Proceedings ArticleDOI
On using eXtreme Gradient Boosting (XGBoost) Machine Learning algorithm for Home Network Traffic Classification
Iyad Lahsen Cherif,Abdesselem Kortebi +1 more
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TLDR
This work considers a supervised approach, namely eXtreme Gradient Boosting (XGBoost) algorithm, which has never been investigated for TC, and obtains 99.5% accuracy on a dataset containing real flows.Abstract:
Traffic classification (TC) is a fundamental task of network management and monitoring operations. Previous works relying on selected packet header fields (e.g. port numbers) or application layer protocol decoding techniques are becoming increasingly difficult and inefficient when facing encrypted traffic and peer-to-peer flows. In this paper, we address the problem of flow based TC using machine learning (ML) algorithms. Our work considers a supervised approach, namely eXtreme Gradient Boosting (XGBoost) algorithm, which has never been investigated for TC. Performance evaluation results show that we obtain 99.5% accuracy on a dataset containing real flows. Additionally, compared to other ML algorithms, XGBoost is the most accurate one.read more
Citations
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Journal ArticleDOI
Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility.
Wei Chen,Wei Chen,Xinxiang Lei,Rabin Chakrabortty,Subodh Chandra Pal,Mehebub Sahana,Saeid Janizadeh +6 more
TL;DR: In this paper, the authors developed head-cut gully erosion prediction maps using boosting ensemble machine learning algorithms, namely Boosted Tree (BT), Boosted Generalized Linear Models (BGLM), Boost Regression Tree (BRT), Extreme Gradient Boosting (XGB), and Deep Boost (DB).
Journal ArticleDOI
A novel blockchain based framework to secure IoT-LLNs against routing attacks
TL;DR: A layered model of IoT routing security to analyze the vulnerabilities associated with each phase of the routing process is proposed and how to leverage the inherent features of blockchain to enhance routing security in IoT-LLNs is explored.
Proceedings ArticleDOI
A Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification
TL;DR: This work proposes an approach using stacked sparse autoencoder (SSAE) accompanied by de-noising and dropout techniques to improve the robustness of extracted features and prevent the over-fitting problem during the training process.
Proceedings ArticleDOI
A Comparative Study on Contemporary Intrusion Detection Datasets for Machine Learning Research
TL;DR: The paper compares the performance of an ML-based IDS by training it with each of recently published datasets, thereby, analyzing how the choice of a dataset impacts the performance.
Proceedings ArticleDOI
Classification and Sentiment Analysis on Tweets of the Ministry of Health Republic of Indonesia
TL;DR: In this article , the extreme gradient boosting (XGBoost) algorithm was used to classify the tweet topics and analyze the sentiment towards comments made by the public on the tweets of the Ministry of Health of the Republic of Indonesia.
References
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Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Proceedings ArticleDOI
BLINC: multilevel traffic classification in the dark
TL;DR: This work presents a fundamentally different approach to classifying traffic flows according to the applications that generate them, based on observing and identifying patterns of host behavior at the transport layer and demonstrates the effectiveness of this approach on three real traces.
Journal ArticleDOI
A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification
TL;DR: The performance impact of feature set reduction, using Consistency-based and Correlation-based feature selection, is demonstrated on Na naïve Bayes, C4.5, Bayesian Network and Naïve Bayes Tree algorithms.
Journal ArticleDOI
Issues and future directions in traffic classification
TL;DR: The persistently unsolved challenges in the field over the last decade are outlined, and several strategies for tackling these challenges are suggested to promote progress in the science of Internet traffic classification.