Author(s)

Wafula Maurice Oboya^*, Anthony Waititu Gichuhi, Anthony Wanjoya

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.

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.

Dense Neural Network (DNN), Radial Basis Function Neural Network (RBFNN), Intrusion Detection System (IDS), Denial of Service (DoS), Remote to Local (R2L), User-to-Root (U2R)

Oboya, W. , Gichuhi, A. and Wanjoya, A. (2023) A Hybrid DNN-RBFNN Model for Intrusion Detection System. Journal of Data Analysis and Information Processing, 11, 371-387. doi: 10.4236/jdaip.2023.114019.