An AI-based, Multi-stage detection system of banking botnets

Autor:	Ling, Li, Gao, Zhiqiang, Silas, Michael A, Lee, Ian, Doeuff, Erwan A Le
Rok vydání:	2019
Předmět:	Computer Science - Cryptography and Security Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Banking Trojans, botnets are primary drivers of financially-motivated cybercrime. In this paper, we first analyzed how an APT-based banking botnet works step by step through the whole lifecycle. Specifically, we present a multi-stage system that detects malicious banking botnet activities which potentially target the organizations. The system leverages Cyber Data Lake as well as multiple artificial intelligence techniques at different stages. The evaluation results using public datasets showed that Deep Learning based detections were highly successful compared with baseline models. Comment: FEAP-AI4Fin 2018 : NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1907.08276 Zobrazit plný text záznamu View this record from Arxiv