loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Authors: Giacomo Iadarola 1 ; Fabio Martinelli 1 ; Francesco Mercaldo 2 and Antonella Santone 3

Affiliations: 1 Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy ; 2 Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy, Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy ; 3 Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy

ISBN: 978-989-758-426-8

ISSN: 2184-4976

Keyword(s): Machine Learning, Mobile Security, Android, Malware Classification, Image Texture Analysis.

Abstract: The increasing number of malware in mobile environment follows the continuous growth of the app stores, which required constant research in new malware detection approaches, considering also the weaknesses of signature-based anti-malware software. Fortunately, most of the malware are composed of well-known pieces of code, thus can be grouped into families sharing the same malicious behaviour. One interesting approach, which makes use of Image Classification techniques, proposes to convert the malware binaries to images, extract feature vectors and classifying them with supervised machine learning models. Realizing that researchers usually evaluate their solutions on private datasets, it is difficult to establish whether a model can be generalized on another dataset, making it difficult to compare the performance of the various models. This paper presents a comparison between different combination of feature vector extraction methods and machine learning models. The methodology aimed t o evaluate feature extractors and supervised machine learning algorithms, and it was tested on more than 20 thousand images of malware, grouped into 10 different malware families. The best classifier, a combination of GIST descriptors and Random Forest classifiers, achieved an accuracy of 0.97 on average. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.232.55.103

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Iadarola, G.; Martinelli, F.; Mercaldo, F. and Santone, A. (2020). Image-based Malware Family Detection: An Assessment between Feature Extraction and Classification Techniques. In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoTs , ISBN 978-989-758-426-8 ISSN 2184-4976, pages 499-506. DOI: 10.5220/0009817804990506

@conference{ai4eiots 20,
author={Giacomo Iadarola. and Fabio Martinelli. and Francesco Mercaldo. and Antonella Santone.},
title={Image-based Malware Family Detection: An Assessment between Feature Extraction and Classification Techniques},
booktitle={Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoTs ,},
year={2020},
pages={499-506},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009817804990506},
isbn={978-989-758-426-8},
issn={2184-4976},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: AI4EIoTs ,
TI - Image-based Malware Family Detection: An Assessment between Feature Extraction and Classification Techniques
SN - 978-989-758-426-8
IS - 2184-4976
AU - Iadarola, G.
AU - Martinelli, F.
AU - Mercaldo, F.
AU - Santone, A.
PY - 2020
SP - 499
EP - 506
DO - 10.5220/0009817804990506

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.