IoT-based Network Intruder Detection and Cyber Attack Prediction System
B. Kalaiselvi, P. Kathiravan, V Kaviyarasan, E. Sabarinathan, S Sudharsan · 2025
In real world, cyber-attacks increasing in sophistication and frequency, and targeting governments, organizations, and individuals. Conventional security methods are unable to keep up with changing attacks like malware, phishing, and ransomware. Artificial Intelligence (autonetics) and Algorith now take center stage in detecting threats in real-time, incident response automation, and proactive defense measures. Autonetics-driven systems, which leverage the capabilities of next-generation artificial intelligence and machine learning, are a significant leap in the field of cybersecurity since they enable real-time threat detection and mitigation. These systems thoroughly analyze vast volumes of data to identify anomalies and emerging patterns, allowing organizations to detect cyber threats on the fly rather than waiting until damage has already been caused. By automating the monitoring and response mechanism, autonetics-driven solutions protect the confidentiality, integrity, and availability of digital assets, reacting to new attack patterns and reducing the window of vulnerability. This move to more adaptive and smarter cybersecurity allows organizations to outrun increasingly advanced threats, reduce response times, and enjoy robust protection in an evolving digital environment.