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: Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional, 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.