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IEEE Symposium on Computers and Communications (ISCC)

作者： Carmela Comito National Research Council of Italy (CNR) Institute for High Performance Computing and Networking (ICAR) Rende CS Italy

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Social media has become a key tool for spreading the news, discussing ideas and comments on world events, playing a relevant role also in public health management, especially in epidemics surveillance like seasonal flu. Online social media actually can provide an important help in monitoring disease spreading as users self-report their health-related issues. Since the very first days of COVID-19 outbreak, people exchanged news, updates, sentiment and opinion about the pandemics. The paper describes a study aiming at evaluating the correlation of tweets with official COVID-19 data. Based on the outcomes of the correlation study, the paper proposes a forecasting model to predict the number of new daily COVID-19 cases. The approach is formulated as an autoregressive model that combines tweets and official COVID-19 data. A real-word dataset of tweets is used for the correlation study and to evaluate the performance of the forecasting model. Results shown the feasibility of the approach, highlighting the improvement obtained when tweets are integrated in the forecasting model, allowing to predict new COVID-19 cases in advance, on average 4–6 days before they were confirmed.

关键词： COVID-19 Correlation Social networking (online) Computational modeling Surveillance Blogs Predictive models

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Detection of Network Covert Channels in IoT Ecosystems Using Machine Learning 6

Detection of Network Covert Channels in IoT Ecosystems Using...

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6th Italian Conference on Cybersecurity, ITASEC 2022

作者： Guarascio, Massimo Zuppelli, Marco Cassavia, Nunziato Manco, Giuseppe Caviglione, Luca ICAR - Institute for High Performance Computing and Networking Via Pietro Bucci cubo 8/9C Rende87036 Italy IMATI - Institute for Applied Mathematics and Information Technologies Via de Marini 6 Genova16149 Italy

Steganographic techniques and covert channels are becoming exploited by a wide-range of malware to avoid detection and bypass network security tools. With the ubiquitous diffusion of IoT nodes, such offensive schemes are expected to be used to exfiltrate data or to covertly orchestrate botnets composed of resource-constrained nodes (e.g., as it happens in Mirai). Therefore, in this paper, we present a machine learning technique for the detection of network covert channels targeting the TTL field of IPv4 datagrams. Specifically, we propose to use Autoencoders to reveal anomalous traffic behaviors. The experimental evaluation performed over realistic traffic traces showcases the effectiveness of our approach. © 2022 Copyright for this paper by its authors.

关键词： Network security

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Cloud- and Edge-based ERP systems for Industrial Internet of Things and Smart Factory

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Procedia Computer Science 2022年 200卷 537-545页

作者： Vijay Prakash Claudio Savaglio Lalit Garg Seema Bawa Giandomenico Spezzano Thapar Institute of Engineering and Technology Patiala India Institute for High Performance Computing and Networking (ICAR) National Research Council Italy University of Malta Msida Malta

Enterprise Resource Planning (ERP) system is a collection of collaborative software programs. It handles transactions through enterprise-wide business processes using shared databases, standard methodologies, and data exchange across and within functional domains. Setting up an enterprise system is a complex activity and a costly and dangerous investment. Further, ERP system potentially affects core business and supporting processes, especially in complex and cyber-physical domains such as Industrial Internet of Things (IIoT) and Smart Factory. Cloud ERP (C-ERP) and Edge ERP (E-ERP) are alternatives to traditional, centralised and monolithic ERP implementation for incorporating the benefits of Cloud and Edge computing. Their main benefits include ease of use, resource balancing, bandwidth, cost-saving, and higher privacy/security. This paper discusses the benefits and limitations of using C-ERP and E- ERP in IIoT and Smart Factory domains, along with future directions in the ERP era of demand.

关键词： Enterprise Resource Planning Cloud ERP Edge computing Edge ERP Industrial Internet of Things ERP Adoption Smart Factory

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Sanitization of images containing stegomalware via machine learning approaches 5

Sanitization of images containing stegomalware via machine l...

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5th Italian Conference on Cybersecurity, ITASEC 2021

作者： Zuppelli, Marco Manco, Giuseppe Caviglione, Luca Guarascio, Massimo ICAR - Institute for High Performance Computing and Networking Via Pietro Bucci cubo 8/9C Rende87036 Italy Genova16149 Italy

In recent years, steganographic techniques have become increasingly exploited by malware to avoid detection and remain unnoticed for long periods. Among the various approaches observed in real attacks, a popular one exploits embedding malicious information within innocent-looking pictures. In this paper, we present a machine learning technique for sanitizing images containing malicious data injected via the Invoke-PSImage method. Specifically, we propose to use a deep neural network realized through a residual convolutional autoencoder to disrupt the malicious information hidden within an image without altering its visual quality. The experimental evaluation proves the effectiveness of our approach on a dataset of images injected with PowerShell scripts. Our tool removes the injected artifacts and inhibits the reconstruction of the scripts, partially recovering the initial image quality. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Malware

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Recommender System for Social Media: Research Challenges and Future Applications 5

Recommender System for Social Media: Research Challenges and...

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5th International Conference on Transdisciplinary AI, TransAI 2023

作者： Ge, Mouzhi Pilato, Giovanni Persia, Fabio D'Auria, Daniela Deggendorf Institute of Technology Faculty of European Campus Rottal-Inn Deggendorf Germany Institute for High Performance Computing and Networking National Research Council of Italy Palermo Italy University of l'Aquila Department of Information Engineering Computer Science and Mathematics L'Aquila Italy University of Bozen-Bolzano Faculty of Computer Science Free Bozen-Bolzano Italy

ISBN: (纸本)9798350358018

Social networks have evolved as a ubiquitous means of communication in our daily lives. Users consistently generate a large amount of data on social media platforms, which on the other hand has increased the complexity when selecting relevant social media content. Thus, recommender system has been emerged as a solution to provide personalized and user-centric media recommendations. However, social media has significantly broadened the scope of available inputs such as social sensing data and new types of social media. It not only offers new research opportunities for social media recommender systems, but also indicates that various domains may benefit from the application of social media recommender systems. Therefore, this paper conducts a comprehensive survey of state-of-the-art research between social media and recommender systems. The survey results identify a set of research challenges on how social media recommender systems can be applied in various domains such as healthcare and route planning. © 2023 IEEE.

关键词： Recommender systems

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Swarm Intelligence Model for Securing Healthcare Ecosystem

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Procedia Computer Science 2022年 210卷 149-156页

作者： Patrizia Ribino Mario Ciampi Shareeful Islam Spyridon Papastergiou Institute for High Performance Computing and Networking National Research Council of Italy School of Computing and Information Science Anglia Ruskin University United Kingdom Focal Point Belgium Department of Informatics University of Piraeus Greece

The healthcare sector is constantly facing challenges in ensuring security due to the sophisticated attacks by the threat actor for acquiring sensitive patient data. An attack on the system can pose any potential risk to the business continuity. The increased use of information technology in the modern healthcare system makes medical devices and systems more vulnerable to exploitation and possible cyber-security attacks. This paper proposes a flexible and decentralized cyber-security model based on the integration of multi-agent systems and swarm intelligence for tackling the propagation of attacks inside interconnected healthcare organizations and ensuring the whole healthcare ecosystem's security and resilience. The proposed model is based on the collaboration between the agents with different functions and cognitive capabilities, named primary and supervisor agents. Primary agents are lightweight BDI (Belief-Desire-Intention) agents implementing a minimum set of capabilities for monitoring a specific area of the healthcare system; supervisor agents incorporate an extended version of the BDI reasoning to provide advanced capabilities for securing the overall healthcare system by enabling collective intelligence and overall cyber-security awareness. The preliminary experimental results show that the model is robust and responsive for securing the ecosystem.

关键词： Healthcare Ecosystem Cyber-Security Swarm Intelligence Multi-Agent Systems Belief-Desire-Intention

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A Novel Deep Learning Approach for Colon and Lung Cancer Classification Using Histopathological Images

A Novel Deep Learning Approach for Colon and Lung Cancer Cla...

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IEEE International Conference on e-Science and Grid computing

作者： Naeem Ullah Ivanoe De Falco Giovanna Sannino Department of Electrical Engineering and Information Technology University of Naples “Federico II” Naples Italy Institute for High-Performance Computing and Networking (ICAR) National Research Council (CNR) Naples Italy

Colon and Lung cancers are two of the most common causes of mortality in adults. They may simultaneously form in organs and have a detrimental effect on human life. There is a high risk that cancer will spread to the two organs if it is not discovered in the early stages. One of the most essential elements of successful therapy is the histological diagnosis of such cancers. Deep learning algorithms have improved the speed and accuracy of time-consuming and challenging procedures, enabling researchers to examine a huge number of patients swiftly and inexpensively. By examining their histological images and applying modern deep learning, this study develops a classification framework called DeepLCCNet to discriminate between five kinds of colon and lung tissues (three malignant and two benign). More precisely, we have classified five tissue types of Lung and Colon Cancer Histopathological Images data set using our model, i.e., benign tissue of the lung, squamous cell carcinoma of the lung, adenocarcinoma of the lung, benign tissue of the colon, and adenocarcinoma of the colon. According to the results, the proposed model can detect cancer tissues with an average accuracy of 99.67% and maximum accuracy of 99.84%. Medical professionals will be able to utilize a precise, automated system for detecting and classifying various kinds of colon and lung cancers.

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Clustering of longitudinal Clinical Dementia Rating data to identify predictors of Alzheimer's disease progression

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Procedia Computer Science 2024年 251卷 326-333页

作者： Patrizia Ribino Giovanni Paragliola Claudia Di Napoli Maria Mannone Davide Chicco Francesca Gasparini Institute for High-Performance Computing and Networking (ICAR-CNR) Italy Institute of Physics and Astronomy University of Potsdam Germany Dipartimento di Informatica Sistemistica e Comunicazione Università di Milano-Bicocca Italy NeuroMI Milan Center for Neuroscience Università di Milano-Bicocca Milan Italy

Clinical Dementia Rating (CDR) is a common tool to assess cognitive and functional abilities in the context of Alzheimer's disease (AD). It is a structured interview that encompasses evaluation across six specific domains. However, AD's initial stages may not lead to a uniform cognitive decline across all cognitive domains. The main aim of this study is to evaluate the prognostic utility of individual CDR domains in predicting the progression of AD dementia over a five-year longitudinal period among an elderly cohort. Initially, a longitudinal-cluster analysis was conducted using five-point longitudinal data to categorize subjects into clusters based on the progression of CDR domains during the follow-up. Then, a statistical analysis was performed on the identified clusters to ascertain whether, at the baseline, patients exhibiting stability have different profiles about CDR domains compared to patients who converted to an AD during the whole follow-up period. Results show that the risk of AD progression was mainly related to problems with Orientation and Judgment at the baseline.

关键词： Alzheimer's disease Cognitive Decline Clinical Dementia Rating Longitudinal Clustering Statistical Analysis

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AIxPAC 2023 - Preface to the 1st Workshop on Artificial Intelligence for Perception and Artificial Consciousness 1

AIxPAC 2023 - Preface to the 1st Workshop on Artificial Inte...

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1st Workshop on Artificial Intelligence for Perception and Artificial Consciousness, AIxPAC 2023

作者： Bruno, Alessandro Pipitone, Arianna Manzotti, Riccardo Augello, Agnese Mazzeo, Pier Luigi Vella, Filippo Chella, Antonio IULM University Via Carlo Bo 1 Milano20143 Italy University of Palermo Via delle Scienze Palermo90128 Italy ICAR Institute for High Performance Computing and Networking CNR Via Ugo La Malfa 153 Palermo90146 Italy ISASI Institute of Applied Sciences Intelligent Systems CNR Via Monteroni sn Lecce73100 Italy

The AIxPAC workshop aims to bring together researchers from academia and industry to discuss the latest advancements in AI for perception and consciousness. The workshop features presentations from experts on the physicalist ontology of consciousness, artificial consciousness, colour perception, and computer vision. Some research questions are addressed in AIxPAC: Can a visual perception system be embedded into machines? How accurately does AI tackle visual attention processes? What is the relation between attention and consciousness? Can AI architectures and approaches be used to design Artificial Consciousness? What are the pros and cons of Large Language Models? The given research questions foster multidisciplinary contributions and several critical readings for the given topics. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Artificial intelligence

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Fighting Misinformation, Radicalization and Bias in Social Media

Fighting Misinformation, Radicalization and Bias in Social M...

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Coppolillo, Erica Comito, Carmela Minici, Marco Ritacco, Ettore Folino, Gianluigi Pisani, Francesco Sergio Guarascio, Massimo Manco, Giuseppe Institute for High Performance Computing and Networking via P. Bucci 8-9/C Rende87036 Italy University of Calabria via P. Bucci Rende87036 Italy Università degli Studi di Pisa Pisa56126 Italy Università degli Studi di Udine Via Palladio8 Udine33100 Italy

Social media have become the ideal place for black hats and malicious individuals to target susceptible users through different attack vectors and then manipulate their opinions and interests. Fake news, radicalization, and pushing bias into the data represent some popular ways noxious users adopt to perpetrate their criminal intents. In this evolving scenario, Artificial Intelligence techniques represent a valuable tool to early detect and mitigate the risk due to the spreading of these emerging attacks. In this work, we describe the Machine Learning based solutions developed to address the problems mentioned above and our current research. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Social networking (online)

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