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Journal articles on the topic 'Ad fraud detection'
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Akash Vir and Dr. Shivam Upadhyay. "Smart Guard: A Comprehensive Approach to Ad Click Fraud Detection." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2018–24. https://doi.org/10.32628/cseit2410612403.
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Digital advertising is plagued by ad click fraud, which can result in large financial losses and skewed statistics. Current research on ad click fraud detection is summarized in this systematic review, which also assesses different methods and approaches. The review addresses difficulties, outlines the efficacy of various approaches, and makes recommendations for future lines of inquiry. Ad click fraud is the practice of creating phony clicks on internet ads, which can be done by malevolent humans, click farms, or automated bots. Click metrics are inflated by this fake activity, resulting in false performance statistics and squandered advertising budgets. Ad click fraud must be identified and stopped in order to preserve the efficacy and integrity of digital advertising campaigns. The objectives of this review are to list popular methods for detecting ad click fraud, assess how well they work in practical situations, talk about the drawbacks and restrictions of the approaches currently in use, and recommend future lines of inquiry to improve ad click fraud detection. The analysis concluded that when it comes to identifying ad click fraud, machine learning and artificial intelligence (AI) algorithms typically perform better than rule-based approaches. However, the caliber and variety of the training data determine how effective these methods are. The results emphasize how crucial it is to fight ad click fraud by utilizing sophisticated detection methods. To increase detection accuracy and lower financial losses, advertisers should spend money on AI and machine learning-based solutions. To keep up with changing fraud strategies, future research should concentrate on hybrid techniques, real-time detection, and cross-platform analysis.
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Almeida, Paulo S., and João J. C. Gondim. "Click Fraud Detection and Prevention System for Ad Networks." Journal of Information Security and Cryptography (Enigma) 5, no. 1 (2019): 27. http://dx.doi.org/10.17648/jisc.v5i1.71.
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Click fraud detection consists of identifying the intention behind received clicks, given only technical data and context information. Reviewing concepts involved in click fraud practices and related work, a system that detects and prevents this type of fraud is proposed and implemented. The system is based and implemented on an ad network, one of the 3 main agents in the online ad environment, and for its validation, 3 servers were used, representing the publisher, the ad network with the system implemented and the announcer, and a bot that attempts to commit a click fraud.
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Neeraja, Anupam, Sriram, Subhani Shaik, and V. Kakulapati. "Fraud Detection of AD Clicks Using Machine Learning Techniques." Journal of Scientific Research and Reports 29, no. 7 (2023): 84–89. http://dx.doi.org/10.9734/jsrr/2023/v29i71762.
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Although all businesses face the possibility of fraud, those that rely on internet advertising face an especially high risk of click fraud, which may lead to inaccurate click statistics and unnecessary expenditures. The cost per click for advertising channels might skyrocket if enough people click on the ads. Internet advertising is becoming a significant revenue source for many websites. Under this model, advertisers pay the publisher a flat rate for each click-through from the ad to the advertiser's site. Since spending much on Internet advertising requires significant resources, the term "click fraud" refers to an attack tactic in which the perpetrator repeatedly clicks on a single link for the sole purpose of generating illicit revenue. By clicking on a pay-per-click (PPC) ad many times using a script, fraudsters may trick online advertisers into paying for clicks that never happened. We may use a variety of methods to identify click fraud anytime a human or computer program clicks on a particular link, and then use the click-through rate to ascertain whether the clicker is legitimate. This work provides a machine-learning strategy for predicting user click fraud, which will enable us to distinguish between fraudulent and legitimate clicks and, therefore, identify fraudulent users from legitimate ones. We have used KNN, SVC, and Random Forest models for this purpose.
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Bathula, Mahesh, Rama Chaithanya Tanguturi, and Srinivasa Rao Madala. "Click Fraud Detection Approaches to analyze the Ad Clicks Performed by Malicious Code." Journal of Physics: Conference Series 2089, no. 1 (2021): 012077. http://dx.doi.org/10.1088/1742-6596/2089/1/012077.
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Abstract Mobile PR is an important component of the mobile app ecosystem. A major threat to this ecosystem’s long-term health is click fraud, which involves clicking on ads while infected with malware or using an automated bot to do it for you. The methods used to identify click fraud now focus on looking at server requests. Although these methods have the potential to produce huge numbers of false negatives, they may easily be avoided if clicks are hidden behind proxies or distributed globally. AdSherlock is a customer-side (inside the app) efficient and deployable click fraud detection system for mobile applications that we provide in this work. AdSherlock separates the computationally expensive click request identification procedures into an offline and online approach. AdSherlock uses URL (Uniform Resource Locator) tokenization in the Offline phase to create accurate and probabilistic patterns. These models are used to identify click requests online, and an ad request tree model is used to detect click fraud after that. In order to develop and evaluate the AdSherlock prototype, we utilise actual applications. It injects the online detector directly into an executable software package using binary instrumentation technology (BIT). The findings show that AdSherlock outperforms current state-of-the-art methods for detecting click fraud with little false positives. Advertisement requests identification, mobile advertising fraud detection are some of the keywords used in this article.
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Alzahrani, Reem A., and Malak Aljabri. "AI-based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions." Journal of Sensor and Actuator Networks 12, no. 1 (2022): 4. http://dx.doi.org/10.3390/jsan12010004.
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Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements are artificially inflated in click fraud. Typical pay-per-click advertisements charge a fee for each click, assuming that a potential customer was drawn to the ad. Click fraud attackers create the illusion that a significant number of possible customers have clicked on an advertiser’s link by an automated script, a computer program, or a human. Nevertheless, advertisers are unlikely to profit from these clicks. Fraudulent clicks may be involved to boost the revenues of an ad hosting site or to spoil an advertiser’s budget. Several notable attempts to detect and prevent this form of fraud have been undertaken. This study examined all methods developed and published in the previous 10 years that primarily used artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for the detection and prevention of click fraud. Features that served as input to train models for classifying ad clicks as benign or fraudulent, as well as those that were deemed obvious and with critical evidence of click fraud, were identified, and investigated. Corresponding insights and recommendations regarding click fraud detection using AI approaches were provided.
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Sadeghpour, Shadi, and Natalija Vlajic. "Ads and Fraud: A Comprehensive Survey of Fraud in Online Advertising." Journal of Cybersecurity and Privacy 1, no. 4 (2021): 804–32. http://dx.doi.org/10.3390/jcp1040039.
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Over the last two decades, we have witnessed a fundamental transformation of the advertising industry, which has been steadily moving away from the traditional advertising mediums, such as television or direct marketing, towards digital-centric and internet-based platforms. Unfortunately, due to its large-scale adoption and significant revenue potential, digital advertising has become a very attractive and frequent target for numerous cybercriminal groups. The goal of this study is to provide a consolidated view of different categories of threats in the online advertising ecosystems. We begin by introducing the main elements of an online ad platform and its different architecture and revenue models. We then review different categories of ad fraud and present a taxonomy of known attacks on an online advertising system. Finally, we provide a comprehensive overview of methods and techniques for the detection and prevention of fraudulent practices within those system—both from the scientific as well as the industry perspective. The main novelty of our work lies in the development of an innovative taxonomy of different types of digital advertising fraud based on their actual executors and victims. We have placed different advertising fraud scenarios into real-world context and provided illustrative examples thereby offering an important practical perspective that is very much missing in the current literature.
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Gabryel, Marcin, Magdalena M. Scherer, Łukasz Sułkowski, and Robertas Damaševičius. "Decision Making Support System for Managing Advertisers By Ad Fraud Detection." Journal of Artificial Intelligence and Soft Computing Research 11, no. 4 (2021): 331–39. http://dx.doi.org/10.2478/jaiscr-2021-0020.
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Abstract Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and bot-origin ones. We use feedforward neural networks with embedding layers. Moreover, we use one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores and the data of lead classification from three financial institutions. The data are collected by a JavaScript code embedded into HTML pages. The three proposed models achieved relatively high accuracy in detecting artificially generated traffic.
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Sadeghpour, Shadi, and Natalija Vlajic. "Click Fraud in Digital Advertising: A Comprehensive Survey." Computers 10, no. 12 (2021): 164. http://dx.doi.org/10.3390/computers10120164.
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Recent research has revealed an alarming prevalence of click fraud in online advertising systems. In this article, we present a comprehensive study on the usage and impact of bots in performing click fraud in the realm of digital advertising. Specifically, we first provide an in-depth investigation of different known categories of Web-bots along with their malicious activities and associated threats. We then ask a series of questions to distinguish between the important behavioral characteristics of bots versus humans in conducting click fraud within modern-day ad platforms. Subsequently, we provide an overview of the current detection and threat mitigation strategies pertaining to click fraud as discussed in the literature, and we categorize the surveyed techniques based on which specific actors within a digital advertising system are most likely to deploy them. We also offer insights into some of the best-known real-world click bots and their respective ad fraud campaigns observed to date. According to our knowledge, this paper is the most comprehensive research study of its kind, as it examines the problem of click fraud both from a theoretical as well as practical perspective.
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Lyu, Qiuyun, Hao Li, Renjie Zhou, Jilin Zhang, Nailiang Zhao, and Yan Liu. "BCFDPS: A Blockchain-Based Click Fraud Detection and Prevention Scheme for Online Advertising." Security and Communication Networks 2022 (April 29, 2022): 1–20. http://dx.doi.org/10.1155/2022/3043489.
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Online advertising, which depends on consumers’ click, creates revenue for media sites, publishers, and advertisers. However, click fraud by criminals, i.e., the ad is clicked either by malicious machines or hiring people, threatens this advertising system. To solve the problem, many schemes are proposed which are mainly based on machine learning or statistical analysis. Although these schemes mitigate the problem of click fraud, several problems still exist. For example, some fraudulent clicks are still in the wild since their schemes only discover the fraudulent clicks with a probability approaching but not 100%. Also, the process of detecting a click fraud is executed by a single publisher, which makes a chance for the publisher to obtain illegal income by deceiving advertisers and media sites. Besides, the identity privacy of consumers is also exposed because the schemes deal with the plain text of consumers’ real identity. Therefore, in this paper, a blockchain-based click fraud detection and prevention scheme (BCFDPS) for online advertising is proposed to deal with the above problems. Specifically, the BCFDPS mainly introduces bilinear pairing to implicitly verify whether a consumer’s real digital identity is contained in a click message to significantly avoid click fraud and employs a consortium blockchain to ensure the transparency of the detection and prevention process. In our scheme, the clicks by machines or fraud ones by a human can be accurately detected and prevented by media sites, publishers, and advertisers. Furthermore, ciphertext-policy attribute-based encryption is adopted to protect the identity privacy of consumers. The implementation and evaluation results show that compared with the existing click fraud detection and prevention schemes based on machine learning and statistical analysis, BCFDPS achieves detection of each fraudulent click with a probability of 100% and consumes lower computation cost; furthermore, BCFDPS adds functions of consumers’ privacy protection and click fraud detection and prevention, compared to the existing blockchain-based online advertising scheme, by introducing limited communication cost ( 4,984 bytes) at lower storage cost.
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Keserwani, Pankaj Kumar, Mahesh Chandra Govil, and Emmanuel Shubhakar Pilli. "The web ad-click fraud detection approach for supporting to the online advertising system." International Journal of Swarm Intelligence 7, no. 1 (2022): 3. http://dx.doi.org/10.1504/ijsi.2022.121091.
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Keserwani, Pankaj Kumar, Mahesh Chandra Govil, and Emmanuel Shubhakar Pilli. "The web ad-click fraud detection approach for supporting to the online advertising system." International Journal of Swarm Intelligence 7, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijsi.2022.10039450.
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Iroshan, Aberathne, and Walgampaya Chamila. "Real Time Mobile Ad Investigator: An Effective and Novel Approach for Mobile Click Fraud Detection." Computing and Informatics 40, no. 3 (2021): 606–27. http://dx.doi.org/10.31577/cai_2021_3_606.
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Researcher. "THE AI REVOLUTION IN AD MEASUREMENT IMPLICATIONS FOR BIG TECH AND ADVERTISERS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1017–25. https://doi.org/10.5281/zenodo.14066629.
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This article explores the transformative impact of artificial intelligence (AI) on ad measurement in the digital advertising landscape. It examines the shift towards AI-powered solutions driven by the need for greater efficiency and personalization in campaign optimization and analysis. The paper delves into the advanced capabilities of AI algorithms in processing massive datasets, enhancing ad targeting, optimizing bidding strategies, and improving performance tracking. Key benefits of AI-powered ad measurement are discussed, including real-time campaign adjustments, advanced fraud detection, and improved return on investment for advertisers. The article addresses how AI tackles traditional challenges such as cross-device attribution and provides more granular measurement solutions. Through case studies of major tech companies and comparative analyses, the superiority of AI methods over traditional approaches is demonstrated. Ethical considerations and privacy concerns surrounding data collection and AI transparency are critically examined. The paper concludes by exploring future trends in AI ad measurement technology and its potential impact on the advertising industry, highlighting the need for balancing technological advancement with ethical considerations and privacy protection. This comprehensive review provides insights into the current state and future directions of AI in advertising measurement, offering valuable perspectives for marketers, technologists, and researchers in the field.
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Raghuwanshi, Ashish. "Ensuring Brand Safety and Reputation in Digital Marketing with Advanced Cybersecurity Protocols." Financial Technology and Innovation 3, no. 1 (2023): 08–17. http://dx.doi.org/10.54216/fintech-i.030101.
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The ever-changing world of digital marketing makes it more important than ever to protect the integrity of brands. This study presents a novel method called "Enhanced Brand Safety Assurance through Cybersecurity Protocols" that combines three important algorithms: Ad Fraud Detection and Prevention, Real-time Behavioral Analysis, and Threat Intelligence Integration. The security of digital advertising, privacy of sensitive information, and customer confidence may all be assured with this framework's proactive threat detection and mitigation capabilities. A strong protection system against ever-changing cyber threats is created by combining the unique characteristics of each algorithm. To react to the constantly changing cybersecurity scene, the suggested solution uses adaptive thresholds, machine learning, and sophisticated analytics. When compared to more conventional approaches, the suggested solution outperforms them in terms of important efficacy indicators and practical implementation details. Experiments show that it can learn a lot, integrate AI, adapt to threats, monitor in real-time, and identify threats very well. Brands can protect themselves from the complex digital threat environment with this comprehensive and proactive cybersecurity solution that tackles the many problems associated with digital marketing.
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Das, Shubhomoy, Md Rakibul Islam, Nitthilan Kannappan Jayakodi, and Janardhan Rao Doppa. "Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning." Journal of Artificial Intelligence Research 80 (May 23, 2024): 127–70. http://dx.doi.org/10.1613/jair.1.14741.
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Anomaly detection (AD) task corresponds to identifying the true anomalies among a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies. The ranked list of anomalies is then analyzed by a human to discover the true anomalies. Ensemble of tree-based anomaly detectors trained in an unsupervised manner and scoring based on uniform weights for ensembles are shown to work well in practice. However, the manual process of analysis can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensembles based on label feedback allows us to quickly discover true anomalies. This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also show empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.
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Zhu, Tong, Zhen Huang, Lu Zhou, Guoxing Chen, Yan Meng, and Haojin Zhu. "Collaborative Ad Fraud Detection in Ad Networks." IEEE Transactions on Networking, 2025, 1–16. https://doi.org/10.1109/ton.2025.3547977.
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"Detection of Fraud in Mobile Advertising using Machine Learning." International Journal of Innovative Technology and Exploring Engineering 9, no. 6 (2020): 855–58. http://dx.doi.org/10.35940/ijitee.f4002.049620.
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With ongoing advancements in the field of technology, mobile advertising has emerged as a platform for publishers to earn profit from their free applications. An online attack commonly known as click fraud or ad fraud has added up to the issue of concerns surfacing mobile advertising. Click fraud is the act of generating illegitimate clicks or data events in order to earn illegal income. Generally, click frauds are generated by infusing the genuine code with some illegitimate bot, which clicks on the ad acting as a potential customer. These click frauds are usually planted by the advertisers or the advertising company so that the number of clicks on the ad increases which will give them the ability to charge the publishers with a hefty sum per number of clicks. A number of studies have determined the risks that click fraud poses to mobile advertising and a few solutions have been proposed to detect click frauds. The solution proposed in this paper comprises of a social network analysis model – to detect and categorize fraudulent clicks and then test sample datasets. This social network analysis model takes into consideration a wide range of parameters from a large group of users. A detailed study is conducted for analyzing these parameters in order to separate the parameters, which affect the click fraud generation process largely. These parameters are then tested and categorized into sample datasets. The mobile advertising industry forms a large part of the revenue generated by the advertising industry. Hence, detection of click fraud in mobile advertising is important to ensure that no illegitimate sources are used to generate this revenue. To be precise, the proposed method touches an accuracy of about 92%.
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Kesavan, Dhanvanth. "Advertisement Click Fraud Detection." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, May 20, 2021, 364–69. http://dx.doi.org/10.32628/cseit217375.
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Mobile ads are tormented with deceitful clicks that may be an important test for the advertising network. Albeit documented promotion systems utilize various methods to acknowledge click fraud, they don’t shield the client from conceivable intrigue among distributors and advertisement systems. What’s additional, promotion systems can’t screen the client’s action for click fraud location once they're entertained to the advertising web content following clicking the ad, i.e., in a web site there will be focuses or some specific spots after we click on the actual spot it'll be entertained to the advertising web site. We propose another publically support-based mostly framework referred to as Click Fraud Crowdsourcing (CFC) that works in conjunction with the two promoters and advertisement organizes therefore as to defend the two gatherings from any conceivable click dishonorable acts. The framework profits by each a worldwide view, wherever it accumulates various promotion demands examining to various advertising organize distributer publicist blends, and a nearby read, wherever it will follow the clients’ commitment in every advertising website.
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Makkineni, Neeraja, Anupam Ciripuram, Sriram N, Shaik Subhani, and V. Kakulapati. "Fraud Detection of AD Clicks Using Machine Learning Techniques." SSRN Electronic Journal, 2023. http://dx.doi.org/10.2139/ssrn.4486834.
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Alzahrani, Reem A., Malak Aljabri, and Rami Mustafa A. Mohammad. "Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms." IEEE Access, 2025, 1. https://doi.org/10.1109/access.2025.3532200.
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Subburayan, Baranidharan, David Winster, K. Dhanalakshmi, and R. Rajkumar. "Combating Evolving Threats: A Systematic Review of Online Ad Fraud Detection." SSRN Electronic Journal, 2025. https://doi.org/10.2139/ssrn.5263103.
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Kumari, Shalini, Chander Prabha, Asif Karim, Md Mehedi Hassan, and Sami Azam. "A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023." IET Information Security 2024, no. 1 (2024). http://dx.doi.org/10.1049/2024/8821891.
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Almost 85% of companies polled said they were looking into anomaly detection (AD) technologies for their industrial image anomalies. The present problem concerns detecting anomalies often occupied by redundant data. It can be either in images or in videos. Finding a correct pattern is a challenging task. AD is crucial for various applications, including network security, fraud detection, predictive maintenance, fault diagnosis, and industrial and healthcare monitoring. Many researchers have proposed numerous methods and worked in the area of AD. Multiple anomalies and considerable intraclass variation make industrial datasets tough. Further, research is needed to create robust, efficient techniques that generalize datasets and detect anomalies in complex industrial images. The outcome of this study focuses on various AD methods from 2019 to 2023. These techniques are categorized further into machine learning (ML), deep learning (DL), and federated learning (FL). It explores AD approaches, datasets, technologies, complexities, and obstacles, emphasizing the requirement for effective detection across domains. It explores the results achieved in various ML, DL, and FL AD methods, which helps researchers explore these techniques further. Future research directions include improving model performance, leveraging multiple validation techniques, optimizing resource utilization, generating high‐quality datasets, and focusing on real‐world applications. The paper addresses the changing environment of AD methods and emphasizes the importance of continuing research and innovation. Each ML and DL AD model has strengths and shortcomings, concentrating on accuracy and performance while applying quality parameters for evaluation. FL provides a collaborative way to improve AD using distributed data sources and data privacy.
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Halim, Fransiscus Ati. "Distribusi Pengiriman File Multimedia Secara RealTime dengan Jaringan WAN Frame Relay." Jurnal ULTIMA Computing 9, no. 1 (2017). http://dx.doi.org/10.31937/sk.v9i1.567.
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Today's communications networks are built using high-speed digital trunks that inherently provide high throughput, minimal relay, and a very low error rate. Such transmission networks supply highly reliable service without the overhead of error control functions. Frame relay is a packet-mode transmission network service that exploits these network characteristics by minimizing the amount of error detection and recovery performed inside the network [1]. In addition, real time network based systems can also minimize the possibility of employees committing fraud resulting in losses for the company. The research was carried out in a service provider video ad impressions company, which has fifteen branches in the islands of Java and Sumatra. The problem is the revenue reporting is not real-time and non-standard video file format ads to each branch. This is because the distribution process with the hard disk media are still using courier services. Based on user demand, it was decided to use the computer network using Frame Relay technology. With the computer network that connects all the branches to the head office, the data updating process can be done in a shorter time than the previous system and will reduce fraud forms of each branch and enable the achievement of a better quality of service to customers.
 Index Terms—Computer Network, Frame Relay
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Aravind Raghu. "Enhancing Financial Transaction Security Using OAuth2, MFA, and Azure AD Authentication: A Java-Based Integrated Approach." International Journal of Computational and Experimental Science and Engineering 11, no. 2 (2025). https://doi.org/10.22399/ijcesen.2068.
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In recent years, financial transactions have been increasingly targeted by cyberattacks, fraud transactions and identity theft. Traditional authentication mechanisms such as basic auth have proven to be insufficient. This paper proposes a multi-layered security framework which integrates three components- Oauth2 token-based authentication, multi-factor authentication (MFA) and Azure Active Directory (AAD) to secure real-time financial transactions. This approach aims to maintain seamless transaction processing along with reducing token compromise rates and prevent unauthorized access. This research paper presents a quantitative approach to evaluate the impact of integrating transaction security, authentication latency and overall performance. A java-based implementation using Springboot and Spring-security has been developed to empirically evaluate the effectiveness of the approach. Using a sample size of 10,000 financial transactions, the integrated Oauth2+MFA+Azure AD approach reduced the token compromise rates from 2.7% to mere 0.4%, which is offset by a latency increase of only 260ms. These findings demonstrate that integrated authentication substantially enhances security at the same time maintains acceptable performance, thus offers a robust foundation for high-throughput and large-scale financial applications. This research lays groundwork for future enhancements into adaptive MFA policies and machine learning based anomaly detection for real-time financial transactions.
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Smirti, Ananya. "DETECTING OF FRAUD CLICK ON ADVERTISEMENT." International Journal of Research in Science and Technology 11, no. 3 (2021). http://dx.doi.org/10.37648/ijrst.v11i03.004.
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Nowadays, advertising plays a major role in boosting a business. And every company has to pay some amount for it. As we know, mobile technology is handy and almost available in everybody's hands. As business owners, many business personals want to boost their business on mobile devices to see the advertisement. But there is a challenge in it. Mobile promotions are abused with bogus snaps that might be a significant test for the publicizing organization. But reported advancement frameworks use different strategies to recognize click deception; they don't safeguard the customer from possible interest among merchants and commercial frameworks. What's extra, advancement frameworks can't evaluate the customer's activity for click theft area whenever they're engaged to the promoting web content after tapping the advertisement, i.e., in a website, there will be focuses or some specific section after we click on the genuine section it'll be locked in to the publicizing website. We propose Click Fraud Crowdsourcing one all the more freely support-based for the most part structure recommended, that works identified with the two sponsors and advancement organizes to safeguard the two social events from any conceivable snap offensive exhibits. The framework paybacks by seeing of each ad
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IJNSA. "International Journal of Network Security & Its Applications (IJNSA)." July 23, 2018. https://doi.org/10.5281/zenodo.1319465.
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<strong>International Journal of Network Security & Its Applications (IJNSA)</strong> <strong> --- UGC Listed, ERA Indexed----</strong> ISSN 0974 - 9330 (Online); 0975 - 2307 (Print) http://airccse.org/journal/ijnsa.html <strong>Scope & Topics</strong> The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas. <strong>Topics of Interest include, but are not limited to, the following:</strong> Network and Wireless Network Security Mobile, Ad Hoc and Sensor Network Security Peer-to-Peer Network Security Database and System Security Intrusion Detection and Prevention Internet Security & Applications Security & Network Management E-mail security, Spam, Phishing, E-mail fraud Virus, worms, Trojan Protection Security threats & countermeasures (DDoS, MiM, Session Hijacking, Replay attack etc,) Ubiquitous Computing Security Web 2.0 security Cryptographic protocols Performance Evaluations of Protocols & Security Application <strong> Paper submission</strong> Authors are invited to submit papers for this journal through e-mail ijnsa@airccse.org Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. <strong>Important Dates</strong> <strong>Submission Deadline :August 04, 2018</strong> Notification : September 04, 2018 Final Manuscript Due : September 12, 2018 Publication Date : Determined by the Editor-in-Chief For other details please visit http://airccse.org/journal/ijnsa.html
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O'Keefe, Patrick P., Suzanne B. Hendrix, Jeffery Zhang, Kent Hendrix, Lixia Wang, and Samuel P. Dickson. "Navigating the Complexities of Alzheimer’s Clinical Trials: The Perils of Small Molecule Studies and the Risk of Fraudulent Practices." Alzheimer's & Dementia 20, S6 (2024). https://doi.org/10.1002/alz.093551.
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AbstractBackgroundAlzheimer’s disease (AD) presents unique challenges in clinical trials involving small molecules. Multifaceted issues plague such trials, emphasizing susceptibility to fraud from clinical sites and “professional patients”. The relative ease of simulating Alzheimer’s diagnosis, coupled with inadequate oversight by Contract Research Organizations (CROs), creates fertile ground for deceptive practices. Poor rater quality and the simplicity of falsifying data exacerbate concerns.A core difficulty is the diagnostic ambiguity of AD, where symptomatic overlap with other cognitive disorders often leads to misdiagnosis or feigned conditions by professional patients. This issue is intensified in small molecule studies, which are easier to replicate or substitute than biologics, increasing the temptation and feasibility of fraudulent activities. Lax monitoring and control by CROs contribute to these vulnerabilities, allowing unnoticed data manipulation and deceitful conduct. It is particularly alarming that sites most prone to deceitful practices include predominantly underrepresented populations, thwarting inclusion efforts.MethodWe critically analyze the inefficacy of regulatory audits in detecting and preventing fraud as clinical sites adeptly prepare regulatory documents to mask misconduct. We evaluate methods for detecting fraudulent practices based on the clinical data and clinical operations metrics.ResultThe absence of medical expertise among auditors limits their ability to discern data integrity issues and the nuances of AD symptomatology. Traditional data management review and statistical analysis methods fail to identify these types of issues.ConclusionWe call for a reevaluation of current methodologies in AD clinical trials. We advocate for enhanced CRO oversight, improved rater training, and incorporation of medical expertise in audits. Targeted, blinded statistical data review can identify problematic sites. Addressing these challenges can safeguard AD research integrity, accelerate development of effective therapeutics, and protect and benefit vulnerable populations.
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"Machine Learning Method for Detecting and Analysis of Fraud Phone Calls Datasets." International Journal of Recent Technology and Engineering 8, no. 6 (2020): 3806–10. http://dx.doi.org/10.35940/ijrte.f8751.038620.
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While using non-stop advancement of correspondences industry, almost all clients steadily appreciate various interchanges companies. To accomplish persuasive and moderate identification with regard to telecom deceit clients, all of us propose an effective and suitable extortion customer discovery method dependent on customer's Call detail Record (CDR). The suggested strategy contains two segments, specific device learning component and file format discovery element. In the equipment wisdom component, a support Vector machine (SVM) computation dependent on aimed knowledge is actually utilized to team clients making use of outline characteristics. Detail evaluation is similarly completed regarding separating the actual detail associated with networks. Outcomes show that these strategies will help rapidly character the ad calls. The actual investigations display that the technique can achieve high reputation precision regarding 97.56%, which exhibit that the proposed technique has progressively brilliant execution in examination with the best in class draws near
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Hendrix, Suzanne B., Patrick P. O'Keefe, Jeffery Zhang, Kent Hendrix, Lixia Wang, and Samuel P. Dickson. "Improving the Chance of Success for Alzheimer’s Clinical Trials by Identifying and Mitigating the Risk of Fraudulent Practices." Alzheimer's & Dementia 20, S8 (2024). https://doi.org/10.1002/alz.095776.
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AbstractBackgroundCompanies recently identified clinical study sites engaging in fraudulent practices, reducing success in Alzheimer’s disease (AD) clinical trials. Successful trials are reliable despite possible victimization by these practices because these sites introduce a negative bias. Ease of simulating Alzheimer’s diagnosis documentation (based on clinical or blood markers) and clinical outcome data, coupled with inadequate oversight by Contract Research Organizations (CROs), facilitates deceptive practices.Fraudulent practices at sites and with patients (“professional patients”) often go hand‐in‐hand and flourished during COVID. These must be aggressively addressed in the post‐COVID environment. Requiring and scrutinizing PET scans increases the chance of detecting fraud. Alarmingly, sites most prone to deceitful practices include predominantly underrepresented populations, thwarting inclusion efforts.MethodWe reviewed completed and ongoing studies to estimate the percentage of potentially fraudulent sites. We developed medical audit methods to identify issues invisible in regulatory audits. Traditional data management and statistical analysis methods fail to identify these issues. We use expected enrollment and true effect size to estimate potential bias and added variability introduced by these sites. We propose methods for detecting fraud based on clinical and operational data.ResultIn studies not requiring PET scans, approximately 10% of sites may be fraudulent, corresponding to a higher proportion of enrolled subjects (∼20‐30%). These sites can reduce observed effect size by 40% (estimated bias) at best and can completely cancel out a true treatment effect at worst. We estimate variability increases by 35‐100%, reducing our ability to observe significance. Algorithms to identify fraudulent sites will be gladly shared confidentially, but not broadly, to prevent sites from circumventing our algorithms.ConclusionAD clinical trials affected by even a minority of fraudulent sites may have underestimated treatment effects. We developed a toolbox for combating these issues. It includes best practices for enhanced CRO oversight, incorporation of medical expertise in audits, and identification of problems based on targeted, blinded statistical algorithms. We freely share it with qualified researchers. Awareness of and taking an aggressive stance as a community against these fraudulent practices can safeguard AD research integrity, protect and benefit vulnerable populations, and accelerate development of effective therapeutics.
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IJNSA. "International Journal of Network Security & Its Applications (IJNSA) - Current Issue - May 2019, Volume 11, Number 3." May 31, 2019. https://doi.org/10.5121/ijnsa.2019.11300.
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International Journal of Network Security & Its Applications (IJNSA) ISSN 0974 - 9330 (Online); 0975 - 2307 (Print) http://airccse.org/journal/ijnsa.html Current Issue - May 2019, Volume 11, Number 3 Enhancing the Wordpress System: from Role to Attribute-Based Access Control Lifeng Cao, Jia Ying Ou and Amirhossein Chinaei, York University, Canada http://aircconline.com/ijnsa/V11N3/11319ijnsa01.pdf Classification Procedures for Intrusion Detection Based on KDD CUP 99 Data Set Shaker El-Sappagh, Ahmed Saad Mohammed and Tarek Ahmed AlSheshtawy, Benha University, Egypt http://aircconline.com/ijnsa/V11N3/11319ijnsa02.pdf Xdoser, A Benchmarking Tool for System Load Measurement Using Denial of Service Features AKM Bahalul Haque, Rabeya Sultana, Mohammad Sajid Fahad , MD Nasif Latif and Md. Amdadul Bari, North South University, Bangladesh http://aircconline.com/ijnsa/V11N3/11319ijnsa03.pdf Multi-Layer Classifier for Minimizing False Intrusion Shaker El-Sappagh, Ahmed saad Mohammed and Tarek Ahmed AlSheshtawy, Benha University, Egypt http://aircconline.com/ijnsa/V11N3/11319ijnsa04.pdf Methods Toward Enhancing RSA Algorithm : A Survey Shaheen Saad Al-Kaabi and Samir Brahim Belhaouari, Hamad Bin Khalifa University (HBKU), Qatar http://aircconline.com/ijnsa/V11N3/11319ijnsa05.pdf Survey on Secure Routing in Vanets Afef Slama1 and Ilhem Lengliz2, 1University of Manouba, Tunisia and 2Military Academy, Tunisia http://aircconline.com/ijnsa/V11N3/11319ijnsa06.pdf A Combination of Temporal Sequence Learning and Data Description for Anomaly - based NIDS Nguyen Thanh Van1,2, Tran Ngoc Thinh1 and Le Thanh Sach1, 1Ho Chi Minh City University of Technology, VietNam and 2Ho Chi Minh City University of Technology and Education, VietNam http://aircconline.com/ijnsa/V11N3/11319ijnsa07.pdf http://airccse.org/journal/jnsa19_current.html
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Network, Security. "International Journal of Network Security & Its Applications (IJNSA)." May 14, 2018. https://doi.org/10.5281/zenodo.1246369.
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The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas
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IJNSA. "The International Journal of Network Security & Its Applications (IJNSA) - ERA, UGC Listed Journal." November 30, 2018. https://doi.org/10.5121/ijnsa.2018.10601.
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A Multi-Layer Hybrid Text Steganography for Secret Communication Using Word Tagging and RGB Color Coding Ali F. Al-Azzawi1, Philadelphia University, Jordan http://aircconline.com/ijnsa/V10N6/10618ijnsa01.pdf Secure Third Party Auditor(TPA) for Ensuring Data Integrity in Fog Computing KashifMunir and Lawan A. Mohammed, University of Hafr Al Batin, KSA http://aircconline.com/ijnsa/V10N6/10618ijnsa02.pdf IOT and Security-Privacy Concerns: A Systematic Mapping Study Moussa WITTI and Dimitri KONSTANTAS, Information Science Institute University of Geneva, Switzerland http://aircconline.com/ijnsa/V10N6/10618ijnsa03.pdf Biometric Smartcard Authentication for Fog Computing Kashif Munir and Lawan A. Mohammed, University of Hafr Al Batin, KSA http://aircconline.com/ijnsa/V10N6/10618ijnsa04.pdf
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IJNSA. "International Journal of Network Security & Its Applications (IJNSA)." September 30, 2018. https://doi.org/10.5281/zenodo.1403993.
Full textAbstract:
The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks.
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AIRCC. "International Journal of Network Security & Its Applications (IJNSA) - ERA, WJCI, H index - Profile." International Journal of Network Security & Its Applications (IJNSA), January 27, 2022, 01–09. https://doi.org/10.5281/zenodo.5910297.
Full textAbstract:
The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
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Michal, Kedziora, Gawin Paulina, Szczepanik Michal, et al. "Current Issue : The International Journal of Network Security & Its Applications (IJNSA)." January 31, 2019. https://doi.org/10.5121/ijnsa.2019.11100.
Full textAbstract:
The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
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Ali, F. Al-Azzawi. "The International Journal of Network Security & Its Applications (IJNSA) -- November 2018 Issue." November 30, 2018. https://doi.org/10.5121/ijnsa.2018.10600.
Full textAbstract:
The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.