Intrusion
Detection Systems (IDS) are pivotal in safeguarding computer networks from
malicious activities. This study presents a novel approach by proposing a
Hybrid Dense Neural Network-Radial Basis Function Neural Network (DNN-RBFNN)
architecture to enhance the accuracy and efficiency of IDS. The hybrid model
synergizes the strengths of both dense learning and radial basis function
networks, aiming to address the limitations of traditional IDS techniques in
classifying packets that could result in Remote-to-local (R2L), Denial of
Service (Dos), and User-to-root (U2R) intrusions.
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