Adaptive Resilience in Business Transactions: Mitigating ML-Orchestrated Cyber Threats via Cryptographic and Behavioural Defence Walls

Business intelligence (BI) systems increasingly rely on data-driven architectures to facilitate informed financial and operational decisions. However, the integration of advanced algorithms into these platforms has also enabled malicious actors to deploy sophisticated, machine-learning (ML)-orchestrated cyberattacks that easily bypass traditional security signatures. To address this vulnerability, this study proposes a hybrid defensive framework combining elliptic-curve cryptography (ECC) and ML to secure the computational and authentication infrastructure underpinning BI transaction pipelines against automated adversarial threats. A mathematical model grounded in mapping theory was developed to facilitate real-time anomaly detection and adaptive threat mitigation. Furthermore, the proposed architecture introduces a continuous user authentication protocol that evaluates login attempts against established behavioural profiles, immediately flagging divergent access patterns for secondary review. For empirical validation, the framework was evaluated using hardware performance counter (HPC) telemetry from the Canadian Institute for Cybersecurity DDoS Attack Dataset (CICEV2023) within a virtualised execution environment. Low-level execution metrics, including CPU cycles, branch instructions, and executed instructions, were analysed as an infrastructure-tier proxy to assess computational exhaustion and instruction diversion under adversarial authentication attacks. Six ML algorithms were evaluated for intrusion detection. The random forest classifier demonstrated superior performance, achieving an accuracy of 81.58%, a specificity of 89.36%, a precision of 80.00%, and an area under the curve (AUC) of 89.29%. The findings indicate that integrating ECC-based cryptographic verification with ML-driven behavioural analysis yields a resilient, multi-layered defence scheme capable of identifying and mitigating infrastructure-level cyber threats in real-time.