JUCS - Journal of Universal Computer Science 26(11): 1422-1434, doi: 10.3897/jucs.2020.075

Detection of Cyberattacks Traces in IoT Data

Vibekananda Dutta^‡, Michał Choraś^§, Marek Pawlicki^‡, Rafał Kozik^|

‡ UTP University of Science and Technology, Bydgoszcz, Poland§ University of Science and Technology, Bydgoszcz, Poland| TP University of Science and Technology, Bydgoszcz, Poland

Corresponding author: Vibekananda Dutta ( dutta.vibekananda@utp.edu.pl )

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.

Citation: Dutta V, Choraś M, Pawlicki M, Kozik R (2020) Detection of Cyberattacks Traces in IoT Data. JUCS - Journal of Universal Computer Science 26(11): 1422-1434. https://doi.org/10.3897/jucs.2020.075

Abstract

Artificial Intelligence plays a significant role in building effective cybersecurity tools. Security has a crucial role in the modern digital world and has become an essential area of research. Network Intrusion Detection Systems (NIDS) are among the first security systems that encounter network attacks and facilitate attack detection to protect a network. Contemporary machine learning approaches, like novel neural network architectures, are succeeding in network intrusion detection. This paper tests modern machine learning approaches on a novel cybersecurity benchmark IoT dataset. Among other algorithms, Deep AutoEncoder (DAE) and modified Long Short Term Memory (mLSTM) are employed to detect network anomalies in the IoT-23 dataset. The DAE is employed for dimensionality reduction and a host of ML methods, including Deep Neural Networks and Long Short-Term Memory to classify the outputs of into normal/malicious. The applied method is validated on the IoT-23 dataset. Furthermore, the results of the analysis in terms of evaluation matrices are discussed.

Keywords

cybersecurity, anomaly detection, neural networks, dimensionality reduction, deep learning