Recent Advancement in Machine Learning Based Internet Traffic Classification

Namdev, Neeraj; Agrawal, Shikha; Silkari, Sanjay

doi:10.1016/j.procs.2015.08.238

Cited by 71 publications

(38 citation statements)

References 4 publications

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“…Then, in the detection stage, these characteristics are used for detection and classification [70]. Supervised learning is one type of machine learning technique in which the characteristics of the training dataset are used in the learning phase to create a classification model, which is then used to classify new unseen instances [71]. Unsupervised learning is a type of machine learning technique that depends on the features of the data without using clustered training data [71].…”

Section: Anomaly-based Intrusion Detectionmentioning

confidence: 99%

“…Supervised learning is one type of machine learning technique in which the characteristics of the training dataset are used in the learning phase to create a classification model, which is then used to classify new unseen instances [71]. Unsupervised learning is a type of machine learning technique that depends on the features of the data without using clustered training data [71]. A pattern classification method in machine learning depends on pattern recognition, whereas a single classifier method depends on a single machine learning algorithm [72,73].…”

Section: Anomaly-based Intrusion Detectionmentioning

confidence: 99%

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Intrusion detection systems for IoT-based smart environments: a survey

Elrawy¹,

2018

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One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.

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Section: Anomaly-based Intrusion Detectionmentioning

confidence: 99%

Section: Anomaly-based Intrusion Detectionmentioning

confidence: 99%

Intrusion detection systems for IoT-based smart environments: a survey

Elrawy¹,

2018

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“…QoE monitoring Surveys [162][163][164][165][166][167] Monitoring in the network QoE estimation [168][169][170][171][172][173] QoE factor estimation [16,27,63,[174][175][176][177][178][179][180][181][182] Machine learning [183][184][185][186][187][188][189] Monitoring in the application QoE factors [176,[190][191][192][193][194][195][196] QoE models [132,148,[197][198][199] QoE-aware tra c management mechanisms Surveys [200,201] Network tra c management [202][203]…”

Section: Background and Related Workmentioning

confidence: 99%

Quality of experience and access network traffic management of HTTP adaptive video streaming

Seufert

Tran-Gia

2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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“…On the other hand, the classification can be performed using supervised learning. Supervised learning tries to figure out classes from the training data, and test data have been measured by studying the learning data [5]. The most used data discriminators in flow based method is given in [6] for the assessment of the classification task.…”

Section: Introductionmentioning

confidence: 99%

Multilayer Feedforward Neural Network for Internet Traffic Classification

Manju¹,

Harish²,

Nagadarshan³

2020

IJIMAI

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Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard).

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Recent Advancement in Machine Learning Based Internet Traffic Classification

Cited by 71 publications

References 4 publications

Intrusion detection systems for IoT-based smart environments: a survey

Intrusion detection systems for IoT-based smart environments: a survey

Quality of experience and access network traffic management of HTTP adaptive video streaming

Multilayer Feedforward Neural Network for Internet Traffic Classification

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