Title: An optimised Darknet traffic detection system using modified locally connected CNN - BiLSTM network

Authors: Abdullah Abdul Sattar Shaikh; M.S. Bhargavi; C. Pavan Kumar

Addresses: Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, Karnataka, India ' Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, Karnataka, India ' Department of Computer Science and Engineering, Indian Institute of Information Technology Dharwad, Karnataka, India

Abstract: The contents of the darkweb have always been a major breach of security and privacy. Due to its anonymous nature, detection of traffic from Darknet becomes difficult. A robust classifier system that accurately predicts and classifies such traffic is a necessity. This research work aims to study the effects of the convolutional-long-short-term memory (CNN-LSTM) system of classification of Darknet through various deep layer modifications on the Nadam optimiser. Experimentations were carried out on different combinations of locally-connected CNNs (LcCNN) and bi-directional LSTM (BiLSTM) to improve accuracy. Data was subjected to various levels of synthetic minority oversampling techniques (SMOTE) to reduce overfitting, data imbalance and achieve better generalisation. A custom decaying call-back function implemented, cut down the learning rate by half and tended to improve accuracy. Results obtained outperformed the base CNN-LSTM system for traffic categorisation with an improved accuracy of 92.57% from 89% using the custom LcCNN-BiLSTM architecture.

Keywords: Darknet; deep learning; convolutional neural network; CNN; bi-directional long short term memory; BiLSTM.

DOI: 10.1504/IJAHUC.2023.131361

International Journal of Ad Hoc and Ubiquitous Computing, 2023 Vol.43 No.2, pp.87 - 96

Received: 17 Aug 2022
Accepted: 13 Oct 2022
Published online: 07 Jun 2023 *

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