AI-Driven Cyber Defense Systems: Strengthening National Security through Intelligent Threat Prediction and Response

Abstract

Artificial intelligence is rapidly transforming national cybersecurity by improving the accuracy, speed, and adaptability of intrusion detection and threat response systems. Traditional security tools rely on static signatures and rule based analysis, which are often unable to detect new or evolving attacks. Recent studies indicate that machine learning driven intrusion detection systems can classify network anomalies more accurately and reduce false positives (Ahmad et al., 2025; Kasongo & Sun, 2020). Deep learning models support real time network behavior analysis and have proven effective for identifying complex threat patterns in Internet of Things and industrial control environments (Lansky et al., 2021; Santoso & Finn, 2023). Research has also shown that ensemble learning and hybrid models offer improved performance in detecting distributed denial of service attacks and advanced malware (Abbas et al., 2022; Lucas et al., 2023). Furthermore, policy frameworks are beginning to recognize artificial intelligence as a strategic asset in national security planning and cyber defense governance (Ahmed, 2024; Ekeneme et al., 2025). This study analyzes current developments in artificial intelligence driven cyber defense systems, identifies research gaps, and proposes a conceptual framework for intelligent threat prediction and automated incident response that can support national cybersecurity resilience.