Extensions to decision-tree based packet classification algorithms to address new classification paradigms

Stimpfling, Thibaut; Belanger, N.; Cherkaoui, Omar; Beliveau, Andre; Beliveau, Ludovic; Savaria, Yvon

doi:10.1016/j.comnet.2017.04.021

Cited by 10 publications

(15 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context, NNs [97][98][99][100][101][102][103][104][105][106], SVM [105][106][107][108][109][110][111][112][113], DTs [114][115][116][117][118][119][120][121][122][123][124][125][126][127][128], ensemble methods [129][130][131][132][133][134][135][136][137][138][139][140] and supervised deep learning [44,[141][142][143][144] approaches were the most used...…”

Section: Supervised Learning In Sdnmentioning

confidence: 99%

Artificial intelligence enabled software‐defined networking: a comprehensive overview

2019

View full text Add to dashboard Cite

Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

show abstract

Section: Supervised Learning In Sdnmentioning

confidence: 99%

Artificial intelligence enabled software‐defined networking: a comprehensive overview

2019

View full text Add to dashboard Cite

show abstract

“…Using machine learning, the results demonstrated that the model is better than other models. Stimpfling et al [46] proposed a method based on the DT algorithm in order to overcome the drawback of memory access as well as the size of the data structure. DT is considered as the optimizing point in the model because it reduces the searching time.…”

Section: Related Workmentioning

confidence: 99%

“…where z can be considered as either the path maximum transmission unit (PMTU) of the destination or previous outstanding data chunk(s) acknowledged (data ACKed). PMTU is the destination PMTU and ACKed is the total size of chunks of the previous outstanding acknowledged data [45][46][47].…”

Section: Enhanced Slow-startmentioning

confidence: 99%

“…Although the performance of IMP-SCTP declined (the rising level in jitter indicates degrading performance), IMP-SCTP was still better than ORG-SCTP. The performance degradations of the latter were evident in the following periods: 13-18 s, 61-69 s, 133-143 s, and 163-172 s. Meanwhile, the lowest performance degradations were observed at 11,20,35,46,59,70,82,92,106,118,144,161,162, and 173 s. Moreover, the jitter utilization of IMP-SCTP increased at 3, 43, 52, 100, 108, and 178-180 s. The performance of IMP-SCTP was better and more stable than that of ORG-SCTP from 10 s to the end of the simulation period, and rarely reached the maximum level. Throughout the 180 s, the behaviors were independent of elapsed time.…”

mentioning

confidence: 95%

See 1 more Smart Citation

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

View full text Add to dashboard Cite

The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

show abstract

“…Data classification is an important task in the field of data mining. Related classification methods and techniques are increasingly extensively studied and some of them have been successfully used to solve practical problems [1,2,3] . In the era of big data, the volume and variety of data is growing and growing.…”

Section: Introductionmentioning

confidence: 99%

A Fast Granular Method for Classifying Incomplete Inconsistent Data

Meng

2017

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Today extracting knowledge from "inferior quality" data that is characterized by incompleteness and inconsistency is an unavoidable and challenging topic in the field of data mining. In this paper, we propose a fast granular method to classify incomplete inconsistent data using attribute-value block technique. Firstly, a granulation model is constructed to provide a foundation for efficient computation. Secondly, an algorithm of acquiring classification rules is proposed and then an algorithm of minimizing rule sets is proposed, and with these proposed algorithms, a classification algorithm is designed to construct a rule-based classifier. Finally, we use the experiment results to illustrate the effectiveness of the proposed algorithms.

show abstract

Extensions to decision-tree based packet classification algorithms to address new classification paradigms

Cited by 10 publications

References 7 publications

Artificial intelligence enabled software‐defined networking: a comprehensive overview

Artificial intelligence enabled software‐defined networking: a comprehensive overview

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

A Fast Granular Method for Classifying Incomplete Inconsistent Data

Contact Info

Product

Resources

About