immUNITY: Detecting and Mitigating Low Volume & Slow Attacks with Programmable Switches and SmartNICs
- URL: http://arxiv.org/abs/2603.20573v1
- Date: Sat, 21 Mar 2026 00:06:45 GMT
- Title: immUNITY: Detecting and Mitigating Low Volume & Slow Attacks with Programmable Switches and SmartNICs
- Abstract summary: Up to 71% of flows contain suspicious behaviors indicative of low-volume network attacks such as port scans.<n>We extend prior work that tracks heavy hitter flows to also detect low-volume and slow attacks by combining the capabilities of both switches and SmartNICs.<n>We develop a dataplane based protocol to rapidly coordinate data structure updates between these devices.
- Score: 6.281749009259195
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Our analysis of recent Internet traces shows that up to 71% of flows contain suspicious behaviors indicative of low-volume network attacks such as port scans. However, distinguishing anomalous traffic in real time is challenging as each attack flow may comprise only a few packets. We extend prior work that tracks heavy hitter flows to also detect low-volume and slow attacks by combining the capabilities of both switches and SmartNICs. We flip the usual design approach by proposing an efficient filter data structure used to quickly route traffic marked as benign towards destination end-systems. We make careful use of limited programmable switch memory and pipeline stages, and complement them with SmartNIC resources to analyze the remaining traffic that may be anomalous. Using machine learning classifiers and intrusion detection rules deployed on the SmartNIC, we identify malicious source IPs, which then undergo more detailed forensics for attack mitigation. Finally, we develop a dataplane based protocol to rapidly coordinate data structure updates between these devices. We implement immUNITY in a testbed with Tofino v1 switch and Bluefield 3 SmartNIC, demonstrating its high accuracy, while minimizing traffic that's analyzed outside the switch.
Related papers
- Autonomous Cyber Defense: Real-Time Attack Detection and Mitigation in Software-Defined Networks Using Machine Learning [78.29352305480285]
In recent intrusions, the interval between initial access and the first lateral movement has been observed to be as short as 27 seconds.<n>This paper presents a closed-loop framework that detects and blocks attacks in software-defined networks without operator involvement.
arXiv Detail & Related papers (2026-08-22T18:38:39Z) - GETA: Generalized Encrypted Traffic Analysis [0.10923877073891443]
GETA is a protocol-agnostic framework for encrypted traffic analysis.<n>It avoids reliance on packet payloads or header semantics.<n> GETA consistently outperforms state-of-the-art baselines.
arXiv Detail & Related papers (2026-05-29T13:09:35Z) - Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks [77.17664010626726]
We focus on the routing with multiple UAV clusters in low-altitude intelligent networks (LAINs)<n>To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques.<n>We show that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks.
arXiv Detail & Related papers (2026-02-27T04:30:35Z) - Video Object Recognition in Mobile Edge Networks: Local Tracking or Edge Detection? [57.000348519630286]
Recent advances in mobile edge computing have made it possible to offload-intensive object detection to edge servers equipped with high-accuracy neural networks.<n>This hybrid approach offers a promising solution but introduces a new challenge: deciding when to perform edge detection versus local tracking.<n>We propose the LTED-Ada in single-device setting, a deep reinforcement learning-based algorithm that adaptively selects between local tracking and edge detection.
arXiv Detail & Related papers (2025-11-25T04:54:51Z) - CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
arXiv Detail & Related papers (2025-05-14T13:37:07Z) - NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics [72.95483148058378]
We propose to pre-train a general-purpose machine learning model to capture traffic dynamics with only traffic data from NetFlow records.<n>We address challenges such as unifying network feature representations, learning from large unlabeled traffic data volume, and testing on real downstream tasks in DDoS attack detection.
arXiv Detail & Related papers (2024-12-30T00:47:49Z) - CyberSentinel: Efficient Anomaly Detection in Programmable Switch using Knowledge Distillation [0.0]
CyberSentinel is a high throughput and accurate anomaly detection system deployed entirely in the programmable switch data plane.<n>To detect unseen network attacks, CyberSentinel uses a novel knowledge distillation scheme that incorporates "learned" knowledge of deep unsupervised ML models.<n>We implement a prototype of CyberSentinel on a testbed with an Intel Tofino switch and evaluate it on various real-world use cases.
arXiv Detail & Related papers (2024-12-21T16:35:44Z) - Exploring Highly Quantised Neural Networks for Intrusion Detection in
Automotive CAN [13.581341206178525]
Machine learning-based intrusion detection models have been shown to successfully detect multiple targeted attack vectors.
In this paper, we present a case for custom-quantised literature (CQMLP) as a multi-class classification model.
We show that the 2-bit CQMLP model, when integrated as the IDS, can detect malicious attack messages with a very high accuracy of 99.9%.
arXiv Detail & Related papers (2024-01-19T21:11:02Z) - RIDE: Real-time Intrusion Detection via Explainable Machine Learning
Implemented in a Memristor Hardware Architecture [24.824596231020585]
We propose a packet-level network intrusion detection solution that makes use of Recurrent Autoencoders to integrate an arbitrary-length sequence of packets into a more compact joint feature embedding.
We show that our approach leads to an extremely efficient, real-time solution with high detection accuracy at the packet level.
arXiv Detail & Related papers (2023-11-27T17:30:19Z) - Network-Aware AutoML Framework for Software-Defined Sensor Networks [3.050185169010028]
We propose a network-aware automated machine learning (AutoML) framework which detects DDoS attacks in software-defined sensor networks.
Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments.
arXiv Detail & Related papers (2023-10-19T17:07:32Z) - Correlating sparse sensing for large-scale traffic speed estimation: A
Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging.
We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
arXiv Detail & Related papers (2022-10-21T07:25:57Z) - Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT
Philosophy [63.91005999481061]
A practical long-term tracker typically contains three key properties, i.e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism.
We propose a two-task tracking frame work (named DMTrack) to achieve distractor-aware fast tracking via Dynamic convolutions (d-convs) and Multiple object tracking (MOT) philosophy.
Our tracker achieves state-of-the-art performance on the LaSOT, OxUvA, TLP, VOT2018LT and VOT 2019LT benchmarks and runs in real-time (3x faster
arXiv Detail & Related papers (2021-04-25T00:59:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.