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A Feature-Rich Embedded NIDS with eBPF/XDP: Detector and Architecture Trade-offs

eBPF/XDP-based NIDS with Isolation Forest reaches 0.965 live F1 on DDoS replay; gRPC microservices match monolithic accuracy within 2ms overhead.

The paper presents a DDoS-focused network intrusion detection system for transport networks built with Ericsson, combining a statistical baseline with an Isolation Forest trained on flow features from GoFlowMeter, an open-source Go implementation of CICFlowMeter, plus eBPF/XDP kernel-level traffic filtering. On a Raspberry Pi 5 testbed replaying CIC-DDoS2019 as real traffic, the Isolation Forest achieves 0.965 recall/F1 live in the monolithic variant, catching low-volume attack windows the baseline misses. gRPC microservices nearly match monolithic accuracy adding under 2ms per window, while the Kafka pipeline trails by roughly nine percentage points and adds about 27ms.

arXiv cs.CR · 4d agoResearch

Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS

Researchers propose DriftXpert, a concept-drift-adaptive network intrusion detection system validated on enterprise networks, addressing degraded AI-based NIDS performance in dynamic traffic.

AI-based network intrusion detection systems assume static data distributions and degrade under concept drift, raising false positives in dynamic environments. DriftXpert uses a two-stage offline framework: an unsupervised latent-manifold anomaly metric to detect traffic drift, and representation consistency alignment with cross-epoch neuron weight aggregation and selective freezing to transfer knowledge without catastrophic forgetting. Experiments on public datasets and a real enterprise network show effective adaptation to drifted data while retaining known-attack detection.

arXiv cs.CR · 7d agoResearch

Retrospectively Reverse-Engineering Apple's Neural Engine

A developer reverse-engineers Apple's M1 Neural Engine architecture, mapping compute cores, MAC datapaths, and schedulers to explain the NPU's decline as transformers displaced CNN workloads.

A developer who previously maintained a reverse-engineered Linux driver for Apple's Neural Engine (ANE) published a retrospective deep dive mapping the M1 ANE's full internal architecture: compute, datapath, scheduler, memory, and execution model. The M1 ANE has 16 compute cores with 128 FP16 (or 256 INT8) MAC lanes each, totaling 2048 parallel MAC lanes, using 32-bit Q16.16 fixed-point accumulation with FP16 readout and an accumulator that saturates at 2^15. The author argues the ANE's dataflow was architected around the predictable reuse patterns of 2017-era CNN workloads (dating to the A11 Bionic), which autoregressive transformer decode broke, limiting its usefulness for general ML. With Apple's M5 folding ANE cores into GPU cores to tout LLM performance, the post frames this as the beginning of the end for the standalone NPU.

Hacker News · AI · 3d agoAI researchHN 54↑ · 7 comments2

Few-Shot Learning for Network Intrusion Detection: Methods, Datasets, and Performance

A PRISMA-style review of 21 few-shot learning studies for network intrusion detection finds meta-learning and CNNs dominant and evaluation inconsistently reported.

The systematic review screened 1,358 records from ACM Digital Library, IEEE Xplore, and Scopus covering 2022-2026 and retained 21 studies on few-shot learning for network intrusion detection. Meta-learning (8 studies) and convolutional neural networks (10) are the most common approaches, while CIC-IDS2017 and CSE-CIC-IDS2018 are the most frequently used datasets. Most evaluations use five or fewer samples per class, and missing parameters and source code limit reproducibility and direct comparison.

arXiv cs.CR · 5d agoResearch1