<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>malware detection on Chiara Camerota</title><link>https://camerotachiara.github.io/tags/malware-detection/</link><description>Recent content in malware detection on Chiara Camerota</description><generator>Hugo -- 0.114.0</generator><language>en</language><atom:link href="https://camerotachiara.github.io/tags/malware-detection/index.xml" rel="self" type="application/rss+xml"/><item><title>Addressing Data Security in IoT: Minimum Sample Size and Denoising Diffusion Models for Improved Malware Detection</title><link>https://camerotachiara.github.io/papers/paper1/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://camerotachiara.github.io/papers/paper1/</guid><description>This paper investigates on how to define the miimum sample siz and improving the data agumentation for Traffic Images. Published 20th International Conference on Network and Service Management, 2024.</description></item><item><title>The intrinsic convenience of federated learning in malware IoT detection</title><link>https://camerotachiara.github.io/papers/paper2/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://camerotachiara.github.io/papers/paper2/</guid><description>This paper investigates on how to implement a federated malware recognition system. Publishing 1st Workshop on Integrated Wireless Networking and Computing (IWNC), 2024.</description></item></channel></rss>