Search
34 articles for “Fraudulence”
-
Identifying Fraudulent Data and Fake Account Profiles Analysis in Examination for Providing Security in Online Dating Site
Abstract: In this research, we are focused on a new yet mysterious method of using malicious apps. You want to be software for socialization. Their main goal is to allow users to buy premium / VIP packages and Start talking to (probably false) other app accounts. False dating apps are called such systems. A comprehensive analysis to explain the whole scam dating apps ecosystem. In specific, we suggested a three-stage approach …
Published in Recent Trends in Parallel Computing · Vol. 7, Issue 1, 2020 · pp. 12–17 Read article
-
Fraudulent Data Detection of Credit Card by Using Random Forest Method to Calculate ROC and Confusion Matrix
Abstract: Credit card fraud defaults in trillions of dollars online merchants. Researchers have more and more complex methods of detecting fraud using algorithms for machine learning, but there are rare reports of hands-on implementations. We describe the implementation of an extortion discovery framework in a huge e-tail retailer. The paper looks at the combination of manual and robotized classification and offers experiences into the whole handle of advancement. The paper can …
Published in Journal of Advanced Database Management & Systems · Vol. 7, Issue 1, 2020 · pp. 15–22 Read article
-
Realization of Trust and Reputation Models in Extreme Fraudulent Environment over ODC WSNs
Abstract: During the last years, WSNs and its relevance have obtained considerable momentum. In WSNs, every sensor is deployed in different location for particular object. Due to unstable conditions, sensors are unable to perform an anticipated result. At this stage, we totally loose our security and it shows inaccuracy in result. So, we focused on the mutual trust between the source to sink and vice versa in WSNs. In this paper, …
Published in Recent Trends in Sensor Research & Technology · Vol. 2, Issue 2, 2015 · pp. 6–15 Read article
-
Cyber Security Implementation Through Deep Learning Based Fraudulent URL Detection
Abstract: A malicious URL, sometimes known as Spam, malicious ads, and drive-by vulnerabilities may all be found on a site that is malevolent. To find rogue URLs, you need to be quick. Researchers previously used blacklisting, regular expression searching and signature matching in their investigations. These strategies are worthless for discovering new malicious URLs or updated versions of previously problematic URLs. A machine learning-based approach may be developed to overcome this …
Published in Recent Trends in Programming languages · Vol. 9, Issue 2, 2022 · pp. 35–42 Read article
-
A Secure Approach for Cross-chain Transactions Using Machine Learning Model
Abstract: The ability to conduct transactions or transfer assets between different blockchain networks is referred to as cross-chain transactions. It enables users to transfer assets from one blockchain network to another. In the Cryptocurrency ecosystem, the risk of fraudulent activities has become a significant concern. Due to these fraudulent activities, the cross-chain transactions have encountered challenges in terms of security and integrity. The need for robust fraud detection mechanisms becomes crucial …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 3, 2023 · pp. 17–24 Read article
-
Review on Offline Signature and Online Signature Recognition and Verification
Abstract: AbstractBiometrics is regularly utilized nowadays with the end goal of distinguishing proof and confirmation of the people and their marks on the planet. Transcribed mark of an individual is a novel recognizable proof work of human which are utilized and acknowledged particularly in the banking and other monetary and lawful exchanges. Now a day, signature verification is required for different frauds. In this paper, an online mark check framework idea …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 7, Issue 3, 2020 · pp. 1–7 Read article
-
Fake Cryptocurrency Detection Using Python
Abstract: This study investigates the use of Python-based techniques for detecting fraudulent cryptocurrencies, addressing a growing concern in the digital financial ecosystem. The research methodology integrates various data science approaches, including web scraping, API integration, and advanced data analysis using Pandas and NLTK. Machine learning models, particularly classification algorithms such as Random Forest, are employed to analyze key features extracted from cryptocurrency whitepapers, social media discussions, and transactional data. By training …
Published in Recent Trends in Programming languages · Vol. 12, Issue 1, 2025 · pp. 1–7 Read article
-
Fraud Detection in Government Procurement Using Machine Learning
Abstract: Fraud represents a significant challenge in the realm of procurement, with estimates indicating that between 12 and 30% of global procurement budgets are lost to fraudulent activities (OECD, 2023). The pervasive nature of procurement fraud, which may encompass a range of deceptive practices such as bid rigging, invoice fraud, and procurement kickbacks, not only undermines the integrity of financial operations but also results in substantial losses for organizations. These losses …
Published in Journal of Open Source Developments · Vol. 12, Issue 2, 2025 · pp. 19–34 Read article
-
Malicious Application Detection in Windows Using SVM Algorithm
Abstract: In recent years, both the development of Windows application clients and the uses of smart mobile phones have increased significantly. As the number of Windows application users continues to grow, there is a rise in malicious individuals who develop harmful Windows applications with the intent of unlawfully obtaining confidential information and engaging in fraudulent activities. These applications are designed to target vulnerable areas such as mobile banking and digital wallets, …
Published in International Journal of Mobile Computing Technology · Vol. 1, Issue 1, 2023 · pp. 30–36 Read article
-
Credit Card Fraud Detection Using Machine Learning Techniques
Abstract: Credit card fraud has become a significant concern in today's digital economy, with billions of dollars being lost annually to fraudulent transactions. Conventional rule-based approaches frequently prove inadequate in addressing the constantly changing strategies employed by fraudsters. Machine learning methods have emerged as robust solutions for detecting credit card fraud, presenting the capability to accurately identify fraudulent transactions promptly. In this study, we investigate the efficiency of three widely used …
Published in Journal of Open Source Developments · Vol. 11, Issue 1, 2024 · pp. 1–7 Read article
-
Blockchain-based Land Registration: A Secure and Transparent Approach
Abstract: Traditional land registration systems have long faced challenges such as inefficiencies, lack of transparency, and security vulnerabilities due to centralized governance. The dependency on centralized entities for property record maintenance introduces risks such as data manipulation, fraudulent transactions, and unauthorized modifications. Additionally, bureaucratic procedures in these systems often lead to delays in property transfers and ownership verification. These shortcomings hinder economic development and trust among stakeholders. Blockchain technology presents an …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 2, 2025 · pp. 40–46 Read article
-
Credit Card Fraud Detection Using Deep Learning Techniques
Abstract: Credit card frauds have become very common nowadays and lot of cases has been reported in the recent past with the increase in cybercrimes. Online credit card frauds can be efficiently detected exploiting deep-learning techniques. This paper proposes two unsupervised learning algorithms namely Auto encoder and RBM (Restricted Boltzmann Machine) that acquire information from new transactions and estimate anomalous patterns from these transactions to accurately predict number of credit card …
Published in Journal of Web Engineering & Technology · Vol. 7, Issue 1, 2020 · pp. 30–47 Read article
-
A Survey of Fraud Detection Techniques for Credit Card Based Transaction Processing
Abstract: The wide emergence of electronic-commerce has widened the extensive usage of credit card for online transactions. However, there is also a high rise in malicious transaction and fraudulent associated with the credit cards. In this study, we present several models and algorithm used in data mining for the detection of such malicious fraudulent or thefts. Such algorithm learns the transaction patterns and clusters the pattern of sequences usually involving with …
Published in Recent Trends in Parallel Computing · Vol. 2, Issue 1, 2015 · pp. 10–15 Read article
-
Card Fraud Detection Using Artificial Neural Network and Multilayer Perception Algorithm
Abstract: Fraud has posed a significant challenge for merchants, especially in the online business sector, over the course of many years. This is primarily due to the advancements in technology that have made credit card transactions a common method of payment. Credit card fraud refers to the unauthorized use of a credit card by an individual for personal purposes, without the owner's consent and with no intention of paying for the …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 1, Issue 1, 2023 · pp. 21–30 Read article
-
Cloud-driven Fraud Detection: Evaluating Decision Tree and Random Forest Classifiers for Credit Card Transaction Security
Abstract: With the alarming rise in global financial fraud, necessitating substantial annual losses, modern techniques for fraud detection are continuously evolving across various business domains. Fraud detection involves constant monitoring of user activities to estimate, perceive, or prevent undesirable behaviour. Cloud Computing emerges as a promising solution, accelerating application deployment, fostering creativity and innovation, reducing costs, and enhancing overall business acumen. This study introduces a cloud-driven approach to fraud detection, specifically …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 1, 2024 · pp. 13–27 Read article
-
Detecting Fake Accounts on Social Media Using Machine Learning
Abstract: The growing frequency of fake accounts on social media platforms underscores the critical necessity for effective detection methods. In response to this challenge, our study leverages state-of-the-art machine learning techniques to identify and counter deceptive entities effectively. By conducting a thorough analysis of social media data, our approach unveils intricate patterns indicative of fraudulent accounts, enabling proactive measures against them. Through the application of advanced algorithms, we present a comprehensive …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 11, Issue 2, 2024 · pp. 21–32 Read article
-
Robustness of Deepfake Detection Systems Against Adversarial Attacks
Abstract: This paper explores a deep learning system to detect deepfake videos, a common type of fake media. With the use of sophisticated methods such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), our system can reliably discern between authentic and altered videos. It analyzes both the images and the audio in videos to find signs of deepfake manipulation. We process video frames and audio, extract features with CNNs …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 Read article
-
Optimizing Charitable Donations with Mobile Technology: A Comprehensive Study
Abstract: This article explores the utilization of mobile applications for charitable donations, facilitating connections with various responsible NGOs. The primary aim of these mobile applications is to streamline the donation process for individuals, enabling them to contribute money, food, clothing, and other essentials efficiently and effectively. Traditionally, donations are made via individual NGO websites or by directly transferring funds to provided account numbers. Mobile applications revolutionize this process by transforming laborious …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 11, Issue 3, 2024 · pp. 6–20 Read article
-
Blockchain for Transparent and Secure Electronic Voting
Abstract: A secure and transparent electronic voting system leveraging blockchain technology addresses the critical need for trustworthy and efficient electoral processes. By integrating blockchain, the system ensures that each voter can only cast their vote once, and the integrity of the vote is maintained without interference. Blockchain’s decentralized and tamper-resistant nature safeguards the voting records, preventing any fraudulent activities or unauthorized alterations. Each vote is treated as a transaction in the …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 3, 2024 · pp. 23–28 Read article
-
Detection of Phishing Website URLs and Email/SMS Using Random Forest and Multinomial Naive Bayes
Abstract: Currently, phishing attacks via SMS/email and URL have become significant threat to cybersecurity, posing risks to both individuals and organizations alike. Phishing attacks typically involve the creation of fraudulent websites or the dissemination of deceptive emails and SMS messages to trick users into disclosing sensitive information such as passwords, credit card numbers or personal details. To respond to these attacks, we develop a robust system for the detection of phishing …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 22–30 Read article