Adaptive AI-Based Threat Detection Architecture for Distributed and Scalable Cloud-Native Enterprise Applications
DOI:
https://doi.org/10.15662/IJARCST.2024.0706040Keywords:
adaptive artificial intelligence, threat detection, cloud-native security, enterprise applications, machine learning, anomaly detection, cybersecurity, microservices, distributed systems, behavioral analytics, container security, security automation, threat intelligence, zero-day detectionAbstract
The rapid adoption of cloud-native technologies, microservices, containers, serverless computing, and distributed data platforms has transformed enterprise application environments while simultaneously expanding the cybersecurity attack surface. Traditional security mechanisms based on static rules and predefined signatures are increasingly insufficient for detecting sophisticated, rapidly evolving, and previously unknown threats across highly dynamic infrastructures. This study proposes an adaptive artificial intelligence (AI)-based threat detection architecture designed for distributed and scalable cloud-native enterprise applications. The proposed architecture integrates continuous observability, centralized and distributed telemetry collection, machine learning, behavioral analytics, threat intelligence, automated response, and cloud-native orchestration to provide intelligent and adaptive security monitoring. The architecture establishes a continuous feedback loop in which application, network, identity, container, and infrastructure telemetry are collected, correlated, analyzed, and converted into actionable security intelligence. Machine-learning models are employed for anomaly detection, behavioral profiling, attack classification, and dynamic risk assessment, while automated response mechanisms enable rapid containment of identified threats. The research methodology combines architectural design, experimental implementation, controlled cyber-threat scenarios, and comparative performance evaluation. Key evaluation measures include detection accuracy, precision, recall, F1-score, false-positive rate, detection latency, scalability, resource overhead, and response effectiveness. The proposed approach seeks to demonstrate that integrating adaptive AI capabilities with cloud-native security mechanisms can improve threat detection while maintaining application scalability and operational efficiency.
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