Gibran Gomez

Gibran Gomez, Kevin van Liebergen, Davide Sanvito et al.

Abuse reporting services collect reports about abuse victims have suffered. Accurate classification of the submitted reports is fundamental to analyzing the prevalence and financial impact of different abuse types (e.g., sextortion, investment, romance). Current classification approaches are problematic because they require the reporter to select the abuse type from a list, assuming the reporter has the necessary experience for the classification, which we show is frequently not the case, or require manual classification by analysts, which does not scale. To address these issues, this paper presents a novel approach to classify cryptocurrency abuse reports automatically. We first build a taxonomy of 19 frequently reported abuse types. Given as input the textual description written by the reporter, our classifier leverages a large language model (LLM) to interpret the text and assign it an abuse type in our taxonomy. We collect 290K cryptocurrency abuse reports from two popular reporting services: BitcoinAbuse and BBB's ScamTracker. We build ground truth datasets for 20K of those reports and use them to evaluate three designs for our LLM-based classifier and four LLMs, as well as a supervised ML classifier used as a baseline. Our LLM-based classifier achieves a precision of 0.92, a recall of 0.87, and an F1 score of 0.89, compared to an F1 score of 0.55 for the baseline. We demonstrate our classifier in two applications: providing financial loss statistics for fine-grained abuse types and generating tagged addresses for cryptocurrency analysis platforms.

Gibran Gomez, Platon Kotzias, Matteo Dell'Amico et al.

Malware abuses TLS to encrypt its malicious traffic, preventing examination by content signatures and deep packet inspection. Network detection of malicious TLS flows is an important, but challenging, problem. Prior works have proposed supervised machine learning detectors using TLS features. However, by trying to represent all malicious traffic, supervised binary detectors produce models that are too loose, thus introducing errors. Furthermore, they do not distinguish flows generated by different malware. On the other hand, supervised multi-class detectors produce tighter models and can classify flows by malware family, but require family labels, which are not available for many samples. To address these limitations, this work proposes a novel unsupervised approach to detect and cluster malicious TLS flows. Our approach takes as input network traces from sandboxes. It clusters similar TLS flows using 90 features that capture properties of the TLS client, TLS server, certificate, and encrypted payload; and uses the clusters to build an unsupervised detector that can assign a malicious flow to the cluster it belongs to, or determine it is benign. We evaluate our approach using 972K traces from a commercial sandbox and 35M TLS flows from a research network. Our clustering shows very high precision and recall with an F1 score of 0.993. We compare our unsupervised detector with two state-of-the-art approaches, showing that it outperforms both. The false detection rate of our detector is 0.032% measured over four months of traffic.