Distributed Batch Processing Architecture for Cross-Platform Abuse Detection at Scale

This paper presents a distributed batch processing architecture for cross-platform abuse detection at scale, addressing the challenges of detecting coordinated malicious activities across heterogeneous online platforms. The proposed architecture integrates platform-specific preprocessing with cross-platform feature normalization through a modular design that separates data acquisition, preprocessing, distributed processing, and result aggregation. We implement a dynamic batching strategy that optimizes computational resource utilization while maintaining detection latency within acceptable bounds. The architecture employs a multi-task learning approach with specialized deep learning models for different abuse types, leveraging platform-aware adversarial encoding to learn platform-independent representations. Performance optimization techniques including adaptive content resizing and model quantization enable efficient execution across diverse hardware environments. Experimental evaluation conducted on a dataset of 3.2 million content items from five major platforms demonstrates that our approach achieves a 12.7 % improvement in cross-platform F1-score compared to platform-specific models, while providing 2.8x higher throughput than naive cross-platform approaches. The architecture's ability to identify coordinated abuse campaigns spanning multiple platforms highlights the value of integrated cross-platform analysis in detecting sophisticated abuse patterns. The implementation successfully balances detection accuracy, processing efficiency, and scalability requirements, providing an effective solution for large-scale abuse detection across diverse online environments.