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Distributed intelligence on IoT edge has been studied to analyze traffic, but introduces delays and raises privacy concerns. Federated learning can address privacy concerns, but does not meet latency requirements. In this paper, we propose FLIP4: an efficient federated learning-based framework for in-network traffic analysis. FLIP4 consumes fewer resources than previous solutions and reduces communication overheads, making it well-suited for IoT edge traffic analysis.

Low-latency detection of fraudulent transactions in real-time is highly important as it enables rapid identification and prevention. In this paper, we introduce MIND, conducting ML-based fraud detection within programmable devices. MIND is prototyped on both software and hardware network devices and experimental results demonstrate that MIND detects transaction fraud in real-time, with a throughput of 6.4 terabits per second and microsecond-scale latency.

In this paper, we design and develop a solution that supports both stock mid-price and volatility movement forecasting using commodity switches. Additionally, our solution adapts a hybrid deployment strategy by combining network hardware and servers. Our approach achieves microsecond-scale, ultra-low latency, compared to previous works, while upholding the same level of ML performance as server models.

Decentralized energy transactions within untrusted and non-transparent energy markets in modern Smart Grids expose vulnerabilities and are susceptible to attacks. This paper proposes GridWatch, an effective real-time in-network intelligent framework to detect false data injection attacks.

Using programmable network devices to aid in-network machine learning has been the focus of significant research. This work presents Planter, an open-source, modular framework for mapping trained machine learning models to programmable devices. Planter supports a wide range of machine learning models, multiple targets and can be easily extended.

Deliverable 4.1 is the first iteration of the Dynamic Swarm Networking technical Work Package. The Work Package combines three tasks: (1) Automatic discovery and dynamic network swarm formation (2) Embedded network security and isolation and (3) Hardware accelerated in-network operations.

This work presents IIsy, which implements machine learning classification models in a hybrid fashion using off-the-shelf network devices. Besides a range of traditional and ensemble machine learning models, IIsy also supports hybrid classification, achieving near-optimal classification results, while significantly reducing latency and load on the servers.

In-network caching expedites data retrieval by storing frequently accessed data items within programmable data planes, thereby reducing data access latency. In this paper, we introduce In-network Caching Shelter (INCS), an in-network machine learning solution implemented on NVIDIA BlueField-2 DPU to mitigate the effect of bots’ traffic. Our evaluation shows that INCS can detect malicious bot traffic patterns with high accuracy.

In-network ML is a promising technology that provides ML inference services with high throughput, low latency and high power efficiency. This paper provides a holistic review of the fundamentals of in-network ML. It introduces its background and solutions, discusses the existing challenges and open issues, and provides insights for further research explorations.

Research has focused on enabling on-device functionality, with limited consideration to distributed in-network computing. This paper explores the applicability of distributed computing to in-network computing and presents DINC, a framework enabling distributed in-network computing, generating deployment strategies, overcoming resource constraints and providing functionality guarantees across a network.

Current gateways struggle with dynamic IoT traffic and have limited defence capabilities against attacks with changing patterns. In this work, we present P4Pir, a novel in-network traffic analysis framework for IoT gateways. P4Pir pioneer the continuous and seamless updates of in-network inference models within gateways.

Traffic load balancing is a long-time networking challenge. This work presents QCMP, a ReinforcementLearning based load balancing solution implemented within the data plane, providing dynamic policy adjustment with quick response to changes in traffic. Our results show that QCMP requires negligible resources, runs at line rate, and adapts quickly to changes in traffic patterns.

IoT devices are widely and dynamically deployed in diverse use cases, leading to a surge of security threats that were previously overlooked. We propose P4Pir, an in-network traffic analysis solution, to enable continuous learning and consistent ML updates. P4Pir achieves real-time multi-protocol data collection, in-network ML-based attack mitigation, and hitless runtime ML updates.

The rise of IoT-connected devices has led to an increase in collected data for service and traffic analysis, but also to emerging threats and attacks. To address these concerns, we present FLIP4, a distributed in-network attack detection framework based on federated tree models.

Machine learning is driving the evolution of algorithmic trading, but the demands for fast execution speed remain. In this paper, we present LOBIN, providing machine learning based market prediction by building limit order books within programmable switches, using high-frequency market data feeds.

Many trading applications require very short response times, which cannot always be supported by traditional machine learning frameworks. We present Linnet, providing in-network financial market prediction. Linnet demonstrates the potential to predict future stock price movements with high accuracy and low latency.

Traditional hard-coded gateways fail to flexibly process diverse IoT traffic from highly dynamic devices. We present P4Pir, an in-network traffic analysis solution for IoT gateways. Preliminary results show that P4Pir can detect emerging attacks accurately based on retraining and updating the machine learning model.

Using programmable network devices to aid in-network machine learning has been the focus of significant research. This work presents Planter, an open-source, modular framework for mapping trained machine learning models to programmable devices. Planter supports a wide range of machine learning models, multiple targets and can be easily extended.

This work presents IIsy, which implements machine learning classification models in a hybrid fashion using off-the-shelf network devices. Besides a range of traditional and ensemble machine learning models, IIsy also supports hybrid classification, achieving near-optimal classification results, while significantly reducing latency and load on the servers.

Data classification within the network significantly benefits reaction time, servers offload and power efficiency. This work designs an algorithm for efficient mapping of ensemble models, such as XGBoost and Random Forest, to programmable switches. The proposed method overlaps trees within match-action tables, achieves high accuracy and low resource overhead.