On-the-fly traffic classification and control with a stateful SDN approach

Bianco, Andrea; Giaccone, Paolo; Kelki, Seyedaidin; Campos, Nicolas Mejia; Traverso, Stefano; Zhang, Tianzhu

doi:10.1109/icc.2017.7997297

Cited by 16 publications

(11 citation statements)

References 7 publications

(9 reference statements)

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“…(x, center) = exp −γ ||x − center|| 2 (11) where γ determines the influence of the new features on the decision boundaries. Though SVM is effective in highdimensional dataset, the training time for large dataset is high and does not perform well with overlapping classes.…”

Section: The Proposed Methodsmentioning

confidence: 99%

FWFS: Selecting Robust Features Towards Reliable and Stable Traffic Classifier in SDN

Zaki

Chin

2019

IEEE Access

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Real-time Internet traffic flow classification is important in managing network resources in accordance to Quality of Service (QoS) requirements. The centralized network's control in Software Defined Networking (SDN) provides a platform for Internet Service Provider (ISP) to perform specific actions on the classified flows through routing and scheduling. Though machine learning (ML) can be the alternative to Deep Packet Inspection (DPI) in classifying SDN traffic flows, several problems, such as classifier's accuracy, computational complexity, multi-class imbalanced data, and concept drift, need to be addressed in order to have a reliable solution. Therefore, this work has proposed a hybrid filter-wrapper feature selection (FS) algorithm, named Filter-Wrapper Feature Selection (FWFS). The algorithm selects robust features that represent minority classes and resistant to concept drift and is also computationally inexpensive by discarding irrelevant features before further processing with wrapper function. Based on the performance evaluation, the feature selection process of FWFS is computationally inexpensive; i.e. 59.6s, which produces a classifier with an overall accuracy of 98.9%. The result is better than state-of-the-art FS algorithm, Efficient Feature Optimization Approach (EFOA) which requires >400s to select features which can produced a classifier with 97.7% accuracy. In addition to the high overall accuracy, the classifier trained with features selected by FWFS has better F-measure values for each classes including minority classes; i.e. >0.8 in MULTIMEDIA and INTERACTIVE which consist only 0.15% and 0.03% instances, respectively, of the total 377,526 instances in the dataset. Furthermore, the classifier is stable and reliable for classifying new data; i.e. 98.7% accuracy for classifying new data and F-measure of more than 0.8 in every class. The classifier model will be embedded in the SDN-ISP traffic classification solution which provides insights for resource allocations and traffic scheduling in the network.

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Section: The Proposed Methodsmentioning

confidence: 99%

FWFS: Selecting Robust Features Towards Reliable and Stable Traffic Classifier in SDN

Zaki

Chin

2019

IEEE Access

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“…The four solutions found have used similar approaches to deal with the above problem. They exploit the data plane programmability to offload down to the network the process of filtering and counting traffic, previously performed by the DPI application on the SDN Controller.…”

Section: Related Workmentioning

confidence: 99%

A real‐time attack defense framework for 5G network slicing

et al. 2020

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Network Slicing (NS) is a key enabler to support 5G network services on-demand. However, since NS is a result of the recent advancement in Software-Defined Networking and Network Function Virtualization, it introduces new security issues which include attacks against an NS instance within an operator network and interslice security threats. In this scenario, identifying and mitigating attacks in real-time is of paramount importance to improve security aspects. However, it is far from being straightforward. Therefore, this work proposes the FrameRTP4, a P4-based framework that aims to deliver real-time attack detection and mitigation mechanisms in 5G NS scenarios. For this, it provides a P4-based switch that implements an Service Function Chaining protocol layer, an efficient and scalable Access Control List for the detection and mitigation of known attacks, and a monitoring system aiming to reduce the overhead induced on the control channel. Furthermore, it delivers an orchestrator that aims to control all switches in order to enable lifecycle management of NS instances and P4 table rules. Besides, it also performs some autonomous tasks such as the wildcard rules generation and the detection of new threats by using machine learning algorithms. Preliminary results point to the potential benefits of FrameRTP4 to be part of a 5G NS infrastructure. K E Y W O R D S5G, bloom filter, cybersecurity, network function virtualization, network slice, P4

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“…Indeed, stateful switches can take local decisions without relying on the intervention of an SDN controller [5]. This fact has many beneficial effects.…”

Section: Arxiv:191203025v2 [Csni] 2 Mar 2020mentioning

confidence: 99%

Optimal State Replication in Stateful Data Planes

Muqaddas

Sviridov

Giaccone

et al. 2020

IEEE J. Select. Areas Commun.

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In SDN stateful data planes, switches can execute algorithms to process traffic based on local states.This approach permits to offload decisions from the controller to the switches, thus reducing the latency when reacting to network events. We consider distributed network applications that process traffic at each switch based on local replicas of network-wide states. Replicating a state across multiple switches poses many challenges, because the number of state replicas and their placement affects both the data traffic distribution and the amount of synchronization traffic among the replicas.In this paper, we formulate the optimal placement problem for replicated states, taking into account the data traffic routing, to ensure that traffic flows are properly managed by network applications, and the synchronization traffic between replicas, to ensure state coherence. Due to the high complexity required to find the optimal solution, we also propose an approximated algorithm to scale to large network instances. We numerically show that this algorithm, despite its simplicity, well approximates the optimal solution. We also show the beneficial effects of state replication with respect to the singlereplica scenario, so far considered in the literature. Finally, we provide an asymptotic analysis to find the optimal number of replicas.

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On-the-fly traffic classification and control with a stateful SDN approach

Cited by 16 publications

References 7 publications

FWFS: Selecting Robust Features Towards Reliable and Stable Traffic Classifier in SDN

FWFS: Selecting Robust Features Towards Reliable and Stable Traffic Classifier in SDN

A real‐time attack defense framework for 5G network slicing

Optimal State Replication in Stateful Data Planes

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