Telecom industries lose globally 46.3 Billion USD due to fraud. Data mining
and machine learning techniques (apart from rules oriented approach) have been
used in past, but efficiency has been low as fraud pattern changes very
rapidly. This paper presents an industrialized solution approach with self
adaptive data mining technique and application of big data technologies to
detect fraud and discover novel fraud patterns in accurate, efficient and cost
effective manner. Solution has been successfully demonstrated to detect
International Revenue Share Fraud with <5% false positive. More than 1 Terra
Bytes of Call Detail Record from a reputed wholesale carrier and overseas
telecom transit carrier has been used to conduct this study.

电信行业因欺诈而在全球范围内损失了 463 亿美元。本文提出了一种工业化的解决方案，采用自适应数据挖掘技术和大数据技术来准确、高效和具有成本效益地检测欺诈，并发现新的欺诈模式。该解决方案已成功地检测出少于 5% 的虚假阳性的国际收入分成欺诈。本研究使用了一家有声望的批发运营商和海外电信中转运营商超过 1TB 的通话详单记录。

基于大数据的机器学习和工程的电信欺诈分析

Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME)  for Telecom

Generative deep learning models are able to create realistic audio and video.
This technology has been used to impersonate the faces and voices of
individuals. These ``deepfakes'' are being used to spread misinformation,
enable scams, perform fraud, and blackmail the innocent. The technology
continues to advance and today attackers have the ability to generate deepfakes
in real-time. This new capability poses a significant threat to society as
attackers begin to exploit the technology in advances social engineering
attacks. In this paper, we discuss the implications of this emerging threat,
identify the challenges with preventing these attacks and suggest a better
direction for researching stronger defences.

本文讨论了生成式深度学习模型所造成的威胁，特别是其用于混淆、欺诈和社会工程攻击等方面的弊端，并且提出了加强社会防御的建议。

即时深度伪造威胁讨论文件

Discussion Paper: The Threat of Real Time Deepfakes

As various forms of fraud proliferate on Ethereum, it is imperative to
safeguard against these malicious activities to protect susceptible users from
being victimized. While current studies solely rely on graph-based fraud
detection approaches, it is argued that they may not be well-suited for dealing
with highly repetitive, skew-distributed and heterogeneous Ethereum
transactions. To address these challenges, we propose BERT4ETH, a universal
pre-trained Transformer encoder that serves as an account representation
extractor for detecting various fraud behaviors on Ethereum. BERT4ETH features
the superior modeling capability of Transformer to capture the dynamic
sequential patterns inherent in Ethereum transactions, and addresses the
challenges of pre-training a BERT model for Ethereum with three practical and
effective strategies, namely repetitiveness reduction, skew alleviation and
heterogeneity modeling. Our empirical evaluation demonstrates that BERT4ETH
outperforms state-of-the-art methods with significant enhancements in terms of
the phishing account detection and de-anonymization tasks. The code for
BERT4ETH is available at: this https URL

提出了一个名为 BERT4ETH 的通用预训练 Transformer 编码器，可用于检测以太坊上的各种欺诈行为，该方法相较于当前研究使用的基于图形的欺诈检测方法，在处理高度重复，偏态分布和异构的以太坊交易方面具有更好的性能，并使用三种实用的有效策略来解决为以太坊预训练 BERT 模型所面临的挑战，BERT4ETH 在钓鱼账户检测和去匿名任务方面表现出显着的改善。