Learning Explainable Representations of Malware Behavior

Autor:	Prasse, Paul, Brabec, Jan, Kohout, Jan, Kopp, Martin, Bajer, Lukas, Scheffer, Tobias
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	We address the problems of identifying malware in network telemetry logs and providing \emph{indicators of compromise} -- comprehensible explanations of behavioral patterns that identify the threat. In our system, an array of specialized detectors abstracts network-flow data into comprehensible \emph{network events} in a first step. We develop a neural network that processes this sequence of events and identifies specific threats, malware families and broad categories of malware. We then use the \emph{integrated-gradients} method to highlight events that jointly constitute the characteristic behavioral pattern of the threat. We compare network architectures based on CNNs, LSTMs, and transformers, and explore the efficacy of unsupervised pre-training experimentally on large-scale telemetry data. We demonstrate how this system detects njRAT and other malware based on behavioral patterns. Comment: This is a pre-print of an article to appear in Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.12328 Zobrazit plný text záznamu View this record from Arxiv