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Journal articles on the topic 'Masquerade detection'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Tapiador, Juan E., and John A. Clark. "Masquerade mimicry attack detection: A randomised approach." Computers & Security 30, no. 5 (2011): 297–310. http://dx.doi.org/10.1016/j.cose.2011.05.004.

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12

Huang, Lin, and Mark Stamp. "Masquerade detection using profile hidden Markov models." Computers & Security 30, no. 8 (2011): 732–47. http://dx.doi.org/10.1016/j.cose.2011.08.003.

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13

Dash, Subrat Kumar, Krupa Sagar Reddy, and Arun K. Pujari. "Adaptive Naive Bayes method for masquerade detection." Security and Communication Networks 4, no. 4 (2011): 410–17. http://dx.doi.org/10.1002/sec.168.

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14

Rodríguez, Jorge, Leonardo Cañete, Raúl Monroy, and Miguel Angel Medina-Pérez. "Experimenting with masquerade detection via user task usage." International Journal on Interactive Design and Manufacturing (IJIDeM) 11, no. 4 (2016): 771–84. http://dx.doi.org/10.1007/s12008-016-0360-1.

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15

., Chitra S. Nair. "MASQUERADE DETECTION USING IMPROVED SEMI GLOBAL POSITIONING ALGORITHM." International Journal of Research in Engineering and Technology 05, no. 16 (2016): 134–38. http://dx.doi.org/10.15623/ijret.2016.0516029.

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16

Sen, S. "Sequence-based masquerade detection for different user groups." Security and Communication Networks 8, no. 7 (2014): 1265–78. http://dx.doi.org/10.1002/sec.1080.

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17

Sivanandhan, Anusha, Bibitha Baby, and Dhanya G S. "Masquerade Attack Identification Via DDSGA-Based Anomaly Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44275.

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To take advantage of the user services and privileges, a masquerade attacker poses as a legitimate user. The semi- global alignment algorithm (SGA) is one of the most effective and efficient techniques to detect these attacks but it has not yet reached the accuracy and performance required by large scale, multiuser systems. To improve both the effectiveness and the performances of this algorithm, the Data-Driven Semi - Global Alignment, DDSGA approach was proposed. By implementing unique alignment settings for every user, DDSGA enhances the scoring systems from the perspective of security effe

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18

Hatture, Sanjeeva Kumar M., and Shweta Policepatil. "Masquerade Attack Analysis for Secured Face Biometric System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (2021): 225–32. http://dx.doi.org/10.35940/ijrte.b6309.0710221.

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Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as masquerade

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19

Shweta, Policepatil, and Kumar M. Hatture Sanjeeva. "Masquerade Attack Analysis for Secured Face Biometric System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (2021): 225–32. https://doi.org/10.35940/ijrte.B6309.0710221.

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Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as m

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20

Skelhorn, John, and Graeme D. Ruxton. "Predators are less likely to misclassify masquerading prey when their models are present." Biology Letters 6, no. 5 (2010): 597–99. http://dx.doi.org/10.1098/rsbl.2010.0226.

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Masquerading animals have evolved striking visual resemblances to inanimate objects. These animals gain protection from their predators not simply by avoiding detection, but by causing their predators to misclassify them as the ‘models’ that they appear to resemble. Using domestic chicks as predators and twig-mimicking caterpillars as prey, we demonstrated that masquerading prey were more likely to be misclassified as their models when viewed in isolation from their models than when viewed alongside examples of their model, although they benefitted from masquerade to some extent in both condit

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21

Camina, J. Benito, Raul Monroy, Luis A. Trejo, and Miguel Angel Medina-Perez. "Temporal and Spatial Locality: An Abstraction for Masquerade Detection." IEEE Transactions on Information Forensics and Security 11, no. 9 (2016): 2036–51. http://dx.doi.org/10.1109/tifs.2016.2571679.

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22

Jian, Zhou, Haruhiko Shirai, Isamu Takahashi, Jousuke Kuroiwa, Tomohiro Odaka, and Hisakazu Ogura. "Masquerade detection by boosting decision stumps using UNIX commands." Computers & Security 26, no. 4 (2007): 311–18. http://dx.doi.org/10.1016/j.cose.2006.11.008.

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23

Elmasry, Wisam, Akhan Akbulut, and Abdul Halim Zaim. "Comparative evaluation of different classification techniques for masquerade attack detection." International Journal of Information and Computer Security 13, no. 2 (2020): 187. http://dx.doi.org/10.1504/ijics.2020.108848.

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24

Zaim, Abdul Halim, Akhan Akbulut, and Wisam Elmasry. "Comparative Evaluation of Different Classification Techniques for Masquerade Attack Detection." International Journal of Information and Computer Security 11, no. 4/5 (2019): 1. http://dx.doi.org/10.1504/ijics.2019.10020664.

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25

Kim, Han-Sung, and Sung-Deok Cha. "Empirical evaluation of SVM-based masquerade detection using UNIX commands." Computers & Security 24, no. 2 (2005): 160–68. http://dx.doi.org/10.1016/j.cose.2004.08.007.

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26

Lerche, Ian. "Direct Detection of Hydrocarbons: Some Preliminary Thoughts and Questions." Energy Exploration & Exploitation 5, no. 4 (1987): 265–85. http://dx.doi.org/10.1177/014459878700500402.

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After briefly reviewing seismic signatures associated with the direct detection of hydrocarbons, some possible non-hydrocarbon sources of seismic signatures which may masquerade as hydrocarbon patterns are considered. Four broad avenues of research are suggested which may allow greater assurance in identifying and interpreting seismic signatures associated with hydrocarbon accumulations than is currently enjoyed. It is also suggested that any available information extending upon any part of this paper will increase the base of knowledge of seismic direct detection indicators.

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27

TIAN, Xin-Guang, Mi-Yi DUAN, and Xue-Qi CHENG. "Masquerade Detection Based on Shell Commands and Multiple Behavior Pattern Mining." Chinese Journal of Computers 33, no. 4 (2010): 697–705. http://dx.doi.org/10.3724/sp.j.1016.2010.00697.

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28

Barseghyan, Ghazaros, Yuyu Yuan, and Manawa Anakpa. "Model for Detection of Masquerade Attacks Based on Variable-Length Sequences." IEEE Access 8 (2020): 210140–57. http://dx.doi.org/10.1109/access.2020.3039166.

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29

Liu, Jia, Miyi Duan, Wenfa Li, and Xinguang Tian. "HMMs based masquerade detection for network security on with parallel computing." Computer Communications 156 (April 2020): 168–73. http://dx.doi.org/10.1016/j.comcom.2020.03.048.

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30

Ayeni, Olaniyi, O. Owolafe, and O. P. Ogunjobi. "A Security System for Detecting Denial of Service (DDoS) and Masquerade Attacks on Social Networks." Journal of Information Security and Cybercrimes Research 5, no. 1 (2022): 80–86. http://dx.doi.org/10.26735/gmey8791.

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This study on a security system for detecting denial of service (DDoS) and masquerade attacks on social networks specifically describes how a Convolutional Neural Network (CNN) algorithm was employed. The dataset used for this research is the CICIDS2017 dataset, which contains benign data (no attack present) and the most up-to-date, frequent attacks which resemble true, real-world data. The feature extraction method used was recursive feature elimination (RFE), which reduced 77 columns of the dataset to 10 columns. This research was motivated by the limitation of Alguliyev and Abdullayeva 2019

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31

Sen, Sevil. "Using instance-weighted naive Bayes for adapting concept drift in masquerade detection." International Journal of Information Security 13, no. 6 (2014): 583–90. http://dx.doi.org/10.1007/s10207-014-0238-9.

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32

Camiña, José Benito, Miguel Angel Medina-Pérez, Raúl Monroy, Octavio Loyola-González, Luis Angel Pereyra Villanueva, and Luis Carlos González Gurrola. "Bagging-RandomMiner: a one-class classifier for file access-based masquerade detection." Machine Vision and Applications 30, no. 5 (2018): 959–74. http://dx.doi.org/10.1007/s00138-018-0957-4.

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33

Dr, Ruksar Fatima* Mohd Shafiuddin. "DETERMINING FALSE ATTACKS USING DDSGA TECHNIQUE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 103–6. https://doi.org/10.5281/zenodo.805359.

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For the sake of identifying masquerade attackers in a computer, various alignment algorithms has been proposed. The semi-global alignment algorithm (SGA) is the most effective and also efficient technique to detect these type of attacks till now, but it has not reach the level of accuracy and effectiveness required by large scale and multiuser systems. To support these shortcomings of SGA and to increase both the effectiveness and the performances of this algorithm, the Data-Driven Semi-Global Alignment, DDSGA approach has been proposed. DDSGA has much more improvements and increases the scori

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34

Pakray, Amir, Nolan Hayden, Farnoosh Sokhandon, and Johnathan Olsen. "Detection and Intervention of Clinically Masquerading Inferior Mesenteric Artery AVMs." Case Reports in Radiology 2021 (May 28, 2021): 1–6. http://dx.doi.org/10.1155/2021/8854806.

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We demonstrate a rare case of inferior mesenteric artery arteriovenous malformations leading to ischemic colitis in a 76-year-old female. Our patient presented with three months of nausea, vomiting, and diarrhea. Colonoscopy displayed diffuse mucosal vascular congestion while CTA and MRA displayed AVMs in the region of the IMA; however, cohesive clinical agreement on AVM from multiple specialties was difficult given its rare occurrence and nonspecific clinical, histopathologic, and directly visualized findings. The three noted dominant AVMs were eventually selected with coil and liquid emboliz

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35

Skudlarek, Stefan Jan, and Hirosuke Yamamoto. "Unsupervised anomaly detection within non-numerical sequence data by average index difference, with application to masquerade detection." Applied Stochastic Models in Business and Industry 30, no. 5 (2014): 632–56. http://dx.doi.org/10.1002/asmb.2057.

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36

Sickbert-Bennett, Emily, Lisa Stancill, Lauren DiBiase, Kevin Alby, and David Jay Weber. "The Great Masquerade: Identification of Clinically Relevant Clostridioides difficile Infections." Infection Control & Hospital Epidemiology 41, S1 (2020): s405. http://dx.doi.org/10.1017/ice.2020.1055.

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Background: Despite clear guidance for appropriate testing of symptomatic patients for Clostridioides difficile testing (McDonald et al), the ideal testing methodology remains unresolved. Laboratories currently use different algorithms that incorporate enzyme immunoassay (EIA) testing for toxin, glutamate dehydrogenase (GDH) antigen, and polymerase chain reaction (PCR) testing in combination or as a single test. At UNC Hospitals, a large academic hospital with nearly 1,000 beds in the ninth most populous state in the United States, patients are currently tested by an EIA test for toxin and GDH

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37

Gaikwad, Priyanka K. "A Survey on Object-based Masquerade Detection System using Temporal and Spatial Locality Features." International Journal of Engineering Research 6, no. 4 (2017): 220. http://dx.doi.org/10.5958/2319-6890.2017.00006.x.

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38

Geng, Dai, Thmohiro Odaka, Jousuke Kuroiwa, and Hisakazu Ogura. "An N-Gram and STF-IDF model for masquerade detection in a UNIX environment." Journal in Computer Virology 7, no. 2 (2010): 133–42. http://dx.doi.org/10.1007/s11416-010-0143-3.

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39

Hashmi, Mohammad Adil ullah. "Study of Machine Learning Algorithm based on Face Anti-Spoofing Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28013.

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Face spoofing detection is one of the most well-studied problems in computer vision. Face recognition has become a widely adopted technique in biometric authentication systems. In face recognition based authentication techniques, the system first recognized the person to verify the legitimacy of the user before granting access to the system resources. The system must be able to determine the liveness of the person in front of the camera, for example, by recognizing the face and denying the types of face presentation attacks related to photographs, videos and the 3D mask of the targeted person.

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40

Li, Biao. "Research on Computer Network Security and Prevention Strategy." Applied Mechanics and Materials 608-609 (October 2014): 526–30. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.526.

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With the development and popularization of the computer network, the security problem has increasingly become the focus of common concern. The computer network safety problems mainly include: protocol design issues, awareness, management system and technical operation. Hackers on the computer network attack techniques: Deceptive attack, masquerade attacks, vulnerability attack, covert attack and technical attack. Accordingly, we should take the corresponding safety countermeasures: strengthen network security education, using the network protection technology, detection technology and anti-vir

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41

Salem, Malek Ben, and Salvatore J. Stolfo. "A comparison of one-class bag-of-words user behavior modeling techniques for masquerade detection." Security and Communication Networks 5, no. 8 (2011): 863–72. http://dx.doi.org/10.1002/sec.311.

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42

Galloway, James A. M., Samuel D. Green, Martin Stevens, and Laura A. Kelley. "Finding a signal hidden among noise: how can predators overcome camouflage strategies?" Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1802 (2020): 20190478. http://dx.doi.org/10.1098/rstb.2019.0478.

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Substantial progress has been made in the past 15 years regarding how prey use a variety of visual camouflage types to exploit both predator visual processing and cognition, including background matching, disruptive coloration, countershading and masquerade. By contrast, much less attention has been paid to how predators might overcome these defences. Such strategies include the evolution of more acute senses, the co-opting of other senses not targeted by camouflage, changes in cognition such as forming search images, and using behaviours that change the relationship between the cryptic indivi

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43

Xiao, Maoran, Qi Zhou, Zhen Zhang, and Junjie Yin. "Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security." HighTech and Innovation Journal 5, no. 3 (2024): 814–27. http://dx.doi.org/10.28991/hij-2024-05-03-018.

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Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, In

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44

Xiao, Xi, Xinguang Tian, Qibin Zhai, and Shutao Xia. "Corrigendum to “A variable-length model for masquerade detection” [J. Syst. Softw. 85 (2012) 2470–2478]." Journal of Systems and Software 86, no. 3 (2013): 868. http://dx.doi.org/10.1016/j.jss.2012.11.008.

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45

Camiña, J. Benito, Carlos Hernández-Gracidas, Raúl Monroy, and Luis Trejo. "The Windows-Users and -Intruder simulations Logs dataset (WUIL): An experimental framework for masquerade detection mechanisms." Expert Systems with Applications 41, no. 3 (2014): 919–30. http://dx.doi.org/10.1016/j.eswa.2013.08.022.

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46

Jadoaa, Sawsan H., Rasha H. Ali, Wisal Hashim Abdulsalam, and Emad M. Alsaedi. "The Impact of Feature Importance on Spoofing Attack Detection in IoT Environment." Mesopotamian Journal of CyberSecurity 5, no. 1 (2025): 240–55. https://doi.org/10.58496/mjcs/2025/016.

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The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance de

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47

Cooper, William E., and Wade C. Sherbrooke. "Choosing between a rock and a hard place: Camouflage in the round-tailed horned lizard Phrynosoma modestum." Current Zoology 58, no. 4 (2012): 541–48. http://dx.doi.org/10.1093/czoolo/58.4.543.

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Abstract The round-tailed horned lizard Phrynosoma modestum is cryptically colored and resembles a small stone when it draws legs close to its body and elevates its back. We investigated effectiveness of camouflage in P. modestum and its dependence on stones by placing a lizard in one of two microhabitats (uniform sand or sand with surface rocks approximately the same size as lizards). An observer who knew which microhabitat contained the lizard was asked to locate the lizard visually. Latency to detection was longer and probability of no detection within 60 s was higher for lizards on rock ba

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Costello, Leah M., Nicholas E. Scott-Samuel, Karin Kjernsmo, and Innes C. Cuthill. "False holes as camouflage." Proceedings of the Royal Society B: Biological Sciences 287, no. 1922 (2020): 20200126. http://dx.doi.org/10.1098/rspb.2020.0126.

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Long noted by naturalists, leaf mimicry provides some of the most impressive examples of camouflage through masquerade. Many species of leaf-mimicking Lepidoptera also sport wing markings that closely resemble irregularly shaped holes caused by decay or insect damage. Despite proposals that such markings can either enhance resemblance to damaged leaves or act to disrupt surface appearance through false depth cues, to our knowledge, no attempt has been made to establish exactly how these markings function, or even whether they confer a survival benefit to prey. Here, in two field experiments us

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Darusalam, Darusalam, and Helen Ashman. "Profiling and Identifying Individual Users by Their Command Line Usage and Writing Style." Knowledge Engineering and Data Science 1, no. 2 (2018): 55. http://dx.doi.org/10.17977/um018v1i22018p55-63.

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Profiling and identifying individual users is an approach for intrusion detection in a computer system. User profiles are important in many applications since they record highly user-specific information - profiles are basically built to record information about users or for users to share experiences with each other. This research extends previous research on re-authenticating users with their user profiles. This research focuses on the potential to add psychometric user characteristics into the user model so as to be able to detect unauthorized users who may be masquerading as a genuine user

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50

Kalaiselvi, R., and P. Meenakshi Sundaram. "Unified framework for sybil attack detection in mobile ad hoc networks using machine learning approach." Scientific Temper 16, no. 02 (2025): 3774–82. https://doi.org/10.58414/scientifictemper.2025.16.2.09.

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Independent wireless communication is possible in a "mobile ad hoc network" regardless of any predefined administrative or physical framework. The comprehensive enhancement of services for these networks depends on protecting their interactions. The Sybil attack creates numerous counterfeit identities to disrupt the system's remote functionalities. Implementing a security plan necessitates the establishment of a trust model that delineates the confidence relationships among entities. The trust structure in mobile ad hoc network security has been extensively researched. Mobile ad hoc networks a

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