Machine Learning and Feature Engineering for Detecting Living off the Land Attacks

被引：0

作者：

Boros, Tiberiu ^{[1
]}

Cotaie, Andrei ^{[1
]}

Stan, Antrei ^{[1
]}

Vikramjeet, Kumar ^{[2
]}

Malik, Vivek ^{[2
]}

Davidson, Joseph ^{[2
]}

机构：

[1] Adobe Syst, Bucharest, Romania

[2] Adobe Syst, San Jose, CA USA

来源：

PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS, BIG DATA AND SECURITY (IOTBDS) | 2022年

关键词：

Machine Learning; Living-off-the-Land (LotL); Feature Engineering; Artificial Intelligence; Random Forest; Commands; CommandLine; OpenSource; Linux;

D O I：

10.5220/0011004500003194

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Among the methods used by attackers to avoid detection, living off the land is particularly hard to detect. One of the main reasons is the thin line between what is actually operational/admin activity and what is malicious activity. Also, as shown by other research, this type of attack detection is underrepresented in Anti-Virus (AV) software, mainly because of the high risk of false positives. Our research focuses on detecting this type of attack through the use of machine learning. We greatly reduce the number of false detection by corpora design and specialized feature engineering which brings in-domain human expert knowledge. Our code is open-source and we provide pre-trained models.

引用

页码：133 / 140

页数：8

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