ISEP – GECAD – Relatórios
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- IoT intrusion detection through machine learningPublication . Vitorino, João; Sousa, Orlando; Praça, Isabel; Andrade, RuiThe digital transformation faces great security challenges. In particular, the Internet of Things (IoT), a concept that expresses the interconnection of physical objects with the Internet, is exposed to several threats. The growing number of cyber attacks targeting IoT systems restates the need for a reliable detection of malicious network activity, to mitigate its impact. The application of Machine Learning (ML) to IoT intrusion detection is a promising approach to tackle the increasingly more complex threats. This project presents a continuously improving Network-based Intrusion Detection System (NIDS) based on user feedback. The system consists of three modular applications and employs an adapted Deep Reinforcement Learning (DRL) methodology to incrementally improve the detection with the alerts validated by end users. The binary and multi-class classification performances of the developed DRL model, a Support Vector Machine (SVM), a Light Gradient Boosting Machine (LightGBM), an Extreme Gradient Boosting (XGBoost), an Isolation Forest (iForest) and a Local Outlier Factor (LOF) were evaluated on several subsets of the IoT-23 dataset. The obtained results indicate that the DRL model requires a large quantity of balanced data to be effective, whereas iForest and LOF are more suitable for a smaller quantity of unbalanced data. Overall, SVM, LightGBM and XGBoost obtained similar results. LightGBM achieved the most reliable performance.
- D7.4 - Proceedings of the Third DREAM-GO Workshop: Intelligent load management in local and wholesale demand response marketsPublication . Barriuso, Alberto L.; Briones, Alfonso González; Lozano, Álvaro; Gazafroudi, Amin Shokri; Iglesia, Daniel H. de la; Sousa, Filipe; Villarrubia, Gabriel; Spínola, João; Revuelta Herrero, Jorge; Paz, Juan F. de; Corchado, Juan Manuel; Venyagamoorthy, Kumar G.; Gomes, Luis; Khorram Ghahfarrokhi, Mahsa; Navarro-Cáceres, María; Abrishambaf, Omid; Faria, Pedro; Castro, Rafael; Silva, Sergio; Coppens, Tom; Vale, ZitaProceedings of the Third DREAM-GO Workshop: Intelligent load management in local and wholesale demand response markets - Deliverable 7.4