@INPROCEEDINGS{Came2410:Addressing,
AUTHOR="Chiara Camerota and Lorenzo Pappone and Tommaso Pecorella and Flavio
Esposito",
TITLE="Addressing Data Security in {IoT:} Minimum Sample Size and Denoising
Diffusion Models for Improved Malware Detection",
BOOKTITLE="2024 20th International Conference on Network and Service Management (CNSM)
(CNSM 2024)",
ADDRESS="Prague, Czech Republic",
PAGES="8.88",
DAYS=27,
MONTH=oct,
YEAR=2024,
KEYWORDS="malware detection; deep learning; data augmentation",
ABSTRACT="Machine learning (ML) has emerged as a compelling approach to identify
attacks in network traffic security. 
Existing malware detection strategies often concentrate on specific facets,
such as efficient data collection, particular types of malware, or handling
data scarcity. While valid, these strategies typically overlook the
potential for minimizing sample size, focusing instead on data
augmentation. This work introduces a novel method to determine the minimum
sample size necessary to achieve a specified accuracy level, measured by
the F1 score derived from the confusion matrix. We focus on TCP header
traffic data transformed into images through flow-splitting techniques for
multi-class traffic classification. In addition, we introduce a diffusion
model to generate new synthetic traffic images and show that our method
outperforms existing techniques in terms of stability and predictability.
This study also compares the effectiveness of synthetic image augmentation
using Generative Adversarial Networks (GANs) and Denoising Diffusion
Probabilistic Models (DDPM) in improving image recognition and
classification accuracy."
}