Journal articles: 'User activity detection'

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Zhu, Hao, and Georgios B. Giannakis. "Exploiting Sparse User Activity in Multiuser Detection." IEEE Transactions on Communications 59, no. 2 (February 2011): 454–65. http://dx.doi.org/10.1109/tcomm.2011.121410.090570.

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Mitra, U., and H. V. Poor. "Activity detection in a multi-user environment." Wireless Personal Communications 3, no. 1-2 (1996): 149–74. http://dx.doi.org/10.1007/bf00333928.

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Lee, Junho, and Seung-Hwan Lee. "Low dimensional multiuser detection exploiting low user activity." Journal of Communications and Networks 15, no. 3 (June 2013): 283–91. http://dx.doi.org/10.1109/jcn.2013.000051.

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Zou, Shihong, Huizhong Sun, Guosheng Xu, and Ruijie Quan. "Ensemble Strategy for Insider Threat Detection from User Activity Logs." Computers, Materials & Continua 65, no. 2 (2020): 1321–34. http://dx.doi.org/10.32604/cmc.2020.09649.

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Wang, Shuwen, Xingquan Zhu, Weiping Ding, and Amir Alipour Yengejeh. "Cyberbullying and Cyberviolence Detection: A Triangular User-Activity-Content View." IEEE/CAA Journal of Automatica Sinica 9, no. 8 (August 2022): 1384–405. http://dx.doi.org/10.1109/jas.2022.105740.

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Park, Hansol, Kookjin Kim, Dongil Shin, and Dongkyoo Shin. "BGP Dataset-Based Malicious User Activity Detection Using Machine Learning." Information 14, no. 9 (September 13, 2023): 501. http://dx.doi.org/10.3390/info14090501.

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Recent advances in the Internet and digital technology have brought a wide variety of activities into cyberspace, but they have also brought a surge in cyberattacks, making it more important than ever to detect and prevent cyberattacks. In this study, a method is proposed to detect anomalies in cyberspace by consolidating BGP (Border Gateway Protocol) data into numerical data that can be trained by machine learning (ML) through a tokenizer. BGP data comprise a mix of numeric and textual data, making it challenging for ML models to learn. To convert the data into a numerical format, a tokenizer, a preprocessing technique from Natural Language Processing (NLP), was employed. This process goes beyond merely replacing letters with numbers; its objective is to preserve the patterns and characteristics of the data. The Synthetic Minority Over-sampling Technique (SMOTE) was subsequently applied to address the issue of imbalanced data. Anomaly detection experiments were conducted on the model using various ML algorithms such as One-Class Support Vector Machine (One-SVM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), Random Forest (RF), and Autoencoder (AE), and excellent performance in detection was demonstrated. In experiments, it performed best with the AE model, with an F1-Score of 0.99. In terms of the Area Under the Receiver Operating Characteristic (AUROC) curve, good performance was achieved by all ML models, with an average of over 90%. Improved cybersecurity is expected to be contributed by this research, as it enables the detection and monitoring of cyber anomalies from malicious users through BGP data.

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Parwez, Md Salik, Danda B. Rawat, and Moses Garuba. "Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network." IEEE Transactions on Industrial Informatics 13, no. 4 (August 2017): 2058–65. http://dx.doi.org/10.1109/tii.2017.2650206.

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Pathmaperuma, Madushi H., Yogachandran Rahulamathavan, Safak Dogan, and Ahmet Kondoz. "CNN for User Activity Detection Using Encrypted In-App Mobile Data." Future Internet 14, no. 2 (February 21, 2022): 67. http://dx.doi.org/10.3390/fi14020067.

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In this study, a simple yet effective framework is proposed to characterize fine-grained in-app user activities performed on mobile applications using a convolutional neural network (CNN). The proposed framework uses a time window-based approach to split the activity’s encrypted traffic flow into segments, so that in-app activities can be identified just by observing only a part of the activity-related encrypted traffic. In this study, matrices were constructed for each encrypted traffic flow segment. These matrices acted as input into the CNN model, allowing it to learn to differentiate previously trained (known) and previously untrained (unknown) in-app activities as well as the known in-app activity type. The proposed method extracts and selects salient features for encrypted traffic classification. This is the first-known approach proposing to filter unknown traffic with an average accuracy of 88%. Once the unknown traffic is filtered, the classification accuracy of our model would be 92%.

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Bashir, Sulaimon Adebayo, Andrei Petrovski, and Daniel Doolan. "A framework for unsupervised change detection in activity recognition." International Journal of Pervasive Computing and Communications 13, no. 2 (June 5, 2017): 157–75. http://dx.doi.org/10.1108/ijpcc-03-2017-0027.

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Purpose This purpose of this paper is to develop a change detection technique for activity recognition model. The approach aims to detect changes in the initial accuracy of the model after training and when the model is deployed for recognizing new unseen activities without access to the ground truth. The changes between the two sessions may occur because of differences in sensor placement, orientation and user characteristics such as age and gender. However, many of the existing approaches for model adaptation in activity recognition are blind methods because they continuously adapt the recognition model without explicit detection of changes in the model performance. Design/methodology/approach The approach determines the variation between reference activity data belonging to different classes and newly classified unseen data. If there is coherency between the data, it means the model is correctly classifying the instances; otherwise, a significant variation indicates wrong instances are being classified to different classes. Thus, the approach is formulated as a two-level architectural framework comprising of the off-line phase and the online phase. The off-line phase extracts of Shewart Chart change parameters from the training data set. The online phase performs classification of new samples and the detection of the changes in each class of activity present in the data set by using the change parameters computed earlier. Findings The approach is evaluated using a real activity-recognition data set. The results show that there are consistent detections that correlate with the error rate of the model. Originality/value The developed approach does not use ground truth to detect classifier performance degradation. Rather, it uses a data discrimination method and a base classifier to detect the changes by using the parameters computed from the reference data of each class to discriminate outliers in the new data being classified to the same class. The approach is the first, to the best of the authors’ knowledge, that addresses the problem of detecting within-user and cross-user variations that lead to concept drift in activity recognition. The approach is also the first to use statistical process control method for change detection in activity recognition, with a robust integrated framework that seamlessly detects variations in the underlying model performance.

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Kim, Park, Kim, Cho, and Kang. "Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms." Applied Sciences 9, no. 19 (September 25, 2019): 4018. http://dx.doi.org/10.3390/app9194018.

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Insider threats are malicious activities by authorized users, such as theft of intellectual property or security information, fraud, and sabotage. Although the number of insider threats is much lower than external network attacks, insider threats can cause extensive damage. As insiders are very familiar with an organization’s system, it is very difficult to detect their malicious behavior. Traditional insider-threat detection methods focus on rule-based approaches built by domain experts, but they are neither flexible nor robust. In this paper, we propose insider-threat detection methods based on user behavior modeling and anomaly detection algorithms. Based on user log data, we constructed three types of datasets: user’s daily activity summary, e-mail contents topic distribution, and user’s weekly e-mail communication history. Then, we applied four anomaly detection algorithms and their combinations to detect malicious activities. Experimental results indicate that the proposed framework can work well for imbalanced datasets in which there are only a few insider threats and where no domain experts’ knowledge is provided.

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Wei, Chao, Huaping Liu, Zaichen Zhang, Jian Dang, and Liang Wu. "Approximate Message Passing-Based Joint User Activity and Data Detection for NOMA." IEEE Communications Letters 21, no. 3 (March 2017): 640–43. http://dx.doi.org/10.1109/lcomm.2016.2624297.

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Han, Gang Tao, Ying Qiang Ding, Xiao Min Mu, and Jian Kang Zhang. "A Modified Energy Detection Algorithm Based on Primary User Activity for Cognitive Radio Networks." Applied Mechanics and Materials 548-549 (April 2014): 1351–54. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1351.

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Spectrum sensing is used to identify the unused frequency bands and as such plays a key role in dynamic spectrum access. In most of the existing spectrum sensing models, the channel state of primary user is assumed unchanged within the spectrum sensing duration, which is not suitable for the channels with high activity, in which the primary user accesses or vacates from the channel frequently. In this paper, a Modified Energy Detection (MED) algorithm is proposed for this scenarios by considering the primary user activity within the spectrum sensing duration. Theory analysis and computer simulation results show that both the probability of detection and false alarm in this scenarios have been improved with our MED.

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Park, Soojin, Sungyong Park, and Kyeongwook Ma. "An Automatic User Activity Analysis Method for Discovering Latent Requirements: Usability Issue Detection on Mobile Applications." Sensors 18, no. 9 (September 5, 2018): 2963. http://dx.doi.org/10.3390/s18092963.

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Starting with the Internet of Things (IoT), new forms of system operation concepts have emerged to provide creative services through collaborations among autonomic devices. Following these paradigmatic changes, the ability of each participating system to automatically diagnose the degree of quality it is providing is inevitable. This paper proposed a method to automatically detect symptoms that hinder certain quality attributes. The method consisted of three steps: (1) extracting information from real usage logs and automatically generating an activity model from the captured information; (2) merging multiple user activity models into a single, representative model; and (3) detecting differences between the representative user activity model, and an expected activity model. The proposed method was implemented in a domain-independent framework, workable on the Android platform. Unlike other related works, we used quantitative evaluation results to show the benefits of applying the proposed method to five Android-based, open-source mobile applications. The evaluation results showed that the average precision rate for the automatic detection of symptoms was 70%, and the success rate for user implementation of usage scenarios demonstrated an improvement of around 21%, when the automatically detected symptoms were resolved.

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Benova, Lenka, and Ladislav Hudec. "Comprehensive Analysis and Evaluation of Anomalous User Activity in Web Server Logs." Sensors 24, no. 3 (January 24, 2024): 746. http://dx.doi.org/10.3390/s24030746.

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In this study, we present a novel machine learning framework for web server anomaly detection that uniquely combines the Isolation Forest algorithm with expert evaluation, focusing on individual user activities within NGINX server logs. Our approach addresses the limitations of traditional methods by effectively isolating and analyzing subtle anomalies in vast datasets. Initially, the Isolation Forest algorithm was applied to extensive NGINX server logs, successfully identifying outlier user behaviors that conventional methods often overlook. We then employed DBSCAN for detailed clustering of these anomalies, categorizing them based on user request times and types. A key innovation of our methodology is the incorporation of post-clustering expert analysis. Cybersecurity professionals evaluated the identified clusters, adding a crucial layer of qualitative assessment. This enabled the accurate distinction between benign and potentially harmful activities, leading to targeted responses such as access restrictions or web server configuration adjustments. Our approach demonstrates a significant advancement in network security, offering a more refined understanding of user behavior. By integrating algorithmic precision with expert insights, we provide a comprehensive and nuanced strategy for enhancing cybersecurity measures. This study not only advances anomaly detection techniques but also emphasizes the critical need for a multifaceted approach in protecting web server infrastructures.

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Truong-Allié, Camille, Alexis Paljic, Alexis Roux, and Martin Herbeth. "User Behavior Adaptive AR Guidance for Wayfinding and Tasks Completion." Multimodal Technologies and Interaction 5, no. 11 (October 20, 2021): 65. http://dx.doi.org/10.3390/mti5110065.

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Augmented reality (AR) is widely used to guide users when performing complex tasks, for example, in education or industry. Sometimes, these tasks are a succession of subtasks, possibly distant from each other. This can happen, for instance, in inspection operations, where AR devices can give instructions about subtasks to perform in several rooms. In this case, AR guidance is both needed to indicate where to head to perform the subtasks and to instruct the user about how to perform these subtasks. In this paper, we propose an approach based on user activity detection. An AR device displays the guidance for wayfinding when current user activity suggests it is needed. We designed the first prototype on a head-mounted display using a neural network for user activity detection and compared it with two other guidance temporality strategies, in terms of efficiency and user preferences. Our results show that the most efficient guidance temporality depends on user familiarity with the AR display. While our proposed guidance has not proven to be more efficient than the other two, our experiment hints toward several improvements of our prototype, which is a first step in the direction of efficient guidance for both wayfinding and complex task completion.

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Li, Jing, Yabo Dong, Shengkai Fang, Haowen Zhang, and Duanqing Xu. "User Context Detection for Relay Attack Resistance in Passive Keyless Entry and Start System." Sensors 20, no. 16 (August 9, 2020): 4446. http://dx.doi.org/10.3390/s20164446.

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In modern cars, the Passive Keyless Entry and Start system (PKES) has been extensively installed. The PKES enables drivers to unlock and start their cars without user interaction. However, it is vulnerable to relay attacks. In this paper, we propose a secure smartphone-type PKES system model based on user context detection. The proposed system uses the barometer and accelerometer embedded in smartphones to detect user context, including human activity and door closing event. These two types of events detection can be used by the PKES to determine the car owner’s position when the car receives an unlocking or a start command. We evaluated the performance of the proposed method using a dataset collected from user activity and 1526 door closing events. The results reveal that the proposed method can accurately and effectively detect user activities and door closing events. Therefore, smartphone-type PKES can prevent relay attacks. Furthermore, we tested the detection of door closing event under multiple environmental settings to demonstrate the robustness of the proposed method.

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Dziurakh, Yurii, Ihor Kulyniak, Hanna Sarkisian, Ivan Zhygalo, Bohdan Chepil, and Khrystyna Vaskovych. "Intrusion Detection Systems for Smart Tourism Platforms: Safeguarding Food Safety and User Privacy." Journal of Internet Services and Information Security 14, no. 4 (November 30, 2024): 484–98. https://doi.org/10.58346/jisis.2024.i4.030.

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The rapid evolution of smart tourism platforms has transformed the travel and hospitality industry, enhancing user experiences through personalized services and real-time data access. However, this technological advancement also raises significant concerns regarding food safety and user privacy. By detecting and reacting to malicious activity and unauthorized access, Intrusion Detection Systems (IDS) are essential in reducing these risks. This article examines IDS's current status in relation to smart tourism platforms, emphasizing its technical implementations, efficacy, and difficulties. The study highlights the necessity of flexible, machine learning-based strategies to improve security measures and offers a thorough framework for incorporating IDS into smart tourism systems. The results highlight how crucial strong IDS are to protecting private user information and guaranteeing food safety in a world that is becoming more networked by the day.

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Tsien, Yu Lei, and Rong Li Gai. "User Activity Based Application-Layer DoS/DDoS Attack Defense Algorithm." Applied Mechanics and Materials 742 (March 2015): 693–97. http://dx.doi.org/10.4028/www.scientific.net/amm.742.693.

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In application-layer DoS/DDoS attacks, malicious users attack the victim server by sending lots of legitimate requesting packages, which overwhelm the server bottleneck resources. Normal user’s request thus may not be satisfied. The traditional intrusion detection systems for network-layer cannot effectively identify this attack, and recent researches on this kind of attack are mainly for Web servers. This paper proposed a new defense algorithm based on user activity for topic-based Pub/Sub communication servers in mobile push notification systems. Users consuming system bottleneck resources the most can get high scores and thus are considered overactive. With some resource retaken strategy, overactive users’ connections will be dropped according to system performance level. Therefore, the system can get rid of latent threatens. Experiments indicated that this algorithm can identify normal and abnormal users well.

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Li, Bo, Jianping Zheng, and Yaoxin Gao. "Compressed Sensing Based Multiuser Detection of Grant-Free NOMA With Dynamic User Activity." IEEE Communications Letters 26, no. 1 (January 2022): 143–47. http://dx.doi.org/10.1109/lcomm.2021.3124608.

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Witte, Tim Niklas. "Phantom Malware: Conceal Malicious Actions From Malware Detection Techniques by Imitating User Activity." IEEE Access 8 (2020): 164428–52. http://dx.doi.org/10.1109/access.2020.3021743.

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He, Qi, Zhi Chen, Tony Q. S. Quek, Jinho Choi, and Shaoqian Li. "Compressive Channel Estimation and User Activity Detection in Distributed-Input Distributed-Output Systems." IEEE Communications Letters 22, no. 9 (September 2018): 1850–53. http://dx.doi.org/10.1109/lcomm.2018.2858241.

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Zhang, Jun, Yongping Pan, and Jie Xu. "Compressive Sensing for Joint User Activity and Data Detection in Grant-Free NOMA." IEEE Wireless Communications Letters 8, no. 3 (June 2019): 857–60. http://dx.doi.org/10.1109/lwc.2019.2897552.

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Lehmann, Frederic. "Joint User Activity Detection, Channel Estimation, and Decoding for Multiuser/Multiantenna OFDM Systems." IEEE Transactions on Vehicular Technology 67, no. 9 (September 2018): 8263–75. http://dx.doi.org/10.1109/tvt.2018.2841190.

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Utkovski, Zoran, Osvaldo Simeone, Tamara Dimitrova, and Petar Popovski. "Random Access in C-RAN for User Activity Detection With Limited-Capacity Fronthaul." IEEE Signal Processing Letters 24, no. 1 (January 2017): 17–21. http://dx.doi.org/10.1109/lsp.2016.2633962.

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Al-Kassawneh, Muna, Zubi Sadiq, and Sana Jahanshahi-Anbuhi. "User-friendly and ultra-stable all-inclusive gold tablets for cysteamine detection." RSC Advances 13, no. 28 (2023): 19638–50. http://dx.doi.org/10.1039/d3ra03073c.

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Esmaeili Kelishomi, Aghil, A. H. S. Garmabaki, Mahdi Bahaghighat, and Jianmin Dong. "Mobile User Indoor-Outdoor Detection Through Physical Daily Activities." Sensors 19, no. 3 (January 26, 2019): 511. http://dx.doi.org/10.3390/s19030511.

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An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications.

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Bhoge, Rutuja K., Snehal A. Nagare, Swapanali P. Mahajan, and Prajakta S. Kor. "Depression Detection by Analyzing Social Media Post of User." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2720–24. http://dx.doi.org/10.22214/ijraset.2022.41874.

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Abstract: Nowadays the problem of early depression detection is one of the most important in the field of psychology .Mental health issues are widely accepted as one of the most prominent health challenges in the world, with over 300 million people currently suffering from depression alone. With massive volumes of user-generated data on social networking platforms, researchers are increasingly using machine learning to determine whether this content can be used to detect mental health problems in users. Depression as a disorder has been an excellent concern in our society and has been continuously a hot topic for researchers in the world. Despite the massive quantity of analysis on understanding individual moods together with depression, anxiety, and stress supported activity logs collected by pervasive computing devices like smartphones, foretelling depressed moods continues to be an open question. Social networks analysis is widely applied to address this problem. In this paper, we have proposed a depression analysis and suicidal ideation detection system, for predicting the suicidal acts supported the extent of depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Social Media user his/her Posts. For this purpose, we trained and tested classifiers to differentiate whether a user is depressed or not using features extracted from his/her activities within the posts. classification machine algorithms are used to train and classify it in Different stages of depression on scale of 0-100%. Also, data was collected in the form of posts and were classified into whether the one that tweeted is in depression or not using classification algorithms of Machine Learning In this way Predictive approach for early detection of depression or other mental illnesses. This study’s main contribution is that the exploration a neighborhood of the features and its impact on detecting Depression level. This study aims to develop a deep learning model to classify users with depression via multiple instance learning, which can learn from user-level labels to identify post-level labels. By combining every possibility of posts label category, it can generate temporal posting profiles which can then be used to classify users with depression. This paper shows that there are clear differences in posting patterns between users with depression and non-depression, which is represented through the combined likelihood of posts label category. In this research, machine learning is used to process the scrapped data collected from social media users posts. Natural Language Processing (NLP), classified using BERT algorithm to detect depression potentially in amore convenient and efficient way. Keywords: Machine Learning, NLP, BERT Algorithm, Classification, Social Media Post

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Nikonowicz, Jakub, Aamir Mahmood, and Mikael Gidlund. "A Blind Signal Samples Detection Algorithm for Accurate Primary User Traffic Estimation." Sensors 20, no. 15 (July 25, 2020): 4136. http://dx.doi.org/10.3390/s20154136.

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The energy detection process for enabling opportunistic spectrum access in dynamic primary user (PU) scenarios, where PU changes state from active to inactive at random time instances, requires the estimation of several parameters ranging from noise variance and signal-to-noise ratio (SNR) to instantaneous and average PU activity. A prerequisite to parameter estimation is an accurate extraction of the signal and noise samples in a received signal time frame. In this paper, we propose a low-complexity and accurate signal samples detection algorithm as compared to well-known methods, which is also blind to the PU activity distribution. The proposed algorithm is analyzed in a semi-experimental simulation setup for its accuracy and time complexity in recognizing signal and noise samples, and its use in channel occupancy estimation, under varying occupancy and SNR of the PU signal. The results confirm its suitability for acquiring the necessary information on the dynamic behavior of PU, which is otherwise assumed to be known in the literature.

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Zain ul Abideen, Muhammad, Shahzad Saleem, and Madiha Ejaz. "VPN Traffic Detection in SSL-Protected Channel." Security and Communication Networks 2019 (October 29, 2019): 1–17. http://dx.doi.org/10.1155/2019/7924690.

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In recent times, secure communication protocols over web such as HTTPS (Hypertext Transfer Protocol Secure) are being widely used instead of plain web communication protocols like HTTP (Hypertext Transfer Protocol). HTTPS provides end-to-end encryption between the user and service. Nowadays, organizations use network firewalls and/or intrusion detection and prevention systems (IDPS) to analyze the network traffic to detect and protect against attacks and vulnerabilities. Depending on the size of organization, these devices may differ in their capabilities. Simple network intrusion detection system (NIDS) and firewalls generally have no feature to inspect HTTPS or encrypted traffic, so they rely on unencrypted traffic to manage the encrypted payload of the network. Recent and powerful next-generation firewalls have Secure Sockets Layer (SSL) inspection feature which are expensive and may not be suitable for every organizations. A virtual private network (VPN) is a service which hides real traffic by creating SSL-protected channel between the user and server. Every Internet activity is then performed under the established SSL tunnel. The user inside the network with malicious intent or to hide his activity from the network security administration of the organization may use VPN services. Any VPN service may be used by users to bypass the filters or signatures applied on network security devices. These services may be the source of new virus or worm injected inside the network or a gateway to facilitate information leakage. In this paper, we have proposed a novel approach to detect VPN activity inside the network. The proposed system analyzes the communication between user and the server to analyze and extract features from network, transport, and application layer which are not encrypted and classify the incoming traffic as malicious, i.e., VPN traffic or standard traffic. Network traffic is analyzed and classified using DNS (Domain Name System) packets and HTTPS- (Hypertext Transfer Protocol Secure-) based traffic. Once traffic is classified, the connection based on the server’s IP, TCP port connected, domain name, and server name inside the HTTPS connection is analyzed. This helps in verifying legitimate connection and flags the VPN-based traffic. We worked on top five freely available VPN services and analyzed their traffic patterns; the results show successful detection of the VPN activity performed by the user. We analyzed the activity of five users, using some sort of VPN service in their Internet activity, inside the network. Out of total 729 connections made by different users, 329 connections were classified as legitimate activity, marking 400 remaining connections as VPN-based connections. The proposed system is lightweight enough to keep minimal overhead, both in network and resource utilization and requires no specialized hardware.

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Huang, Nai-Hsuan, and Tzi-Dar Chiueh. "Sequence Design and User Activity Detection for Uplink Grant-Free NOMA in mMTC Networks." IEEE Open Journal of the Communications Society 2 (2021): 384–95. http://dx.doi.org/10.1109/ojcoms.2021.3056994.

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Gnanasekar, A. "Detecting Spam Bots on Social Network." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 5, 2021): 850–60. http://dx.doi.org/10.47059/revistageintec.v11i2.1719.

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Bots have made an appearance on social media in a variety of ways. Twitter, for instance, has been particularly hard hit, with bots accounting for a shockingly large number of its users. These bots are used for nefarious purposes such as disseminating false information about politicians and inflating celebrity expectations. Furthermore, these bots have the potential to skew the results of conventional social media research. With the multiple increases in the size, speed, and style of user knowledge in online social networks, new methods of grouping and evaluating such massive knowledge are being explored. Getting rid of malicious social bots from a social media site is crucial. The most widely used methods for identifying fraudulent social bots focus on the quantitative measures of their actions. Social bots simply mimic these choices, leading to a low level of study accuracy. Transformation clickstream sequences and semi-supervised clustering were used to develop a new technique for detecting malicious social bots. This method considers not only the probability of user activity clickstreams being moved, but also the behavior's time characteristic. The detection accuracy for various kinds of malware social bots by the detection technique assisted transfer probability of user activity clickstreams will increase by a mean of 12.8 percent, as per results from our research on real online social network sites, compared to the detection method funded estimate of user behaviour.

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Chiossi, Francesco, Robin Welsch, Steeven Villa, Lewis Chuang, and Sven Mayer. "Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience." Big Data and Cognitive Computing 6, no. 2 (May 13, 2022): 55. http://dx.doi.org/10.3390/bdcc6020055.

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Virtual reality is increasingly used for tasks such as work and education. Thus, rendering scenarios that do not interfere with such goals and deplete user experience are becoming progressively more relevant. We present a physiologically adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We investigated the usability of the adaptive system in a simulated social virtual reality scenario. Participants completed an n-back task (primary) and a visual detection (secondary) task. Here, we adapted the visual complexity of the secondary task in the form of the number of non-player characters of the secondary task to accomplish the primary task. We show that an adaptive virtual reality can improve users’ comfort by adapting to physiological arousal regarding the task complexity. Our findings suggest that physiologically adaptive virtual reality systems can improve users’ experience in a wide range of scenarios.

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Zabielski, Michał, Zbigniew Tarapata, and Rafał Kasprzyk. "Adaptive method of similarity detection of user profiles on online social networks." Bulletin of the Military University of Technology 68, no. 2 (June 28, 2019): 43–57. http://dx.doi.org/10.5604/01.3001.0013.3002.

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The paper presents a method, based on graph and network theory, which allows to detect cloned user profiles on Online Social Networks. Moreover, an idea of similarity containers, which gives an opportunity to incorporate importance and context of data into a model, was introduced. The presented solutions were adapted to the idea of simulation environment, which will allow to detect a profile cloning process before that activity will be completely performed by an attacker. Keywords: Online Social Networks, user profile cloning, violation of privacy on the web.

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Priyatno, Arif Mudi. "SPAMMER DETECTION BASED ON ACCOUNT, TWEET, AND COMMUNITY ACTIVITY ON TWITTER." Jurnal Ilmu Komputer dan Informasi 13, no. 2 (July 1, 2020): 97–107. http://dx.doi.org/10.21609/jiki.v13i2.871.

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Spammers are the activities of users who abuse Twitter to spread spam. Spammers imitate legitimate user behavior patterns to avoid being detected by spam detectors. Spammers create lots of fake accounts and collaborate with each other to form communities. The collaboration makes it difficult to detect spammers' accounts. This research proposed the development of feature extraction based on hashtags and community activities for the detection of spammer accounts on Twitter. Hashtags are used by spammers to increase popularity. Community activities are used as features for the detection of spammers so as to give weight to the activities of spammers contained in a community. The experimental result shows that the proposed method got the best performance in accuracy, recall, precision and g-means with are 90.55%, 88.04%, 3.18%, and 16.74%, respectively. The accuracy and g-mean of the proposed method can surpassed previous method with 4.23% and 14.43%. This shows that the proposed method can overcome the problem of detecting spammer on Twitter with better performance compared to state of the art.

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Kamble, Kunal, Pranit Jadhav, Atharva Shanware, and Pallavi Chitte. "Smart Surveillance System for Anomaly Recognition." ITM Web of Conferences 44 (2022): 02003. http://dx.doi.org/10.1051/itmconf/20224402003.

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Situation awareness is the key to security. Surveillance systems are installed in all places where security is very important. Manually observing all the surveillance footage captured is a monotonous and time consuming task. Security can be defined in different terms in different conditions like violence detection, theft identification, detecting harmful activities etc. In crowded public places the term security covers almost all type of unusual events. To eliminate the tedious manual surveillance we have developed a smart surveillance which will detect an anomaly and alert the user and authority without any human interference. It is a very critical issue in a smart surveillance system to instantly detect an anomalous behaviour in video surveillance system. In this project, a unified framework based on deep neural network framework is proposed to detect anomalous activities. This neural network framework consists of (a) an object detection module, (b) an object discriminator and tracking module, (c) an anomalous activity detection module based on recurrent neural network. The system is a web application where user can apply for three different security services namely motion detection, fall detection and anomaly detection which is applicable for monitoring different environment like homes, roads, offices, schools, shops, etc. On detection of anomalous activity the system will notify the user and responsible authority regarding the anomaly through mail with an anomaly detected frame attachment.

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S, Shalini. "Behavioral Based Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3556–61. http://dx.doi.org/10.22214/ijraset.2021.37134.

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Credit card fraud is a significant threat in the BFSI sector. This credit card fraud detection system analyzes user behavioral patterns and their location to identify any unusual patterns. This consists of user characteristics, which includes user spending styles as well as standard user geographic places to verify his identity. One of the user behavior patterns includes spending habits, usage patterns, etc. This system deals with user credit card data for various characteristics, which includes user country, usual spending procedures. Based upon previous transactions information of that person, the system recognizes unusual patterns in the payment method. The fraud detection system contains the past transaction data of each user. Based on this data, it identifies the standard user behavior patterns for individual users, and any deviation from those normal user patterns becomes a trigger for the detection system. If it detects any unusual patterns, then user will be required to undergo the security verification, which identifies the original user using QR code recognition system. In case of any unusual activity, the system not only raises alerts but it will block the user after three invalid attempts.

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Zhang, Wei, Jiahui Li, Xiujun Zhang, and Shidong Zhou. "A Joint User Activity Detection and Channel Estimation Scheme for Packet-Asynchronous Grant-Free Access." IEEE Wireless Communications Letters 11, no. 2 (February 2022): 338–42. http://dx.doi.org/10.1109/lwc.2021.3127680.

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Kim, Youngho, Tae Oh, and Jeongnyeo Kim. "Analyzing User Awareness of Privacy Data Leak in Mobile Applications." Mobile Information Systems 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/369489.

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To overcome the resource and computing power limitation of mobile devices in Internet of Things (IoT) era, a cloud computing provides an effective platform without human intervention to build a resource-oriented security solution. However, existing malware detection methods are constrained by a vague situation of information leaks. The main goal of this paper is to measure a degree of hiding intention for the mobile application (app) to keep its leaking activity invisible to the user. For real-world application test, we target Android applications, which unleash user privacy data. With the TaintDroid-ported emulator, we make experiments about the timing distance between user events and privacy leaks. Our experiments with Android apps downloaded from the Google Play show that most of leak cases are driven by user explicit events or implicit user involvement which make the user aware of the leakage. Those findings can assist a malware detection system in reducing the rate of false positive by considering malicious intentions. From the experiment, we understand better about app’s internal operations as well. As a case study, we also presents a cloud-based dynamic analysis framework to perform a traffic monitor.

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Trisno, Syahrul Nugraha, Achmad Ubaidillah, and Kunto Aji Wibisono. "SMART TROLLY DESIGN BASED ON MARKER DETECTION." MULTITEK INDONESIA 15, no. 1 (October 4, 2021): 43–53. http://dx.doi.org/10.24269/mtkind.v15i1.2429.

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In this modern era, robots are very instrumental in helping the human work and even replace their work. From the various human works, humans will need a trolley to carry goods. As in supermarkets, people will need a trolley to make it easier to carry groceries and other luggage. But with a trolley that is commonly used, users must encourage the trolley to move so that it reduces hand activity to do other activities. Therefore, we need a trolley that can move to follow the user automatically, so the user no longer needs to push the trolley, and the user's hand can be more free to do other activities. This research discusses about trolley robot using marker detection. With the application of this method will help the robot determine the object so as to facilitate the movement of the robot to follow the user. In the testing process carried out in the study, the system accuracy in detecting markers on objects was 70%.

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Zheng, Panpan, Shuhan Yuan, and Xintao Wu. "SAFE: A Neural Survival Analysis Model for Fraud Early Detection." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1278–85. http://dx.doi.org/10.1609/aaai.v33i01.33011278.

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Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-theart fraud early detection approaches.

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Natadimadja, Muhammad Rayhan, Maman Abdurohman, and Hilal Hudan Nuha. "A Survey on Phishing Website Detection Using Hadoop." Jurnal Informatika Universitas Pamulang 5, no. 3 (September 30, 2020): 237. http://dx.doi.org/10.32493/informatika.v5i3.6672.

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Phishing is an activity carried out by phishers with the aim of stealing personal data of internet users such as user IDs, password, and banking account, that data will be used for their personal interests. Average internet user will be easily trapped by phishers due to the similarity of the websites they visit to the original websites. Because there are several attributes that must be considered, most of internet user finds it difficult to distinguish between an authentic website or not. There are many ways to detecting a phishing website, but the existing phishing website detection system is too time-consuming and very dependent on the database it has. In this research, the focus of Hadoop MapReduce is to quickly retrieve some of the attributes of a phishing website that has an important role in identifying a phishing website, and then informing to users whether the website is a phishing website or not.

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Ando, Bruno, Salvatore Baglio, Salvatore Castorina, Ruben Crispino, and Vincenzo Marletta. "An Assistive Technology Solution for User Activity Monitoring Exploiting Passive RFID." Sensors 20, no. 17 (September 1, 2020): 4954. http://dx.doi.org/10.3390/s20174954.

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Population ageing is having a direct influence on serious health issues, including hampered mobility and physical decline. Good habits in performing physical activities, in addition to eating and drinking, are essential to improve the life quality of the elderly population. Technological solutions, aiming at increasing awareness or providing reminders to eat/drink regularly, can have a significant impact in this scenario. These solutions enable the possibility to constantly monitor deviations from users’ normal behavior, thus allowing reminders to be provided to users/caregivers. In this context, this paper presents a radio-frequency identification (RFID) system to monitor user’s habits, such as the use of food, beverages, and/or drugs. The device was optimized to fulfill specifications imposed by the addressed application. The approach could be extended for the monitoring of home appliances, environment exploitation, and activity rate. Advantages of the approach compared to other solutions, e.g., based on cameras, are related to the low level of invasiveness and flexibility of the adopted technology. A major contribution of this paper is related to the wide investigation of system behavior, which is aimed to define the optimal working conditions of the system, with regards to the power budget, user (antenna)-tag reading range, and the optimal inter-tag distance. To investigate the performance of the system in tag detection, experiments were performed in a scenario replicating a home environment. To achieve this aim, specificity and sensitivity indexes were computed to provide an objective evaluation of the system performance. For the case considered, if proper conditions are meet, a specificity value of 0.9 and a sensitivity value of 1 were estimated.

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Sharanya S, Sridhar PA, Suresh MP, Poorana Mary Monisha W, and Tharadevi R. "Development of Graphical User Interface to Classify Cardiac Abnormalities using ECG Signal." International Journal of Research in Pharmaceutical Sciences 10, no. 3 (July 12, 2019): 1621–25. http://dx.doi.org/10.26452/ijrps.v10i3.1326.

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Analysis of Electrocardiogram (ECG) signal can lead to better detection of cardiac arrhythmia. The important steps involved in the ECG signal analysis include acquisition of data, pre-processing of signal to remove artefacts, feature extraction of attributes and finally identifying abnormalities. This work proposes an efficient implementation of the R-R interval-based ECG classification technique for detecting abnormalities in heart functioning. ECG signals from an online database (PhysioNet.org) was analysed after noise removal for R-R interval, as R peak has the maximum prominent amplitude in ECG wave. Deviation in the R-R interval values obtained from unhealthy was observed and compared with healthy subjects. This observation of cardiac activity can be visualised in our developed Graphical User Interface (GUI). The GUI platform requires only the input of the ECG signal that is to be analysed for abnormalities, which can provide the clinician with the result of cardiac abnormality classification and can help in diagnosis.

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Roy, Avirup, Hrishikesh Dutta, Amit Kumar Bhuyan, and Subir Biswas. "On-Device Semi-Supervised Activity Detection: A New Privacy-Aware Personalized Health Monitoring Approach." Sensors 24, no. 14 (July 9, 2024): 4444. http://dx.doi.org/10.3390/s24144444.

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This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The clinical objective is to monitor and detect the unhealthy sedentary lifestyle of a user. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit sensors installed as wearable devices. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based and distance-based strategies, are employed to achieve the desired classification performance. The proposed system is shown to be highly accurate and computationally efficient for different algorithmic parameters, which is relevant in the context of limited computing resources on typical wearable devices. Extensive experimentation and simulation study have been conducted on multi-user human activity data from the public domain in order to analyze the trade-off between classification accuracy and computation complexity of the proposed learning paradigm with different algorithmic hyper-parameters. With 4.17 h of training time for 8000 activity episodes, the proposed SSL approach consumes at most 20 KB of CPU memory space, while providing a maximum accuracy of 90% and 100% classification rates.

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Eerdekens, Anniek, Margot Deruyck, Jaron Fontaine, Bert Damiaans, Luc Martens, Eli De Poorter, Jan Govaere, David Plets, and Wout Joseph. "Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data." Animals 11, no. 10 (October 7, 2021): 2904. http://dx.doi.org/10.3390/ani11102904.

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Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively.

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Chen, Shuo, Haojie Li, Lanjie Zhang, Mingyu Zhou, and Xuehua Li. "Block Sparse Bayesian Learning Based Joint User Activity Detection and Channel Estimation in Grant-Free MIMO-NOMA." Drones 7, no. 1 (December 31, 2022): 27. http://dx.doi.org/10.3390/drones7010027.

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In the massive machine type of communication (mMTC), grant-free non-orthogonal multiple access (NOMA) is receiving more and more attention because it can skip the complex grant process to allocate non-orthogonal resources to serve more users. To address the limited wireless resources and substantial connection challenges, combining grant-free NOMA and multiple-input multiple-output (MIMO) is crucial to further improve the system’s capacity. In the grant-free MIMO-NOMA system, the base station should obtain the relevant information of the user before data detection. Thus, user activity detection (UAD) and channel estimation (CE) are two problems that should be solved urgently. In this paper, we fully consider the sparse characteristics of signals and the spatial correlation between multiple antennas in the grant-free MIMO-NOMA system. Then, we propose a spatial correlation block sparse Bayesian learning (SC-BSBL) algorithm to address the joint UAD and CE problems. First, by fully mining the block sparsity of signals in the grant-free MIMO-NOMA system, we model the joint UAD and CE problem as a three-dimensional block sparse signal recovery problem. Second, we derive the cost function based on the hierarchical Bayesian theory and spatial correlation. Finally, to estimate the channel and the set of active users, we optimize the cost function with fast marginal likelihood maximization. The simulation results indicate that, compared with the existing algorithms, SC-BSBL can always fully use the signal sparsity and spatial correlation to accurately complete UAD and CE under various user activation probabilities, SNRs, and the number of antennas. The normalized mean square error of CE can be reduced to 0.01, and the UAD error rate can be less than 10−5.

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Ramirez, Heilym, Sergio A. Velastin, Paulo Aguayo, Ernesto Fabregas, and Gonzalo Farias. "Human Activity Recognition by Sequences of Skeleton Features." Sensors 22, no. 11 (May 25, 2022): 3991. http://dx.doi.org/10.3390/s22113991.

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In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person’s body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people’s activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset.

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Et.al, D. Priyadarshini. "A Novel Technique for IDS in Distributed Data Environment Using Merkel Based Security Mechanism for Secure User Allocation." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 4284–97. http://dx.doi.org/10.17762/turcomat.v12i3.1720.

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Multiple corporations and people frequently launching their data in the cloud environment. With the huge growth of data mining and the cloud storage paradigm without checking protection policies and procedures that can pose a great risk to their sector. The data backup in the cloud storage would not only be problematic for the cloud user but also the Cloud Service Provider (CSP). The unencrypted handling of confidential data is likely to make access simpler for unauthorized individuals and also by the CSP. Normal encryption algorithms need more primitive computing, space and costs for storage. It is also of utmost importance to secure cloud data with limited measurement and storage capacity. Till now, different methods and frameworks to maintain a degree of protection that meets the requirements of modern life have been created. Within those systems, Intrusion Detection Systems (IDS) appear to find suspicious actions or events which are vulnerable to a system's proper activity. Today, because of the intermittent rise in network traffic, the IDS face problems for detecting attacks in broad streams of links. In existing the Two-Stage Ensemble Classifier for IDS (TSE-IDS) had been implemented. For detecting trends on big data, the irrelevant data characteristics appear to decrease both the velocity of attack detection and accuracy. The computing resource available for training and testing of the IDS models is also increased. We have put forward a novel strategy in this research paper to the above issues to improve the balance of the server load effectively with protected user allocation to a server, and thereby minimize resource complexity on the cloud data storage device, by integrating the Authentication based User-Allocation with Merkle based Hashing-Tree (AUA-MHT) technique. Through this, the authentication attack and flood attack are detected and restrict unauthorized users. By this proposed model the cloud server verifies, by resolving such attacks, that only approved users are accessing the cloud info. The proposed framework AUA-MHT performs better than the existing model TSE-IDS for parameters such as User Allocation Rate, Intrusion Detection Rate and Space Complexity

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Thorat, Sankalp, Riyesh Rahate, Das Paramjeet Singh, and Ganesh Shinde. "Keylogger Deployment and Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 4557–60. http://dx.doi.org/10.22214/ijraset.2023.51006.

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Abstract: T Keylogger is software used to record keystrokes of a user. It is a very important tool of surveillance as it records the activity of a user. Keyloggers can be used for constructive as well as destructive and malicious purposes. In this project we intend to make a keylogger which can be used as a means of control and surveillance by employers and parents. We also intend to create a program which can be used to detect keyloggers embedded in our system without our permission. Passwords are the need of the hour and so is its protection. Keystrokes monitoring by using keylogger is an advanced way to steal passwords and valuable data and more sophisticated methods of doing this are cropping up. Keyloggers provide a middle ground for surveillance as it is neither too invasive, nor too lax. In this project we make a case for a better use of keyloggers and enlightening people about the various uses and dangers of keyloggers. Keylogger detection is an important part of cybersecurity, given how easy it is to code a keylogger and easy to deploy, keylogger detectors should be of high importance and must be included in all malware detection software. In this project we explore the different types of keyloggers, their effectiveness, and their shortcomings and base our opinion on these facts.

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Yakovyna, Vitalii, and Bohdan Uhrynovskyi. "User-perceived Response Metrics in Android OS for Software Aging Detection." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 9 (June 10, 2021): 32–43. http://dx.doi.org/10.23939/sisn2021.09.032.

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Mobile systems and devices including Android are vulnerable to the effects of software aging which are manifested in performance degradation during long run-time. It is important to identify efficient system and user interface metrics for detecting and counteracting the software aging effects. The aging metrics used in researches of the Android operating system do not take into account the aging processes in user applications. Therefore, this paper discusses two new graphical user interface metrics that allow to track performance degradation and user applications response time: Frame Draw Time and Janky Frames (dropped or delayed frames). Test framework was implemented to perform stress testing of mobile applications in the Android operating system, to collect system state data during stress test performing and to map obtained raw data into time series. Calculated time series are used for further analysis and study of system and graphical user interface metrics. The considered metrics have been compared to the previously used Android Activity Launch Time metric and RAM usage metrics. Practical results have shown that Frame Draw Time and Janks Frames metrics provide data, which can be useful in most scenarios of mobile application using. Therefore, it is proposed to use the two new metrics in combination with other previously used metrics to detect aging trends in the system state and to study the phenomenon of software aging in general. It is noted that the Frame Draw Time metric value can be mapped to states with determined thresholds for transition between these states. These states and thresholds provide the possibility of developing mathematical models based on Markov chains or forecasting the time to aging-failure using regression methods. The need of further study of the correlations between Frame Draw Time metric, Janky Frames metric and metrics of memory usage by different system processes has been identified. Thus, the expediency of using the proposed metrics in future studies of the aging phenomenon in the Android operating system is substantiated, in particular, the effectiveness of the proposed metrics could be checked for different mobile use cases and for different types of mobile applications.

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Journal articles on the topic 'User activity detection'

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