Towards a Network Intrusion Classification System using Machine Learning - Almeida, Luna dos Santos; Spredemann, Fabiola; Morales, Analucia Schiaffino; Rodrigues-Filho, Roberto; Panisson, Alison R. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
With the expansion of the Internet and digital transformation, cybersecurity threats have increased, including attacks such as Denial of Service (DoS), ransomware, phishing, and trojans. Intrusion Detection Systems (IDS) play a crucial role in mitigating these threats, often leveraging artificial intelligence for enhanced detection. This study proposes a machine learningbased approach for cyber-attack classification, designed for integration into IDS. Using a multilayer neural network, the model successfully classified nine attack types and normal network activities, achieving an accuracy of 90.49% through optimized intermediate layer configurations.