TWINAI DEFENSE: COORDINATED MACHINE LEARNING MODELS FOR DETECTION AND PREVENTION OF IOT BOTNET ACTIVITIES

Abstract

The exponential growth of Internet of Things (IoT) devices has led to a massive increase in interconnectivity, but it has also exposed networks to serious security vulnerabilities such as botnet attacks. These attacks exploit weakly secured IoT devices to create large-scale networks of compromised nodes, capable of launching distributed denial-of-service (DDoS) attacks, data theft, and unauthorized surveillance. To address these challenges, this paper presents TwinAI Defense, a coordinated two-fold machine learning framework designed for both the prevention and detection of IoT botnet activities. The proposed framework integrates a dual-stage learning mechanism. The first stage employs supervised learning models to identify and block malicious traffic patterns before infiltration, focusing on anomaly detection and signature-based classification. The second stage utilizes deep learning architectures—such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN)—to continuously monitor network behavior, detect evolving attack signatures, and adapt to new threats in real time. By combining static and dynamic analysis, the system achieves robust defense against both known and zeroday botnet attacks. Extensive experimentation on benchmark IoT datasets demonstrates that TwinAI Defense significantly improves detection accuracy, reduces false alarm rates, and enhances network resilience compared to existing single-stage models. The results indicate that this two-fold learning strategy not only strengthens proactive security measures but also ensures adaptive protection in continuously evolving IoT environments. The framework thus provides a scalable and intelligent solution for safeguarding nextgeneration IoT ecosystems from complex and coordinated cyber threats