Updated 12th October 2021
From the 7th to the 9th May 2020, CyberSANE participated in a special session on Artificial Intelligence for Emerging IoT Systems: Open Challenges and Novel Perspectives (AI4EIoTs 2020) within the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020) online conference.
During this event, the paper Image-based Malware Family Detection: An Assessment between Feature Extraction and Classification Techniques, written by Giacomo Iadarola1, Fabio Martinelli1, Francesco Mercaldo2 & Antonella Santone2 was presented to the participants.
1 CNR
2 University of Molise, Italy
What is AI4EIoTs 2020?
Artificial Intelligence and Machine Learning applications in IoT domain are hardened by several factors as noisy and decentralized data,a different ownership of these data, and security and privacy concerns, that can limit the data provisioning. AI/ML systems needs to be trained of large amounts of data but the lack of a well-trained AI/ML model causes a low interest toward these systems, thus generating a sort of deadlock condition.This special session aims to collect novel proposals for addressing existing criticalities and to highlight emerging challenges in applying AI/ML to IoT systems. We invite international researchers and practitioners to submit papers describing original work, experiences, or vision related to the entire lifecycle of emerging IoT systems powered by AI and ML.
What is IoTBDS 2020?
The internet of things (IoT) is a platform that allows a network of devices (sensors, smart meters, etc.) to communicate, analyse data and process information collaboratively in the service of individuals or organisations. The IoT network can generate large amounts of data in a variety of formats and using different protocols which can be stored and processed in the cloud. The conference looks to address the issues surrounding IoT devices, their interconnectedness and services they may offer, including efficient, effective and secure analysis of the data IoT produces using machine learning and other advanced techniques, models and tools, and issues of security, privacy and trust that will emerge as IoT technologies mature and become part of our everyday lives.
Big Data (BD) has core values of volume, velocity, variety and veracity. After collecting much data from IoT, BD can be jointly used with machine learning, AI, statistical and other advanced techniques, models and methods, which can create values for people and organizations adopting it, since forecasting, deep analysis and analytics can help identify weaknesses and make improvements based on different analysis.
Maintaining a high level of security and privacy for data in IoT are crucial and we welcome recommendations, solutions, demonstrations and best practices for all forms of security and privacy for IoT and BD.
