Deep Dive into Hunting for LotLs Using Machine Learning and Feature Engineering

被引：0

作者：

Boros, Tiberiu ^{[1
]}

Cotaie, Andrei ^{[2
]}

机构：

[1] Adobe Syst, Secur Coordinat Ctr, Bucharest, Romania

[2] UIPath, Secur Operat, Bucharest, Romania

来源：

PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS, BIG DATA AND SECURITY, IOTBDS 2023 | 2023年

关键词：

Machine Learning; Feature Engineering; Living Off the Land Attacks;

D O I：

10.5220/0011968700003482

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Living off the Land (LotL) is a well-known method in which attackers use pre-existing tools distributed with the operating system to perform their attack/lateral movement. LotL enables them to blend in along side sysadmin operations, thus making it particularly difficult to spot this type of activity. Our work is centered on detecting LotL via Machine Learning and Feature Engineering while keeping the number of False Positives to a minimum. The work described here is implemented in an open-source tool that is provided under the Apache 2.0 License, along side pre-trained models.

引用

页码：194 / 199

页数：6

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