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

Bhawana Sharma, Lokesh Sharma, Chhagan Lal

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE 7th International conference for Convergence in Technology (I2CT)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)978-1-6654-2168-3
ISBN (Print)978-1-6654-2169-0
DOIs
Publication statusPublished - 2022
Event2022 IEEE 7th International conference for Convergence in Technology (I2CT) - Pune, India
Duration: 7 Apr 20229 Apr 2022
Conference number: 7th

Conference

Conference2022 IEEE 7th International conference for Convergence in Technology (I2CT)
Abbreviated titleI2CT 2022
Country/TerritoryIndia
CityPune
Period7/04/229/04/22

Keywords

  • ntrusion Detection System
  • ML
  • DL
  • DNN
  • CNN
  • NIDS
  • HIDS
  • SVM

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