerade detection on GUI-based Windows systems." International Journal of Security and Networks 10, no. 1 (2015): 32. http://dx.doi.org/10.1504/ijsn.2015.068409.

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Dissertations / Theses on the topic "Masquerade detection"

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Panopoulos, Vasileios. "Near Real-time Detection of Masquerade attacks in Web applications : catching imposters using their browsing behavor." Thesis, KTH, Kommunikationsnät, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183777.

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This Thesis details the research on Machine Learning techniques that are central in performing Anomaly and Masquerade attack detection. The main focus is put on Web Applications because of their immense popularity and ubiquity. This popularity has led to an increase in attacks, making them the most targeted entry point to violate a system. Specifically, a group of attacks that range from identity theft using social engineering to cross site scripting attacks, aim at exploiting and masquerading users. Masquerading attacks are even harder to detect due to their resemblance with normal sessions, t
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Wiklund, Anton. "Masquerader Detection via 2fa Honeytokens." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-106964.

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Detection of insider threats is vital within cybersecurity. Techniques for detection include honeytokens, which most often are resources that, through deception, seek to expose intruders. One kind of insider that is detectable via honeytokens is the masquerader. This project proposes implementing a masquerader detection technique where honeytokens are placed within users’ filesystems in such a way that they also provide Two Factor Authentication(2fa) functionality. If a user’s second factor – the honeytoken –is not accessed within a specified timeframe after login, this indicates a potential i
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Kambhampaty, Krishna Kanth. "Detecting Insider and Masquerade Attacks by Identifying Malicious User Behavior and Evaluating Trust in Cloud Computing and IoT Devices." Diss., North Dakota State University, 2019. https://hdl.handle.net/10365/29914.

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There are a variety of communication mediums or devices for interaction. Users hop from one medium to another frequently. Though the increase in the number of devices brings convenience, it also raises security concerns. Provision of platform to users is as much important as its security. In this dissertation we propose a security approach that captures user behavior for identifying malicious activities. System users exhibit certain behavioral patterns while utilizing the resources. User behaviors such as device location, accessing certain files in a server, using a designated or specific use
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Ben, Salem Malek. "Towards Effective Masquerade Attack Detection." Thesis, 2012. https://doi.org/10.7916/D8J96DBT.

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Data theft has been the main goal of the cybercrime community for many years, and more and more so as the cybercrime community gets more motivated by financial gain establishing a thriving underground economy. Masquerade attacks are a common security problem that is a consequence of identity theft and that is generally motivated by data theft. Such attacks are characterized by a system user illegitimately posing as another legitimate user. Prevention-focused solutions such as access control solutions and Data Loss Prevention tools have failed in preventing these attacks, making detection not a
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Gunasekaran, Karthic. "Probabilistic Vs. clustering analysis of modified UNIX command lines for masquerade detection." 2005. http://digital.library.okstate.edu/etd/umi-okstate-1374.pdf.

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Books on the topic "Masquerade detection"

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Salem, Malek Ben. Towards Effective Masquerade Attack Detection. [publisher not identified], 2012.

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Rice, Molly. Silent masquerade. Silhouette, 1995.

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Satterthwait, Walter. Masquerade. St. Martin's Press, 1998.

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Satterthwait, Walter. Masquerade. St. Martin's Paperbacks, 1999.

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Copyright Paperback Collection (Library of Congress), ed. The marriage masquerade. St. Martin's Paperbacks, 2002.

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Bugeja, Michael J. Bonfire Masquerade: Nancy Drew: Girl Detective and the Hardy Boys: Undercover Brothers Super Mystery #5. Aladdin, 2011.

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Allan, Poe Edgar. Mystery Tales. Giunti, 2003.

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Allan, Poe Edgar. Tales of Mystery and Horror: Vol. III. Ulverscroft, 2012.

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Allan, Poe Edgar. Edgar Allan Poe. Sterling Pub. Co., 2006.

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Allan, Poe Edgar. Maxon's Poe: Seven stories and poems. Cottage Classics, 1997.

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Book chapters on the topic "Masquerade detection"

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Dash, Subrat Kumar, Krupa Sagar Reddy, and Arun K. Pujari. "Episode Based Masquerade Detection." In Information Systems Security. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11593980_19.

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Latendresse, Mario. "Masquerade Detection via Customized Grammars." In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11506881_9.

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Kadala Manikoth, Swathi Nambiar, Fabio Di Troia, and Mark Stamp. "Masquerade Detection on Mobile Devices." In Computer Communications and Networks. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92624-7_13.

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Yung, Kwong H. "Using Feedback to Improve Masquerade Detection." In Applied Cryptography and Network Security. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45203-4_4.

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Salem, Malek Ben, and Salvatore J. Stolfo. "Modeling User Search Behavior for Masquerade Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23644-0_10.

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Saljooghinejad, Hamed, and Wilson Naik Bhukya. "Layered Security Architecture for Masquerade Attack Detection." In Data and Applications Security and Privacy XXVI. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31540-4_19.

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Yu, Feng, and Min Wang. "Applying Frequent Episode Algorithm to Masquerade Detection." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85930-7_63.

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Ben Salem, Malek, and Salvatore J. Stolfo. "Decoy Document Deployment for Effective Masquerade Attack Detection." In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22424-9_3.

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Chandrasekara, Pranieth, Hasini Abeywardana, Sammani Rajapaksha, Sanjeevan Parameshwaran, and Kavinga Yapa Abeywardana. "Behavior and Biometrics Based Masquerade Detection Mobile Application." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52243-8_32.

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Saljooghinejad, Hamed, and Wilson Naik Rathore. "Multi Application User Profiling for Masquerade Attack Detection." In Advances in Computing and Communications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22714-1_70.

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Conference papers on the topic "Masquerade detection"

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Berdychowski, Matt, and Sergio A. Salinas Monroy. "Automated Detection of Masquerade Attacks with AI and Decoy Documents." In 2024 Cyber Awareness and Research Symposium (CARS). IEEE, 2024. https://doi.org/10.1109/cars61786.2024.10778670.

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Imsand, Eric S., and John A. Hamilton Jr. "Masquerade Detection through GUIID." In IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference. IEEE, 2008. http://dx.doi.org/10.1109/glocom.2008.ecp.405.

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Strasburg, Chris, Sandeep Krishnan, Karin Dorman, Samik Basu, and Johnny S. Wong. "Masquerade Detection in Network Environments." In 2010 10th IEEE/IPSJ International Symposium on Applications and the Internet (SAINT). IEEE, 2010. http://dx.doi.org/10.1109/saint.2010.66.

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Yang, Min, Huanguo Zhang, and H. J. Cai. "Masquerade Detection Using String Kernels." In 2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2007. http://dx.doi.org/10.1109/wicom.2007.910.

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Imsand, Eric S., and John A. Hamilton. "GUI Usage Analysis for Masquerade Detection." In 2007 IEEE SMC Information Assurance and Security Workshop. IEEE, 2007. http://dx.doi.org/10.1109/iaw.2007.381943.

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Shemla, A., and V. Bineesh. "An EvABCD approach for masquerade detection." In 2014 2nd International Conference on Current Trends in Engineering and Technology (ICCTET). IEEE, 2014. http://dx.doi.org/10.1109/icctet.2014.6966354.

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Shim, Charlie Y., Jung Yeop Kim, and Rex E. Gantenbein. "Practical User Identification for Masquerade Detection." In Advances in Electrical and Electronics Engineering - IAENG Special Edition of the World Congress on Engineering and Computer Science (WCECS). IEEE, 2008. http://dx.doi.org/10.1109/wcecs.2008.14.

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Raveendran, Reshma, and K. A. Dhanya. "Applicability of clustering techniques on masquerade detection." In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2014. http://dx.doi.org/10.1109/icacci.2014.6968577.

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Ding, Yuxin, Ping Sun, Xiuyue Chen, and Changan Liu. "Masquerade Detection Based on One Class SVM." In 11th Joint Conference on Information Sciences. Atlantis Press, 2008. http://dx.doi.org/10.2991/jcis.2008.121.

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Bhukya, Wilson Naik, G. Suresh Kumar, and Atul Negi. "A Study of Effectiveness in Masquerade Detection." In TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.344199.

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