4 Contributo in Atti di Convegno (Proceeding)

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4.1 Contributo in Atti di convegno 11559
4.2 Abstract in Atti di convegno 2202
4.3 Poster 279

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Benchmark Analysis of YOLOv8 for Edge AI Video-Surveillance Applications

Video surveillance systems (VSSs) are crucial for identifying crimes involving handheld weapons, but their effectiveness is limited by high operational costs and the need for continuous human monitoring. To overcome these challenges, research has turned to deep learning (DL) to automate weapon detection. Early efforts focused on detecting weapons in images using resource-intensive frameworks – and, thus, unaffordable – and relying on datasets that poorly represent real-world settings. Given t...

A Max-Plus Algebra-based Approach for Modelling Shoals of Fish Robots during Underwater Exploration

This paper introduces a novel approach to modelling fish robots for underwater exploration, focusing on a shoal of three biomimetic vehicles, each equipped with distinct sensors. The aim is to efficiently model the coordinated behaviour of the robots within the team in exploring an area with certain Points of Interest (POIs) through predefined cyclical paths. Depending on their capabilities, robots are assigned diversified tasks and paths. The resulting model, represented as a max-plus linear...

Predictive Modeling of Key Performance Indicators for Greenhouse Gas Emission Reduction Using Machine Learning

As demand for digital technologies grows, so does the impact of greenhouse gas (GHG) emissions. This paper explores innovative strategies to reduce carbon dioxide (CO2e) emissions in the product and technology industry, focusing on sustainable solutions to minimize environmental impact during product processes and transformations. The integration of artificial intelligence and Key Performance Indicators (KPIs) is significant within industrial sectors. This study proposes a machine learning-ba...

Applying Intrusion Detection Based on Algorithmically Generated Domain Classification to Real Network Traffic

Domain Generation Algorithms (DGAs) are used by malware in establishing a connection with the Command and Control (C&C) server, to reduce the risk of the connection being identified by changing the C&C domain name dynamically but predictably. Since malicious Algorithmically-Generated Domains (mAGDs) have identifiable patterns, suitably trained Machine Learning (ML) models, here called mAGD classifiers, are often able to identify such domains. However, these results are typically obtai...

Using Graph Theory for Improving Machine Learning-based Detection of Cyber Attacks

Early detection of network intrusion attempts is one of the main pillars of cybersecurity. An effective approach in this regard consists in analyzing network traffic with the help of artificial intelligence algorithms, with the aim of detecting the possible presence of an attacker by distinguishing it from a legitimate user. This is commonly done by collecting the traffic exchanged between terminals in a network and analyzing it on a per-packet or per-connection basis. In this paper, we propo...

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