Anomaly Based Network Intrusion Detection for IoT Attacks using Convolution Neural Network

Anomaly Based Network Intrusion Detection for IoT Attacks using Convolution Neural Network

Sharma, Bhawana (Manipal University Jaipur)
Sharma, Lokesh (Manipal University Jaipur)
Lal, C. (TU Delft Cyber Security)

2022

IoT is widely used in many fields, and with the expansion of the network and increment of devices, there is the dynamic growth of data in IoT systems, making the system more vulnerable to various attacks. Nowadays, network security is the primary issue in IoT, and there is a need for the system to detect intruders. In this paper, we constructed a deep learning CNN model for NIDS and utilized the NSL-KDD benchmark dataset, consisting of four attack classes, for evaluating the model’s performance. We applied the filter method for feature reduction where highly correlated features are dropped. Our 2D-CNN model achieved an accuracy of 99.4% with reduced loss. We also compared the performance of DNN and CNN models in terms of accuracy and other evaluation metrics.

ntrusion Detection System
ML
DL
DNN
CNN
NIDS
HIDS
SVM

http://resolver.tudelft.nl/uuid:afd6b79d-76d2-41e6-9c0c-c580f6f17121

https://doi.org/10.1109/I2CT54291.2022.9824229

IEEE

2023-07-01

978-1-6654-2169-0

Proceedings of the 2022 IEEE 7th International conference for Convergence in Technology (I2CT)

2022 IEEE 7th International conference for Convergence in Technology (I2CT), 2022-04-07 → 2022-04-09, Pune, India

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2022 Bhawana Sharma, Lokesh Sharma, C. Lal