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Journal articles on the topic 'Suspicious behaviors'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Cheng, Junyi, Xianfeng Zhang, Xiao Chen, Miao Ren, Jie Huang, and Peng Luo. "Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data." ISPRS International Journal of Geo-Information 11, no. 9 (2022): 478. http://dx.doi.org/10.3390/ijgi11090478.

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Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods that are focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors. While criminals intend to avoid abnormal behaviors under surveillance, their suspicious behaviors prior to crimes will be unconsciously reflected in the trajectories. Herein, we characterize several suspicious behaviors from unusual movement patterns, unusual behaviors, and unusual gatherings of peop

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Park, Yeonji, Yoojin Jeong, and Chaebong Sohn. "Suspicious behavior recognition using deep learning." Journal of Advances in Military Studies 4, no. 1 (2021): 43–59. http://dx.doi.org/10.37944/jams.v4i1.78.

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The purpose of this study is to reinforce the defense and security system by recognizing the behaviors of suspicious person both inside and outside the military using deep learning. Surveillance cameras help detect criminals and people who are acting unusual. However, it is inefficient in that the administrator must monitor all the images transmitted from the camera. It incurs a large cost and is vulnerable to human error. Therefore, in this study, we propose a method to find a person who should be watched carefully only with surveillance camera images. For this purpose, the video data of doub

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Wong, Khai Chiuan, and Mohd Ridzuan bin Ahmad. "Reviewing Approaches and Techniques for Detecting Suspicious Human Behavior: A Comprehensive Survey." ELEKTRIKA- Journal of Electrical Engineering 23, no. 2 (2024): 44–52. http://dx.doi.org/10.11113/elektrika.v23n2.538.

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The paper aims to review related works that focus on detecting suspicious human behavior using machine-learning techniques. Suspicious human behavior refers to behaviors that may indicate involvement in or preparation for a crime. Detecting such behaviors before a crime is committed allows law enforcement to take early action and prevent criminal activities. One of the challenges in developing an effective detection system for suspicious human behavior is the absence of a well-defined definition for such behaviors. Different definitions can lead to various methods for designing the detection s

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Cheoi, Kyung Joo. "Temporal Saliency-Based Suspicious Behavior Pattern Detection." Applied Sciences 10, no. 3 (2020): 1020. http://dx.doi.org/10.3390/app10031020.

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The topic of suspicious behavior detection has been one of the most emergent research themes in computer vision, video analysis, and monitoring. Due to the huge number of CCTV (closed-circuit television) systems, it is not easy for people to manually identify CCTV for suspicious motion monitoring. This paper is concerned with an automatic suspicious behavior detection method using a CCTV video stream. Observers generally focus their attention on behaviors that vary in terms of magnitude or gradient of motion and behave differently in rules of motion with other objects. Based on these facts, th

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Wong, Keri Ka-Yee, and Adrian Raine. "Peer Problems and Low Self-esteem Mediate the Suspicious and Non-suspicious Schizotypy–Reactive Aggression Relationship in Children and Adolescents." Journal of Youth and Adolescence 48, no. 11 (2019): 2241–54. http://dx.doi.org/10.1007/s10964-019-01125-9.

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Abstract The relationship between schizophrenia and violence has been well-established. Yet very little prior research exists on the factors that might explain the nature of this relationship and even fewer studies seek to clarify the etiology of aggressive behavior in adolescents with specific features of schizotypal personality that might help improve the specificity of intervention. The current study tested whether one dimension of schizotypy alone (i.e., the ‘suspicious’ feature) or the other 8 dimensions (i.e., the ‘non-suspicious’ features) were particularly associated with aggressive be

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Yan, Guanghua, Qiang Li, Dong Guo, and Xiangyu Meng. "Discovering Suspicious APT Behaviors by Analyzing DNS Activities." Sensors 20, no. 3 (2020): 731. http://dx.doi.org/10.3390/s20030731.

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As sensors become more prevalent in our lives, security issues have become a major concern. In the Advanced Persistent Threat (APT) attack, the sensor has also become an important role as a transmission medium. As a relatively weak link in the network transmission process, sensor networks often become the target of attackers. Due to the characteristics of low traffic, long attack time, diverse attack methods, and real-time evolution, existing detection methods have not been able to detect them comprehensively. Current research suggests that a suspicious domain name can be obtained by analyzing

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Schoenmakers, Birgitte, and Johan Wens. "Efficiency, Usability, and Outcomes of Proctored Next-Level Exams for Proficiency Testing in Primary Care Education: Observational Study." JMIR Formative Research 5, no. 8 (2021): e23834. http://dx.doi.org/10.2196/23834.

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Background The COVID-19 pandemic has affected education and assessment programs and has resulted in complex planning. Therefore, we organized the proficiency test for admission to the Family Medicine program as a proctored exam. To prevent fraud, we developed a web-based supervisor app for tracking and tracing candidates’ behaviors. Objective We aimed to assess the efficiency and usability of the proctored exam procedure and to analyze the procedure’s impact on exam scores. Methods The application operated on the following three levels to register events: the recording of actions, analyses of

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Zeigler-Hill, Virgil, and Jennifer Vonk. "Borderline Personality Features and Mate Retention Behaviors: The Mediating Roles of Suspicious and Reactive Jealousy." Sexes 4, no. 4 (2023): 507–21. http://dx.doi.org/10.3390/sexes4040033.

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We investigated the roles that suspicious jealousy and reactive jealousy might play in the associations between borderline personality features (BPF) and mate retention behaviors. Study 1 (N = 406) found that BPF had positive indirect associations with benefit-provisioning behaviors and cost-inflicting behaviors through suspicious jealousy but not through reactive jealousy. Study 2 (N = 334 (a dyadic sample of 167 romantic couples)) revealed actor effects such that BPF had positive indirect associations with benefit-provisioning behaviors and cost-inflicting behaviors through suspicious jealou

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Saghehei, Ehsan, and Azizollah Memariani. "Suspicious Behavior Detection in Debit Card Transactions using Data Mining." Information Resources Management Journal 28, no. 3 (2015): 1–14. http://dx.doi.org/10.4018/irmj.2015070101.

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The approach used in this paper is an implementation of a data mining process against real-life transactions of debit cards with the aim of detecting suspicious behavior. The framework designed for this purpose has been obtained through merging supervised and unsupervised models. First, due to unlabeled data, Twostep and Self-Organizing Map algorithms have been used in clustering the transactions. A C5.0 classification algorithm has been applied to evaluate supervised models and also to detect suspicious behaviors. An innovative plan has been designed to evaluate hybrid models and select the m

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Gong, Kai, Zhong Xu, Zhefeng Cai, Yuxiu Chen, and Zhanxiang Wang. "Internet Hospitals Help Prevent and Control the Epidemic of COVID-19 in China: Multicenter User Profiling Study." Journal of Medical Internet Research 22, no. 4 (2020): e18908. http://dx.doi.org/10.2196/18908.

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Background During the spread of the novel coronavirus disease (COVID-19), internet hospitals in China were engaged with epidemic prevention and control, offering epidemic-related online services and medical support to the public. Objective The aim of this study is to explore the role of internet hospitals during the prevention and control of the COVID-19 outbreak in China. Methods Online epidemic-related consultations from multicenter internet hospitals in China during the COVID-19 epidemic were collected. The counselees were described and classified into seven type groups. Symptoms were recor

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Poornima, S., and M. Geethanjali. "Shilling Attack Detection in User Based Recommendation System." Data Analytics and Artificial Intelligence 3, no. 2 (2023): 85–94. http://dx.doi.org/10.46632/daai/3/2/17.

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The majority of the existing unsupervised methods for detecting shilling attacks are based on user rating patterns, ignoring the differences in rating behavior between legitimate users and attack users. These methods have low accuracy in detecting different shilling attacks without having any prior knowledge of the attack types. We provide a novel unsupervised shilling assault detection technique based on an examination of user rating behavior in order to overcome these constraints. By first examining the deviation of rating tendencies on each item, we are able to determine the target item(s)

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Shrushti, Thigale, Musale Jitendra, Shinde Swapnil, Deshmane Swamini, and Kale Harshad. "Deep Learning model for Anomaly Detection in Video Surveillance: A CNN Approach." Research and Applications: Embedded System 7, no. 2 (2024): 32–44. https://doi.org/10.5281/zenodo.11483938.

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<em>Suspicious activity encompasses a broad concept relating to actions, behaviors, or occurrences that give rise to concerns regarding potential illegality, threat, or ethical violations. This term is commonly employed in various domains such as law enforcement, cyber security, and financial sectors. Detecting and addressing suspicious activity often involves vigilant observation, data analysis, and the use of technology to identify patterns that deviate from established norms. Individual and community awareness is essential for recognizing and reporting such activities, contributing to the o

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Gruenewald, Jeff, Brent R. Klein, Grant Drawve, Brent L. Smith, and Katie Ratcliff. "Suspicious preoperational activities and law enforcement interdiction of terrorist plots." Policing: An International Journal 42, no. 1 (2019): 89–107. http://dx.doi.org/10.1108/pijpsm-08-2018-0125.

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Purpose The purpose of this paper is to provide a metric for validating the Nationwide Suspicious Activity Reporting (SAR) Initiative’s (NSI) sixteen-category instrument, which is designed to guide law enforcement in the collection and analysis of suspicious behaviors preceding serious crimes, including terrorist attacks. Design/methodology/approach Data on suspicious preoperational activities and terrorism incident outcomes in the USA between 1972 and 2013 come from the American Terrorism Study (ATS). Using a mixed-method approach, the authors conduct descriptive and multivariate analyses to

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Jiang, Meng, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, and Christos Faloutsos. "Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algorithms." IEEE Transactions on Knowledge and Data Engineering 28, no. 8 (2016): 2187–200. http://dx.doi.org/10.1109/tkde.2016.2555310.

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Martinez Torres, Duber, Humberto Loaiza Correa, and Eduardo Caicedo Bravo. "Online learning of contexts for detecting suspicious behaviors in surveillance videos." Image and Vision Computing 89 (September 2019): 197–210. http://dx.doi.org/10.1016/j.imavis.2019.07.006.

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Sharma, Ritesh Manoj. "Active Chat Monitoring and Suspicious Chat Detection Over Internet." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32752.

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In the digital age, the proliferation of online communication platforms has facilitated unprecedented levels of connectivity and interaction. However, this interconnectedness has also introduced new challenges, particularly in ensuring the safety and security of users in online environments. One critical area of concern is the monitoring and detection of suspicious activities within chat platforms, where malicious actors may engage in harmful behaviors such as cyberbullying, harassment, or illicit activities. This research paper focuses on the development and implementation of active chat moni

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Saravanan Arumugam. "Deep Learning-Based Smart Invigilation System for Enhanced Exam Integrity." Proceedings of Engineering and Technology Innovation 29 (February 10, 2025): 99–115. https://doi.org/10.46604/peti.2024.14105.

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This study proposes a smart invigilation system to preserve exam integrity by detecting suspicious student behaviors using deep learning. The model consists of three phases, i.e., student identity verification using face recognition, behavioral sampling for model training utilizing gesture analysis and convolutional 3D networks for emotion analysis, and live video analysis of suspicious activities integrating gesture, emotional analysis, and face recognition. The model is evaluated using 4,000 training and 1,000 test images and identifies non-cheating activities with 99% accuracy and cheating

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Yadav, Pratik. "Predict, Identify and Alert on Suspicious Activity by Multiple Zone." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4705–8. http://dx.doi.org/10.22214/ijraset.2023.52523.

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Abstract: Suspicious human activity detection in security capture is a study topic in image processing and vision. The mysterious identification of human activity from video surveillance is an area of study in both fields. Human activity can be monitored visually in conspicuous public spaces like bus depots, airports, railway stations, financial institutions, malls, schools, and universities to avoid terrorist activity, vandalism, accidents, prohibited parking spaces, vandalism, fighting chain theft, criminality, and other unusual behavior. Extremely difficult to continually monitor public spa

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Adams, Peter, and Nancy M. Smith. "Understanding behavior detection technology: How it finds suspicious behaviors and meets requirements of new compliance environment." Journal of Investment Compliance 5, no. 1 (2004): 33–38. http://dx.doi.org/10.1108/15285810410636055.

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Ayed, M. B., S. Elkosantini, and M. Abid. "An Automated Surveillance System Based on Multi-Processor and GPU Architecture." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2319–23. https://doi.org/10.5281/zenodo.1119002.

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Video surveillance systems are a powerful tool applied in various systems. Traditional systems based on human vision are to be avoided due to human errors. An automated surveillance system based on suspicious behavior presents a great challenge to developers. Such detection is a rather complex procedure and also a rather time-consuming one. An abnormal behavior could be identified by: actions, faces, route, etc. The definition of the characteristics of an abnormal behavior still present a big problem. This paper proposes a specific architecture for a surveillance system. The aim is to accelera

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Gupta, Neha, and Bharat Bhushan Agarwal. "Suspicious Activity Classification in Classrooms using Deep Learning." Engineering, Technology & Applied Science Research 13, no. 6 (2023): 12226–30. http://dx.doi.org/10.48084/etasr.6228.

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Video processing is attracting the attention of both research and industry. The existence of intelligent surveillance cameras with high processing power has paved the way for designing intelligent visual surveillance systems. Along with analyzing video for information recovery, it is nowadays used to analyze live surveillance video to detect activities. These systems are implemented in real time. The proposed work's goal is to create a method that can examine and discover suspicious behaviors in the lecture room environment. Video analytics offers the most efficient answer because it enables p

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Navale, Dr. Mahesh, Aryan Arjun Jadhav, Mahesh Shrikrishna Kadam, Shalmali Dipak Karandikar, and Siddhi Anil Kate. "From Manual to Automated: A Computer Vision-Based Solution for Exam Cheating Detection." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 5 (2024): 414–19. https://doi.org/10.5281/zenodo.14066366.

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Cheating during exams is a widespread issue that undermines the credibility of educational assessments. Traditional invigilation methods, relying on manual supervision, often fall short in effectively detecting dishonest behavior, especially in large-scale exam settings. This study proposes an automated system that leverages computer vision and CCTV footage to detect suspicious behavior in real time, offering a scalable solution for maintaining exam integrity. Results demonstrate that the proposed method is both reliable and efficient, achieving high accuracy in detecting cheating behaviors wi

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Lowe, Maria R., Angela Stroud, and Alice Nguyen. "Who Looks Suspicious? Racialized Surveillance in a Predominantly White Neighborhood." Social Currents 4, no. 1 (2016): 34–50. http://dx.doi.org/10.1177/2329496516651638.

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In recent decades, neighborhoods across the United States have begun to employ digital media to monitor their communities for outsiders who are seen as suspicious. Yet, little is known about these surveillance practices and their consequences at the individual and neighborhood levels. Such monitoring behaviors are important to analyze not only because of the ways that perceptions of criminal threat are often racialized but also because of the role that private citizens play in initiating contact between strangers and the police. Based on an analysis of e-mails submitted to a listserv in a libe

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Subha, I., P. Narmadha, S. Nivedha, and T. Sethukarasi. "Real-Time Suspicious Human Action Recognition from Surveillance Videos for Resource-Constrained Devices." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3790–97. http://dx.doi.org/10.1166/jctn.2020.9322.

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Recent developments in computer vision are seen as a vital advancement in video surveillance. The goal of this research is to build a deep learning model that is capable of analyzing and classifying the video from running CCTV streams to detect criminal actions and identify suspects on the scene. In particular, we focus on the detection of dangerous human behaviors in surveillance videos. This work provides a low cost embedded solution that can be integrated with the existing CCTV cameras. This integration can reduce the cost of transmitting the data to any centralized server, which may have v

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Alimahomed-Wilson, Sabrina. "When the FBI Knocks: Racialized State Surveillance of Muslims." Critical Sociology 45, no. 6 (2018): 871–87. http://dx.doi.org/10.1177/0896920517750742.

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The sustained fixation on Muslims as the perennial suspects in domestic terrorism is a stereotype that continues to pervade counter-intelligence driven efforts. This research analyzes 113 cases of FBI contact with US Muslims living in Los Angeles, CA. Based upon these cases, this research suggests that every day, normal behavior becomes suspicious only when practiced by US Muslims, which would otherwise be acceptable, mundane, and unremarkable for ordinary white Christians, therefore constituting a form of “racialized state surveillance.” The most prevalent questions asked by FBI agents to Mus

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Arroyo, Roberto, J. Javier Yebes, Luis M. Bergasa, Iván G. Daza, and Javier Almazán. "Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls." Expert Systems with Applications 42, no. 21 (2015): 7991–8005. http://dx.doi.org/10.1016/j.eswa.2015.06.016.

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PELED, DORON, and HONGYANG QU. "ENFORCING CONCURRENT TEMPORAL BEHAVIORS." International Journal of Foundations of Computer Science 17, no. 04 (2006): 743–61. http://dx.doi.org/10.1142/s012905410600408x.

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The outcome of verifying software is often a 'counterexample', i.e., a listing of the actions and states of a behavior not satisfying the specification. The verification is usually done using a model of the software (often also using some abstraction to reduce its complexity) rather than the actual code. In order to understand the reason for the failure manifested by such a counterexample, it is sometimes necessary to test such an execution using the actual code. In this way we also find out whether we have a genuine error or a "false negative". Due to nondeterminism in concurrent code, enforc

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Zeigler-Hill, Virgil, Cheryl A. Cosby, Jennifer K. Vrabel, and Ashton C. Southard. "Narcissism and mate retention behaviors: What strategies do narcissistic individuals use to maintain their romantic relationships?" Journal of Social and Personal Relationships 37, no. 10-11 (2020): 2737–57. http://dx.doi.org/10.1177/0265407520939190.

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The present studies examined the possibility that narcissistic admiration (assertive self-enhancement and self-promotion) and narcissistic rivalry (self-protection and self-defense) would have divergent associations with benefit-provisioning and cost-inflicting mate retention behaviors. Study 1 ( N = 625) revealed that narcissistic admiration was associated with benefit-provisioning behaviors, whereas narcissistic rivalry was associated with cost-inflicting behaviors. Study 2 ( N = 349) showed that narcissistic admiration was positively associated with cost-inflicting behaviors when levels of

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Yang, Zhihai, Qindong Sun, Yaling Zhang, Lei Zhu, and Wenjiang Ji. "Inference of Suspicious Co-Visitation and Co-Rating Behaviors and Abnormality Forensics for Recommender Systems." IEEE Transactions on Information Forensics and Security 15 (2020): 2766–81. http://dx.doi.org/10.1109/tifs.2020.2977023.

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do Nascimento, Vinicius D., Tiago A. O. Alves, Claudio M. de Farias, and Diego Leonel Cadette Dutra. "A Hybrid Framework for Maritime Surveillance: Detecting Illegal Activities through Vessel Behaviors and Expert Rules Fusion." Sensors 24, no. 17 (2024): 5623. http://dx.doi.org/10.3390/s24175623.

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Maritime traffic is essential for global trade but faces significant challenges, including navigation safety, environmental protection, and the prevention of illicit activities. This work presents a framework for detecting illegal activities carried out by vessels, combining navigation behavior detection models with rules based on expert knowledge. Using synthetic and real datasets based on the Automatic Identification System (AIS), we structured our framework into five levels based on the Joint Directors of Laboratories (JDL) model, efficiently integrating data from multiple sources. Activiti

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Galdelli, Alessandro, Adriano Mancini, Carmen Ferrà, and Anna Nora Tassetti. "A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities." Sensors 21, no. 8 (2021): 2756. http://dx.doi.org/10.3390/s21082756.

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Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of

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Huang, Hong, Weitao Huang, and Feng Jiang. "GSIDroid: A Suspicious Subgraph-Driven and Interpretable Android Malware Detection System." Sensors 25, no. 13 (2025): 4116. https://doi.org/10.3390/s25134116.

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In recent years, the growing threat of Android malware has caused significant economic losses and posed serious risks to user security and privacy. Machine learning-based detection approaches have improved the accuracy of malware identification, thereby providing more effective protection for Android users. However, graph-based detection methods rely on whole-graph computations instead of subgraph-level analyses, and they often ignore the semantic information of individual nodes. Moreover, limited attention has been paid to the interpretability of these models, hindering a deeper understanding

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Ahmad, Heryanto, Stiawan Deris, Hermansyah Adi, et al. "The incorporation of stacked long short-term memory into intrusion detection systems for botnet attack classification." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3657–70. https://doi.org/10.11591/ijai.v13.i3.pp3657-3670.

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Botnets are a common cyber-attack method on the internet, causing infrastructure damage, data theft, and malware distribution. The continuous evolution and adaptation to enhanced defense tactics make botnets a strong and difficult threat to combat. In light of this, the study's main objective was to find out how well techniques like principal component analysis (PCA), synthetic minority oversampling technique (SMOTE), and long short-term memory (LSTM) can help find botnet attacks. PCA shows the ability to reduce the feature dimensions in network data, allowing for a more efficient and effectiv

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Li, Juan, Xianwen Fang, and Yinkai Zuo. "Entropy-Based Behavioral Closeness Filtering Chaotic Activity Method." Mathematics 12, no. 5 (2024): 666. http://dx.doi.org/10.3390/math12050666.

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In the era of big data, one of the key challenges is to discover process models and gain insights into business processes by analyzing event data recorded in information systems. However, Chaotic activity or infrequent behaviors often appear in actual event logs. Process models containing such behaviors are complex, difficult to understand, and hide the relevant key behaviors of the underlying processes. Established studies have generally achieved chaotic activity filtering by filtering infrequent activities or activities with high entropy values and ignoring the behavioral relationships that

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Researcher. "PREVENTING SYBIL ATTACKS IN BLOCKCHAIN USING DISTRIBUTED BEHAVIOR MONITORING AND INTEGRITY VALIDATION OF MINERS." International Journal of Computer Science Review (IJCSR) 1, no. 1 (2024): 12–20. https://doi.org/10.5281/zenodo.14440824.

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The swift expansion of blockchain technology has underscored the critical necessity of safeguarding decentralized networks against Sybil attacks, wherein malicious actors compromise network integrity by establishing a multitude of fraudulent nodes. This study introduces an innovative methodology for alleviating Sybil attacks within blockchain networks via a distributed behavior monitoring framework tailored for miners, in conjunction with augmented data integrity validation mechanisms. Within this system, the conduct of each miner is perpetually observed and documented, with suspicious behavio

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Heryanto, Ahmad, Deris Stiawan, Adi Hermansyah, et al. "The incorporation of stacked long short-term memory into intrusion detection systems for botnet attack classification." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3657. http://dx.doi.org/10.11591/ijai.v13.i3.pp3657-3670.

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<p><span lang="EN-US">Botnets are a common cyber-attack method on the internet, causing infrastructure damage, data theft, and malware distribution. The continuous evolution and adaptation to enhanced defense tactics make botnets a strong and difficult threat to combat. In light of this, the study's main objective was to find out how well techniques like principal component analysis (PCA), synthetic minority oversampling technique (SMOTE), and long short-term memory (LSTM) can help find botnet attacks. PCA shows the ability to reduce the feature dimensions in network data, allowing

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Chang, Remco, Alvin Lee, Mohammad Ghoniem, et al. "Scalable and Interactive Visual Analysis of Financial Wire Transactions for Fraud Detection." Information Visualization 7, no. 1 (2008): 63–76. http://dx.doi.org/10.1057/palgrave.ivs.9500172.

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Large financial institutions such as Bank of America handle hundreds of thousands of wire transactions per day. Although most transactions are legitimate, these institutions have legal and financial obligations to discover those that are suspicious. With the methods of fraudulent activities ever changing, searching on predefined patterns is often insufficient in detecting previously undiscovered methods. In this paper, we present a set of coordinated visualizations based on identifying specific keywords within the wire transactions. The different views used in our system depict relationships a

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Sikder, Abu Sayed. "Unveiling the Human Aspect of Cybersecurity: A Holistic Examination of Employee Behavior and Its Significance in Safeguarding Organizational Security within the Context of Bangladesh." International Journal of Imminent Science & Technology. 1, no. 1 (2017): 199–215. http://dx.doi.org/10.70774/ijist.v1i1.19.

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This research delves into the critical role of the human factor in cybersecurity and its impact on organizational security within the specific context of Bangladesh. As cyber threats continue to evolve in complexity and sophistication, understanding and addressing the human element in cybersecurity have become paramount for safeguarding organizational assets and sensitive information. This study adopts a comprehensive and multifaceted approach to explore employee behavior, awareness, and practices concerning cybersecurity within various organizations in Bangladesh. The research methodology com

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VARUN R, Pranav P Rao, Manohar S N, and Nagsharan A. "Virtual Overseer for Examination Using Artificial Intelligence." Indonesian Journal of Information Systems 6, no. 2 (2024): 174–82. http://dx.doi.org/10.24002/ijis.v6i2.7311.

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The Virtual Overseer for Examination using AI is a proposed system that utilizes artificial intelligence (AI) to monitor and supervise online exams. The system aims to enhance the credibility and integrity of online exams by detecting and preventing cheating behaviors such as copying answers or using unauthorized materials. The proposed system utilizes computer vision and machine learning algorithms to analyze exam sessions in real time and flag suspicious behaviors for review by human proctors. The system also employs facial recognition technology to authenticate test-takers' identity and ens

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Li, Zhuoxuan, Warren Seering, Tiffany Tao, and Shengnan Cao. "Understanding Community Behaviors in For-Profit Open Source Hardware Projects." Proceedings of the Design Society: International Conference on Engineering Design 1, no. 1 (2019): 2397–406. http://dx.doi.org/10.1017/dsi.2019.246.

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AbstractFree contributors have successfully shown the potential in large/complex software co-creation in the Free and Open Source Software Movement, triggering many discussions and exploration ventures from academia to industry and to the government. Though many research efforts explored whether the same level of co-creation efforts could take place broadly in the hardware realm, very few research studies focus on profit-seeking hardware projects initiated by companies. In fact, the specific nature of being tangible and profitable makes company-led open source hardware projects suspicious to b

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Zhang, Ge, Zhao Li, Jiaming Huang, et al. "eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks." ACM Transactions on Information Systems 40, no. 3 (2022): 1–29. http://dx.doi.org/10.1145/3474379.

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With the development of e-commerce, fraud behaviors have been becoming one of the biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking system of e-commerce platforms and adversely influence the shopping experience of users. It is of great practical value to detect fraud behaviors on e-commerce platforms. However, the task is non-trivial, since the adversarial action taken by fraudsters. Existing fraud detection systems used in the e-commerce industry easily suffer from performance decay and can not adapt to the upgrade of fraud patterns, as they take already

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McNamarah, Chan. "White Caller Crime: Racialized Police Communication and Existing While Black." Michigan Journal of Race & Law, no. 24.2 (2019): 335. http://dx.doi.org/10.36643/mjrl.24.2.white.

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Over the past year, reports to the police about Black persons engaged in innocuous behaviors have bombarded the American consciousness. What do we make of them? And, equally important, what are the consequences of such reports? This Article is the first to argue that the recent spike in calls to the police against Black persons who are simply existing must be understood as a systematic phenomenon which it dubs racialized police communication. The label captures two related practices. First, racially motivated police reporting—calls, complaints, or reports made when Black persons are engaged in

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Zhou, Xiaojun, Zhen Xu, Liming Wang, Kai Chen, Cong Chen, and Wei Zhang. "Inside the Closed World: User and Device Profile Analytics for SCADA Security." MATEC Web of Conferences 173 (2018): 03039. http://dx.doi.org/10.1051/matecconf/201817303039.

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Attacks that use sophisticated and complex methods in-creased recently, aiming to infiltrate the Supervisory Control and Data Acquisition (SCADA) system and stay undetected. Therefore, attackers often get access to authorized permissions of SCADA and bring catastrophic damages by sending ‘legitimate’ control commands. Furthermore, insiders may also misuse or abuse their permissions to damage SCADA system, which is difficult to predict and protect against them. Most existing security systems employ standard signature-based or anomaly-based approaches, which are not able to identify this type of

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Mehmood, Abid. "LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection." Sensors 21, no. 24 (2021): 8501. http://dx.doi.org/10.3390/s21248501.

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The continuous development of intelligent video surveillance systems has increased the demand for enhanced vision-based methods of automated detection of anomalies within various behaviors found in video scenes. Several methods have appeared in the literature that detect different anomalies by using the details of motion features associated with different actions. To enable the efficient detection of anomalies, alongside characterizing the specificities involved in features related to each behavior, the model complexity leading to computational expense must be reduced. This paper provides a li

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Chen, Tong, Jiqiang Liu, Yalun Wu, et al. "Survey on Astroturfing Detection and Analysis from an Information Technology Perspective." Security and Communication Networks 2021 (December 1, 2021): 1–16. http://dx.doi.org/10.1155/2021/3294610.

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With the development of the Internet, user comments produced an unprecedented impact on information acquisition, goods purchase, and other aspects. For example, the user comments can quickly render a topic the focus of discussion in social networks. It can promote the sales of goods in e-commerce, and it influences the ratings of books, movies, or albums. Among these network applications and services, “astroturfing,” a kind of online suspicious behavior, can generate abnormal, damaging, and even illegal behaviors in cyberspace that mislead public perception and bring a bad effect on Internet u

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Jiang, Chao. "Deploying human body detection technologies in security systems: An in-depth study of the FASTER-GCNN algorithm." Applied and Computational Engineering 32, no. 1 (2024): 210–15. http://dx.doi.org/10.54254/2755-2721/32/20230213.

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The field of human body detection, a pivotal area in computer vision, merits comprehensive discussion. Remarkable advancements have been achieved in the techniques for human body detection over the past few decades, with significant applications spanning various sectors. This discussion delves into the potential of human detection technology within the realm of security - a field that necessitates efficient and accurate human detection technology to promptly identify potential threats, suspicious behaviors, or unusual activities. Deep learning-based human detection algorithms have substantiall

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Cheng, Dawei, Sheng Xiang, Chencheng Shang, Yiyi Zhang, Fangzhou Yang, and Liqing Zhang. "Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 362–69. http://dx.doi.org/10.1609/aaai.v34i01.5371.

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Credit card fraud is an important issue and incurs a considerable cost for both cardholders and issuing institutions. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant patterns in fraudulent behavior. Therefore, in this work, we propose a spatial-temporal attention-based neural network (STAN) for fraud detection. In particular, transaction records are modeled by

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Muhajir Syamsu. "Relationship Between Artificial Intelligence and Machine Learning in Network Monitoring." International Journal of Integrative Research 1, no. 6 (2023): 359–76. http://dx.doi.org/10.59890/ijir.v1i6.72.

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Artificial Intelligence and Machine Learning can have a close relationship. AI is a discipline that focuses on developing systems that can perform tasks that require human intelligence, where Machine Learning is one of the main branches of AI that deals with the development of algorithms and statistical models to analyze network data in real-time, identify patterns and behaviors and take appropriate actions, thereby strengthening the detection of security threats in the network through network traffic data analysis, ML algorithms can learn from normal traffic patterns and identify suspicious b

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Fatahi, Somayeh, and Mohammad Rabiei. "Users clustering Based on Search Behavior Analysis Using the LRFM Model (Case Study: Iran Scientific Information Database (Ganj))." Iranian Journal of Information Processing & Management 36, no. 2 (2021): 419–42. https://doi.org/10.35050/JIPM010.2020.006.

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Iran scientific information database (Ganj) which includes almost one million scientific records provides the search opportunity in dissertations, domestic scientific journals, articles, conferences, research projects, and governmental reports. A large number of researchers meet the needs of their scientific and research resources from Ganj database daily. Users’ needs and behaviors are variant and understanding it helps system administrators to use different strategies to manage the better databases and provide efficient services to users. One way to understand users’ needs is to

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Researcher. "DEEP LEARNING FOR AUTOMATICALLY DETECTING CHEATING IN ONLINE EXAMS." International Journal of Information Technology Research and Development (IJITRD) 4, no. 2 (2023): 17–25. https://doi.org/10.5281/zenodo.15267916.

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<em>Since the quick move towards online education, strict concerns over academic integrity have been heightened to such an extent, especially in terms of cheating during remote exams. The purpose of this research is to establish a robust and a deep learning based approach for detecting cheating behaviors during online exams, which can ensure fairness and reliability of remote assessments. Using the latest developments in convolutional neural networks and long short term memory networks, our method seamlessly fuse the data analysis of multiple modalities, web cam feed of facial expressions, eye

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