Behavior-aware cybersecurity using artificial intelligence and cryptographic intelligence

Cyber-attacks become manifested as a series of behavioral patterns, but not as an event, and many current security regimes stay based upon a static detection and fixed trust implementation. Such incongruence restricts their capability to act in a dependable manner in fluctuating and unpredictable threat situations. The existing artificial intelligence-based cybersecurity products mainly focus on the detection performance. Due to this, such systems will still be vulnerable to false positives, erratic reactions, and degradation of performance over time. This paper proposes a behavior-sensitive cybersecurity model that brings together sequential behavioral modelling, risk-adaptive cryptography implementation, and integrity-guaranteed learning in an architecture with closed loops. The temporally structured patterns of activity are considered as behavioral risk, which allows making proportional, not binary, trust decisions. Cryptographic policies are adaptively changed based on the inferenced risk, whereas learning updates are explicitly secured to maintain the model reliability as time goes by. The experimental findings indicate that the proposed framework can obtain a detection accuracy of 96.7% and F 1-score of 96.0, as well as a false positive rate decreased to 3.1%. Moreover, the adaptive response latency is also decreased by a factor of about 20-30% relative to the representative baselines and also enhanced stability in response to adversarial noise. These results indicate behavior-based intelligence.