ItemConference Paper

An Algorithm for Cybersecurity Threats Detection in the Internet of Things using Deep Learning Approach

Title
An Algorithm for Cybersecurity Threats Detection in the Internet of Things using Deep Learning Approach
Creator
Mahnot, Saksham; George, Jossy P; Paul Alapatt, Bosco
Description
We perform research to develop a combined deep learning algorithm that enhances security threat detection within the Internet of Things networks. The resource variations across IoT devices create obstacles for Traditional Intrusion Detection Systems (IDSs) regarding their scalability and adaptability elements. This study explores the application of Bidirectional Recurrent Neural Networks and Long Short-Term Memory networks, which are trained on Traffic data records from NSL-KDD, a widely recognized benchmark dataset. It's a secondary dataset. This dataset is preprocessed and features are engineered to be optimized for sequential pattern recognition and handling of long-term dependency. Experimental results validate the achievement of a cross-validation accuracy of 93.40%, F1 is 91.62% and precision is 90.42%, which is greater than the individual models, such as CNN, BiRNN, or LSTM. The stacking Models Bi-RNN sequential learning and LSTM dependency retention makes the system perform better at threat classification along with elevated detection accuracy for IoT-related security issues like DoS, Probe, R2L, and U2R. The consistent performance of the model through this validation split provides evidence that the system can effectively handle IoT cybersecurity threats. 2025 IEEE.
Source
Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.1464-1471
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Bidirectional Recurrent Neural Networks; CNN; Cyber Security; Internet of Things; Long Short-Term Memory
Coverage
Mahnot S., CHRIST University, India; George J.P., CHRIST University, India; Paul Alapatt B., CHRIST University, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833151175-3;
Format
online
Language
English
Type
Conference paper

Citation

Mahnot, Saksham; George, Jossy P; Paul Alapatt, Bosco, “An Algorithm for Cybersecurity Threats Detection in the Internet of Things using Deep Learning Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/26022.

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