A Systematic Approach for Predicting Cybersecurity Attacks in IoT using CNN-LSTM with HABCABO
M. Rani, Arul Mary Rexy, Priyanka Dhondiraj Halle, Jyoti Kaushal, Amit Chauhan, H. Anwer Basha · 2025
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.