Learning Explainable Representations of Malware Behavior

• URL: http://arxiv.org/abs/2106.12328v1
• Date: Wed, 23 Jun 2021 11:50:57 GMT
• Title: Learning Explainable Representations of Malware Behavior
• Authors: Paul Prasse, Jan Brabec, Jan Kohout, Martin Kopp, Lukas Bajer, Tobias Scheffer
• Abstract summary: We develop a neural network that processes network-flow data into comprehensible emphnetwork events We then use the emphintegrated-gradients method to highlight events that jointly constitute the characteristic behavioral pattern of the threat. We demonstrate how this system detects njRAT and other malware based on behavioral patterns.
• Score: 3.718942345103135
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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