Hybrid and multi cloud adoption has made enterprise integration a strategic architecture problem rather than a narrow middleware concern. Large organizations frequently operate SAP S4HANA, SAP ECC, SaaS business applications, partner portals, data lakes, and hyper scaler services across heterogeneous environments. Point-to-point interfaces and legacy enterprise service bus patterns often create tight coupling, duplicated mappings, weak observability, and security gaps. This paper proposes a Hybrid Multi Cloud Integration Modernization Framework, abbreviated as HMC-IMF, for SAP Business Technology Platform centered enterprise integration modernization. The framework combines SAP Integration Suite, API management, event driven integration, clean core extensions, runtime placement, security governance, and observability into a unified modernization model. A synthetic enterprise integration dataset is defined with 186 applications, 2,850 interfaces, 1,120 APIs, 740 event topics, 320 batch flows, five cloud environments, and 24 months of operational logs. Experimental analysis compares a legacy point-to-point baseline with the proposed BTP centered architecture. Results show a 38 percent reduction in interface complexity, a decrease in average integration latency from 420 MS to 245 MS, a reduction in mean time to recovery from 155 minutes to 54 minutes, and a decline in failed message rate from 3.8 percent to 1.1 percent. The findings suggest that SAP BTP can serve as a governed integration modernization layer when paired with explicit pattern selection, policy controls, and operational telemetry. Index Terms SAP BTP, enterprise integration, hybrid cloud, multi-cloud architecture, SAP Integration Suite, API management, event-driven architecture, clean core
Connected aftermarket devices extend vehicle diagnostics, repair workflows, and over-the-air software maintenance beyond original equipment manufacturer boundaries, yet their heterogeneous ownership and long service life complicate conventional perimeter security. This paper develops Zero Trust Federated Learning for Connected Aftermarket Devices (ZT FL CADE), an edge-learning architecture that combines device-level access control, privacy-preserving federated learning, and adversarial validation for over-the-air update and predictive maintenance decisions. The evaluation uses a single synthetic dataset of 144,000 telemetry windows from 240 devices, 12 vendor domains, and 180 days of operation. Features include bus entropy, update latency, attestation age, signature retries, environmental signals, fault-code rates, packet loss, drift, mileage, and trust score, with targets for maintenance risk, update intrusion, and access action. ZT FL CADE trains local temporal models, aggregates privacy-bounded updates, scores each device against behavioral and update integrity evidence, and routes update requests to allow, challenge, or quarantine actions. Synthetic experiments improve maintenance risk F1 from 0.837 for Fed Avg to 0.883, improve intrusion F1 from 0.856 to 0.897, and preserve 0.842 intrusion recall when 20 percent of selected clients are adversarial. Mean access-decision latency remains 44 MS, below the 100 MS operational budget used in the simulation. The results do not establish field validation, but they indicate that zero-trust policy enforcement and federated learning can be evaluated jointly rather than as separate aftermarket security controls. Index Terms Zero trust architecture, federated learning, connected aftermarket devices, over-the-air updates, adversarial machine learning, edge artificial intelligence, predictive maintenance, automotive cybersecurity
Shunmukha Sagar Puppala· 0 citations
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