842 research outputs found
Machine learning-based malware detection for IoT devices using control-flow data
Embedded devices are specialised devices designed for one or only a few
purposes. They are often part of a larger system, through wired or wireless
connection. Those embedded devices that are connected to other computers or
embedded systems through the Internet are called Internet of Things (IoT for
short) devices.
With their widespread usage and their insufficient protection, these devices
are increasingly becoming the target of malware attacks. Companies often cut
corners to save manufacturing costs or misconfigure when producing these
devices. This can be lack of software updates, ports left open or security
defects by design. Although these devices may not be as powerful as a regular
computer, their large number makes them suitable candidates for botnets. Other
types of IoT devices can even cause health problems since there are even
pacemakers connected to the Internet. This means, that without sufficient
defence, even directed assaults are possible against people.
The goal of this thesis project is to provide better security for these
devices with the help of machine learning algorithms and reverse engineering
tools. Specifically, I study the applicability of control-flow related data of
executables for malware detection. I present a malware detection method with
two phases. The first phase extracts control-flow related data using static
binary analysis. The second phase classifies binary executables as either
malicious or benign using a neural network model. I train the model using a
dataset of malicious and benign ARM applications
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