A modular sampling framework for flexible traffic analysis

Silva, João Marco C.; Carvalho, Paulo; Lima, Solange Rito

doi:10.1109/softcom.2015.7314061

Cited by 5 publications

(3 citation statements)

References 16 publications

(18 reference statements)

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“…The authors of [ 22 , 23 ] investigated how packet sampling influenced anomaly detection results. Silva et al [ 24 ] proposed a framework for evaluating packet sampling’s effects. They examined the effectiveness of each sampling algorithm and proposed a set of metrics for assessing each sampling technique’s ability to produce a representative sample of the original traffic.…”

Section: Related Workmentioning

confidence: 99%

Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems

Hajj

Azar

Abdo

et al. 2023

Sensors

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With the proliferation of IoT devices, ensuring the security and privacy of these devices and their associated data has become a critical challenge. In this paper, we propose a federated sampling and lightweight intrusion-detection system for IoT networks that use K-meansfor sampling network traffic and identifying anomalies in a semi-supervised way. The system is designed to preserve data privacy by performing local clustering on each device and sharing only summary statistics with a central aggregator. The proposed system is particularly suitable for resource-constrained IoT devices such as sensors with limited computational and storage capabilities. We evaluate the system’s performance using the publicly available NSL-KDD dataset. Our experiments and simulations demonstrate the effectiveness and efficiency of the proposed intrusion-detection system, highlighting the trade-offs between precision and recall when sharing statistics between workers and the coordinator. Notably, our experiments show that the proposed federated IDS can increase the true-positive rate up to 10% when the workers and the coordinator collaborate.

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Section: Related Workmentioning

confidence: 99%

Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems

Hajj

Azar

Abdo

et al. 2023

Sensors

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“…A framework to evaluate the impact of packet sampling over several various tools is presented in [5]. The author discusses the very generic performances of each sampling algorithm.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Impact of Traffic Sampling onAATAC�s DDoS Detection

Roudi�re¹,

Owezarski²

2019

JCSM

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As Distributed Denial of Service (DDoS) attack are still a severe threat for the Internet stakeholders, they should be detected with efficient tools meeting industrial requirements. We previously introduced the AATAC detector, which showed its ability to accurately detect DDoS attacks in real time on full traffic, while being able to cope with the several constraints due to an industrial operation, as time to detect, limited resources for running detection algorithms, detection autonomy for not wasting uselessly administrators' time. However, in a realistic scenario, network monitoring is done using sampled traffic. Such sampling may impact the detection accuracy or the pertinence of produced results. Consequently, in this paper, we evaluate AATAC over sampled traffic. We use five different count-based or time-based sampling techniques, and show that AATAC's resources consumption is in general greatly reduced with little to no impact on the detection accuracy. Obtained results are succinctly compared with those from FastNetMon, an open-source threshold-based DDoS detector.

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“…Articulating the measurement scope, the required information model and the adequate sampling strategy is a major design issue for achieving an encompassing and efficient sampling solution. Thus, a flexible sampling framework has been implemented in order to help in selecting and configuring each sampling technique to be more adjusted to the measurement purpose . The framework allows to combine the sampling components defined earlier and can be applied to both online and offline measurement scenarios.…”

Section: Sampling‐based Measurement Systemsmentioning

confidence: 99%

Inside packet sampling techniques: exploring modularity to enhance network measurements

Silva

Carvalho

Lima

2016

Int J Communication

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Summary Traffic sampling is viewed as a prominent strategy contributing to lightweight and scalable network measurements. Although multiple sampling techniques have been proposed and used to assist network engineering tasks, these techniques tend to address a single measurement purpose, without detailing the network overhead and computational costs involved. The lack of a modular approach when defining the components of traffic sampling techniques also makes difficult their analysis. Providing a modular view of sampling techniques and classifying their characteristics is, therefore, an important step to enlarge the sampling scope, improve the efficiency of measurement systems, and sustain forthcoming research in the area. Thus, this paper defines a taxonomy of traffic sampling techniques resorting to a comprehensive analysis of the inner components of existing proposals. After identifying granularity, selection scheme, and selection trigger as the main components differentiating sampling proposals, the study goes deeper on characterizing these components, including insights into their computational weight. Following this taxonomy, a general‐purpose architecture is established to sustain the development of flexible sampling‐based measurement systems. Traveling inside packet sampling techniques, this paper contributes to a clearer positioning and comparison of existing proposals, providing a road map to assist further research and deployments in the area. Copyright © 2016 John Wiley & Sons, Ltd.

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A modular sampling framework for flexible traffic analysis

Cited by 5 publications

References 16 publications

Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems

Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems

Evaluating the Impact of Traffic Sampling onAATAC�s DDoS Detection

Inside packet sampling techniques: exploring modularity to enhance network measurements

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