A Perspective of Machine Learning Approach for the Packet Classification in the Software-Defined Network

Indira, B.; Valarmathi, K.

doi:10.32604/iasc.2020.010114

Cited by 15 publications

(2 citation statements)

References 18 publications

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“…has been achieved in a KDN scenario with high classification accuracy [128]. Some have classified packets based on the action/flow of each packet in KDNs using support vector machines, where five features of the IP header have been extracted before classification [129]. In a study that investigated the performance of the SVM supervised machine learning model and the K-means clustering unsupervised machine learning model for traffic classification, SVM yielded a higher classification accuracy than K-means [130].…”

Section: Generating Knowledge Using Machine Learning Methodsmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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Section: Generating Knowledge Using Machine Learning Methodsmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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“…Data transmitted in the network will face various kinds of attacks, such as Data Leakage [l1], Data Tampering [12], Data Replay [13]. Meanwhile, the development of machine learning in packet analysis makes the network transmission based on encryption face greater risks [14]. Therefore, limiting the number of data exposed to attackers will be an effective way to mitigate these attacks.…”

Section: Related Workmentioning

confidence: 99%

Polymorphic Path Transferring for Secure Flow Delivery

2021

KSII TIIS

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In most cases, the routing policy of networks shows a preference for a static one-to-one mapping of communication pairs to routing paths, which offers adversaries a great advantage to conduct thorough reconnaissance and organize an effective attack in a stress-free manner. With the evolution of network intelligence, some flexible and adaptive routing policies have already proposed to intensify the network defender to turn the situation. Routing mutation is an effective strategy that can invalidate the unvarying nature of routing information that attackers have collected from exploiting the static configuration of the network. However, three constraints execute press on routing mutation deployment in practical: insufficient route mutation space, expensive control costs, and incompatibility. To enhance the availability of route mutation, we propose an OpenFlow-based route mutation technique called Polymorphic Path Transferring (PPT), which adopts a physical and virtual path segment mixed construction technique to enlarge the routing path space for elevating the security of communication. Based on the Markov Decision Process, with considering flows distribution in the network, the PPT adopts an evolution routing path scheduling algorithm with a segment path update strategy, which relieves the press on the overhead of control and incompatibility. Our analysis demonstrates that PPT can secure data delivery in the worst network environment while countering sophisticated attacks in an evasion-free manner (e.g., advanced persistent threat). Case study and experiment results show its effectiveness in proactively defending against targeted attacks and its advantage compared with previous route mutation methods.

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Research on Cotton Impurity Detection Algorithm Based on Image Segmentation

Yang

Diao

2021

Lecture Notes in Computer Science

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A Perspective of Machine Learning Approach for the Packet Classification in the Software-Defined Network

Cited by 15 publications

References 18 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

Polymorphic Path Transferring for Secure Flow Delivery

Research on Cotton Impurity Detection Algorithm Based on Image Segmentation

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