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Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance
2024 IEEE International Conference on Big Data (BigData)Can we trust Large Language Models (LLMs) to accurately predict scam? This paper investigates the vulnerabilities of LLMs when facing adversarial scam messages for the task of scam detection. We addressed this issue by creating a comprehensive dataset with fine-grained labels of scam messages, including both original and adversarial scam messages.
Michin Hong +2 more
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Streaming phishing scam detection method on Ethereum
2023 IEEE International Symposium on Circuits and Systems (ISCAS), 2023 Phishing is a widespread scam activity on Ethereum, causing huge financial losses to victims. Most existing phishing scam detection methods abstract accounts on Ethereum as nodes and transactions as edges, then use manual statistics of static node features to obtain node embedding and finally identify phishing scams through classification models ...
Wenjia Yu +3 more
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Attack Economics Based Fraud Detection for MVNO [PDF]
Jisuanji kexue, 2023 Driven by the full utilization of telecommunication resources and stimulating healthy market competition,mobile virtual network opera-tors(MVNOs) become popular rapidly in recent years.MVNOs rely on the infrastructures of mobile network ope-rators(MNOs ...
LI Yang, LI Zhenhua, XIN Xianlong
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Sistemasi: Jurnal Sistem Informasi, 2023 The popularity of blockchain continues to increase as technology develops, especially in the context of Ethereum as one of the leading blockchain platforms. However, this increase was also followed by many cases of fraud, especially in the form of tokens.
Dimas Arya Pamungkas +3 more
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Backers Beware: Characteristics and Detection of Fraudulent Crowdfunding Campaigns
Sensors, 2022 Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging companies or new startups in recent years. As crowdfunding prevails, it is also under substantial risk of the occurrence of fraud.
SeungHun Lee +2 more
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The SCAM Approach to Copy Detection in Digital Libraries
D-Lib Magazine, 1995 Scenario 1 Your local publishing company Books'R'Us decides to publish on the Internet its latest book in an effort to cut down on printing costs and book distribution expenses. Customers pay for the digital books using sophisticated electronic payment mechanisms such as DigiCash, First Virtual or InterPay.
Shivakumar, Narayanan +1 more
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Using LLM-Generated Data to Create a Roman Urdu Scam Call Detector
Sustainable Business and Society in Emerging EconomiesPurpose: Scam calls are spreading at an alarming pace where it is estimated that the world will lose more than a thousand billion dollars in 2024. Current machine-learning systems to classify scam-calls are not yet generalized: most of such systems are ...
Sameed Irfan +2 more
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Scientific ReportsLung cancer is a leading cause of cancer-related death globally, and accurate diagnostic methods for early detection are crucial to enhance survival. The current diagnostic procedures and classical machine learning methods are helpful; however, modelling
S. Sudeshna, B. Umamaheswara Rao
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Heuristic machine learning approaches for identifying phishing threats across web and email platforms [PDF]
Frontiers in Artificial IntelligenceLife has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat.
Ramprasath Jayaprakash +8 more
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Detecting phishing gangs via taint analysis on the Ethereum blockchain [PDF]
Blockchain, 2023 Blockchain technology has created a new cryptocurrency world and attracted a lot of attention. It also attracts scams, for example, phishing scam, a typical fraud, has been found making a notable amount of money in the blockchain ecosystem, which has a ...
Kangrui Huang, Weili Chen, Zibin Zheng
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