Carpet-bombing DDoS attacks spread traffic almost uniformly across a destination prefix, keeping each host below alarm thresholds while exhausting prefix-level resources; spoofed sources make source-side evidence unreliable. Existing detectors either decide on windows of seconds, alarming only at window boundaries, or depend on supervised training, so short attacks often end before the first alarm. We present SweepSketch, a destination-anchored, per-packet sketch detector that keeps no source-side state, needs no training, and alarms while the attack is still in progress. Its core is a tagged self-cleaning windowed HyperLogLog (T-HLL) that turns uniformity testing into one event per distinct destination per window; per-packet cumulative-sum (CUSUM) decisions and dual exponentially weighted moving average (EWMA) gates form the decision chain. The reference state occupies 512 KB, and a bit-exact C++ implementation reaches 13.2 Mpps per core. On attacks lasting only 0.5 s, it raises its first alarm before the attack ends in 95.6% of attacks; on real captures, 49.3% alarm within the first second; static and mixed-traffic F1 both reach 0.991. SweepSketch is deployed in two hardware forms: a Tofino2 program carries the complete per-packet state chain and places the final gates on the control plane, while a DPU micro-engine keeps the entire decision chain in the fast path.
Knowledge editing aims to modify outdated knowledge in language models efficiently while retaining their original capabilities. Mainstream datasets for knowledge editing are predominantly static and fail to keep in pace with the evolving real-world knowledge. In this work, we introduce CRAFT, an ever-evolving real-world dataset for knowledge editing. It evaluates models on temporal locality, common-sense locality, composite portability and alias portability, providing a comprehensive and challenging evaluation for knowledge editing, on which previous methods hardly achieve balanced performance. Towards flexible real-time knowledge editing, we propose KEDAS, a novel paradigm of knowledge editing alignment featuring diverse edit augmentation and self-adaptive post-alignment inference, exhibiting significant performance gain on both CRAFT and traditional datasets compared to previous methods. We hope this work may serve as a catalyst for shifting the focus of knowledge editing from static update to dynamic evolution.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表