Dear all,

We are submitting a paper “A Novel Approach for Network Intrusion Detection using Multistage Deep Learning Image Recognition” to the journal Electronics MDPI. The paper’s abstract is below. If accepted we will acknowledge SPARTA.

The authors are Jevgenijus Toldinas, Algimantas Venčkauskas, Robertas Damaševičius, Šarūnas Grigaliūnas, Nerijus Morkevičius, Edgaras Baranauskas.

All the best,

Algimantas Venčkauskas

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Abstract: The current rise in hacking and computer network attacks throughout the world has heightened the demand for improved intrusion detection and prevention solutions. The intrusion detection system (IDS) is critical in identifying abnormalities and assaults on the network, which have grown in size and pervasiveness. The paper proposes a novel method for network intrusion detection using multistage deep learning image recognition. The dataset network features are normalized and transformed into four-channel (Red, Green, Blue, and Alpha) images. The images are used to train and test the pre-trained deep learning model ResNet50. The proposed approach is evaluated using two benchmark datasets, UNSW-NB15 and BOUN Ddos. On the UNSW-NB15 dataset, the proposed method achieved 99.8% accuracy in the detection of the Generic attack. On the BOUN DDos dataset, the suggested method achieves 99.7% accuracy in the detection of the DDos attack and 99.7% accuracy in the detection of the normal traffic.