Anouska Dutta

The one-time passwords (OTPs) that are delivered by the use of SMSs have become a very attractive target of telecom fraud since it can be used as a second line of authentication. OTP Bots are automation-based interception services that run through Telegram-based Fraud-as-a-Service (FaaS) platforms that have become a systemic tool of real-time avoidance of multi-factor authentication, caller-ID spoofing, and abusive number-porting. In this paper, we systematically investigate the OTP bot threat space and make three main contributions, namely: (i): four-tier taxonomic framework distinguishing between Vishing Bots, SMS Interception Bots, SIM-Swap Bots, and AI-enhanced hybrid bots; (ii): five-step operational life cycle model superimposed on the MITRE ATTACK framework; and (iii): a hybrid detection architecture that uses rule-based analysis in combination with a Random Forest classifier that we tested on 50 thousand artificial authentication sessions. It has an accuracy of 96.84% and recall of 94.67% as well as a falsepositive rate of 2.9% and a detection latency of 42 milliseconds, which is better than both rule-based and machine learning-only baselines. Threat-intelligence reports suggest that during the next year, 2023 to 2024, the activity of dark-web OTP bots is expected to increase by 31 per cent each year, and the cost of services is USD 10-50 per session, and success rates can be up to 90 per cent. It is estimated that artificially inflated traffic (AIT) contributes to 20% of OTP SMS volume, which implies USD 1.15 billion of fraudulent charges per year. We offer a tiered mitigation structure that operates on the level of telecom operators (implementing STIR/SHAKEN, SS7 firewalling, and porting KYC verification), financial institutions (migration to FIDO2 and reducing the OTP validity window), regulators (attestation requirements and coordinated takedown), and end users (improved social-engineering awareness). The evidence indicates that, without reform at the infrastructure level, OTPs, which are based on SMS, do not currently offer a high enough level of second-factor authentication to prevent the high-assurance authentication

The term “Deepfake” combines “Deep Learning” and “Fake” which refers to any manipulated or altered content. Deepfakes are AI-generated fake videos, images, or audio recordings that look and sound incredibly realistic. With more personal information available online on social media, news websites, and other public platforms, the ease of creating deepfakes has become incredibly easy. Malicious actors leverage Open-Source Intelligence (OSINT) techniques to gather publicly available information, to obtain the necessary photos, videos, and voice recordings to create sophisticated deepfakes and use them for fraud, disinformation, and identity theft.This paper examines five concrete types of deepfakes that take advantage of OSINT. These are face-swapped videos in news media, impersonation videos for business executives, fake testimonies for use in courtrooms, contextual fabrications for the manipulation of political issues, and voice cloning for phone scams. Through analysis of documented real-world cases, this study demonstrates how easily accessible public information has been weaponized by cybercriminals, creating unprecedented threats that undermine trust and security in our digital world.