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Lin, Y., Zhao, H., Tu, Y., Mao, S. and Dou, Z. (2020) Threats of Adversarial Attacks in DNN-Based Modulation Recognition. IEEE INFOCOM, 2020-IEEE Conference on Computer Communications, Toronto, 6-9 July 2020, 2469-2478.
https://doi.org/10.1109/INFOCOM41043.2020.9155389

has been cited by the following article:

• TITLE: A Hybrid DNN-RBFNN Model for Intrusion Detection System

  AUTHORS: Wafula Maurice Oboya, Anthony Waititu Gichuhi, Anthony Wanjoya

  KEYWORDS: 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)

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.11 No.4, November 1, 2023

  ABSTRACT: 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.