Seyed Mohammad Mehdi Mirnajafizadeh

arXiv2026

Toward Intelligent Networks via AI-Aware GPU-Native Packet Processing

The integration of inline Artificial Intelligence (AI) models into critical network infrastructure is fundamentally bottlenecked by the high latency and synchronization overhead of CPU-mediated packet processing. While legacy GPU offload and recent CPU-bypass frameworks attempt to bridge this gap, they remain trapped in proprietary ecosystems or still rely on the host CPU and coarse-grained batching to coordinate stateful telemetry and AI pipeline execution. In this paper, we propose AGP, a novel framework that promotes the GPU from a passive accelerator to a primary data-path controller, answering what changes architecturally when the GPU autonomously owns the complete packet-to-inference pipeline. By enabling GPU-native packet processing, contention-free in-GPU stateful aggregation, and a persistent mega-kernel for continuous AI inference, AGP removes the CPU from the critical path. Our evaluation demonstrates that native in-GPU packet processing sustains line-rate throughput while being over 6.1x more power-efficient. Furthermore, our integrated Intrusion Detection System (IDS) stress test eliminates the legacy CPU-GPU synchronization tax, reducing end-to-end latency by 7.9x (up to 35x p99) and accelerating whole-system inference throughput by 9.3x using only ~2% of the GPU's thread capacity.

cs.NI↗

arXiv2025

Enhancing Resiliency of Sketch-based Security via LSB Sharing-based Dynamic Late Merging

With the exponentially growing Internet traffic, sketch data structure with a probabilistic algorithm has been expected to be an alternative solution for non-compromised (non-selective) security monitoring. While facilitating counting within a confined memory space, the sketch's memory efficiency and accuracy were further pushed to their limit through finer-grained and dynamic control of constrained memory space to adapt to the data stream's inherent skewness (i.e., Zipf distribution), namely small counters with extensions. In this paper, we unveil a vulnerable factor of the small counter design by introducing a new sketch-oriented attack, which threatens a stream of state-of-the-art sketches and their security applications. With the root cause analyses, we propose Siamese Counter with enhanced adversarial resiliency and verified feasibility with extensive experimental and theoretical analyses. Under a sketch pollution attack, Siamese Counter delivers 47% accurate results than a state-of-the-art scheme, and demonstrates up to 82% more accurate estimation under normal measurement scenarios.

cs.CR↗