ItemConference Paper

A Systematic Approach for Predicting Cybersecurity Attacks in IoT using CNN-LSTM with HABCABO

Title
A Systematic Approach for Predicting Cybersecurity Attacks in IoT using CNN-LSTM with HABCABO
Creator
Rani, M.; Arul Mary Rexy, V.; Halle, Priyanka D; Kaushal, Jyoti; Chauhan, Amit; Basha, H. Anwer
Description
IoT has transformed how devices work together. Now, billions of connected devices may share data across smart homes, energy systems, and environmental monitoring. In Internet of Things ecosystems, rapid IoT expansion has made them very vulnerable, which makes them easy targets for cyberattacks. Hackers can break into IoT devices that don't have enough protection to stop services, steal data, and invade privacy. This paper shows how to use deep learning using CNNs and LSTM networks and the HABCABO optimization algorithm to deal with these new dangers. After careful sequencing, scaling, and noise reduction, filter-based feature selection uses statistical methods to keep the most important information. To get the best detection, the CNN-LSTM model is trained with features that are carefully regulated. The suggested model is more accurate than CNN and LSTM approaches, with an accuracy rate of 98.04 %. These results show that the model can find and stop IoT cybersecurity threats. In conclusion, CNN-LSTM and HABCABO are strong and smart ways to make sure that IoT infrastructure is safe and reliable right now. 2025 IEEE.
Source
3rd IEEE International Conference on Data Science and Network Security, ICDSNS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
cybersecurity attacks; internet of things (IOT); long short-term memory (LSTM)
Coverage
Rani M., Kumaraguru College of Technology and Business School, Coimbatore, India; Arul Mary Rexy V., Simats Kuthambakkam, Saveetha College of Liberal Arts and Sciences, Department of Commerce, Chennai, India; Halle P.D., Skn Sinhgad Institute of Technology and Science, Department of Information Technology, Lonavala, Pune, India; Kaushal J., Geetanjali Institute of Technical Studies, Department of Computer Science and Engineering, Udaipur, India; Chauhan A., School of Sciences, Christ (Deemed to Be University), Department of Life Sciences, Bengaluru, India; Basha H.A., Saveetha Institute of Medical and Technical Sciences, Saveetha College of Liberal Arts and Sciences, Department of Computer Science, Chennai, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833153679-4;
Format
online
Language
English
Type
Conference paper

Citation

Rani, M.; Arul Mary Rexy, V.; Halle, Priyanka D; Kaushal, Jyoti; Chauhan, Amit; Basha, H. Anwer, “A Systematic Approach for Predicting Cybersecurity Attacks in IoT using CNN-LSTM with HABCABO,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25974.

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