MALITE: Lightweight Malware Detection and Classification for Constrained Devices
Today, malware is one of the primary cyberthreats to organizations. Malware has pervaded almost every type of computing device including the ones having limited memory, battery and computation power such as mobile phones, tablets and embedded devices like Internet-of-Things (IoT) devices. Consequent...
Gespeichert in:
Hauptverfasser: | , , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Today, malware is one of the primary cyberthreats to organizations. Malware
has pervaded almost every type of computing device including the ones having
limited memory, battery and computation power such as mobile phones, tablets
and embedded devices like Internet-of-Things (IoT) devices. Consequently, the
privacy and security of the malware infected systems and devices have been
heavily jeopardized. In recent years, researchers have leveraged machine
learning based strategies for malware detection and classification. Malware
analysis approaches can only be employed in resource constrained environments
if the methods are lightweight in nature. In this paper, we present MALITE, a
lightweight malware analysis system, that can classify various malware families
and distinguish between benign and malicious binaries. MALITE converts a binary
into a gray scale or an RGB image and employs low memory and battery power
consuming as well as computationally inexpensive malware analysis strategies.
We have designed MALITE-MN, a lightweight neural network based architecture and
MALITE-HRF, an ultra lightweight random forest based method that uses histogram
features extracted by a sliding window. We evaluate the performance of both on
six publicly available datasets (Malimg, Microsoft BIG, Dumpware10, MOTIF,
Drebin and CICAndMal2017), and compare them to four state-of-the-art malware
classification techniques. The results show that MALITE-MN and MALITE-HRF not
only accurately identify and classify malware but also respectively consume
several orders of magnitude lower resources (in terms of both memory as well as
computation capabilities), making them much more suitable for resource
constrained environments. |
---|---|
DOI: | 10.48550/arxiv.2309.03294 |