Evaluation and Mitigation of Adversarial Attacks on IoT Intrusion Detection Systems

• Antonia Mayara de A. da Silva UFC
• Paulo Antonio Leal Rego UFC
• Michel Sales Bonfim UFC

DOI: https://doi.org/10.5753/sbseg.2025.11486

Abstract

Security in IoT networks still faces limitations, especially when attacks target deep learning-based intrusion detection systems (IDS). In this work, four adversarial attack techniques were applied to CNN, LSTM and GRU models, and their accuracy was evaluated after malicious samples were inserted. In a second step, the models were retrained with these samples, learning to recognize the patterns of the attacks, which is an important step in mitigating this type of threat in IoT environments.

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