Network Traffic Classification Techniques-A Review

Goli, Yoga Durgadevi; Ambika, R.

doi:10.1109/ctems.2018.8769309

Cited by 13 publications

(10 citation statements)

References 9 publications

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“…ADFA is an IDS that includes three data types in its structure: (1) normal training data with 4373 traces; (2) normal validation data with 833 traces; and (3) attack data with 10 attacks per vector. As the web became a significant internet criminal activity platform, the security community put in efforts to blacklist malicious URLs.…”

Section: Common Datasets Usedmentioning

confidence: 99%

“…Several techniques have been proposed over the years to handle and classify network traffic attacks. One is the port-based technique, which includes identifying port numbers among the ones registered by the Internet Assign Number Authority (IANA) [1]. However, due to the growing number of applications, the number of unpredictable ports has increased and this technique has proven to be ineffective.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, this technique does not cover account applications that do not register their ports with the IANA or applications that use dynamic port numbers. Another technique that has been proposed is the payload-based technique, also known as deep packet inspection (DPI), where the network packet contents are observed and matched with an existing set of signatures stored in the database [1]. This method provides more accurate results than the port-based technique, but does not work on network applications using encrypted data.…”

Section: Introductionmentioning

confidence: 99%

“…This method provides more accurate results than the port-based technique, but does not work on network applications using encrypted data. Furthermore, this technique has been proven to be complex, involving high computational costs and a high processing load [1]. Behavioral classification techniques analyze the entire network traffic received at the host in order to identify the type of application [2].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Aljabri

Aljameel

Mohammad

et al. 2021

Sensors

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The significant growth in the use of the Internet and the rapid development of network technologies are associated with an increased risk of network attacks. Network attacks refer to all types of unauthorized access to a network including any attempts to damage and disrupt the network, often leading to serious consequences. Network attack detection is an active area of research in the community of cybersecurity. In the literature, there are various descriptions of network attack detection systems involving various intelligent-based techniques including machine learning (ML) and deep learning (DL) models. However, although such techniques have proved useful within specific domains, no technique has proved useful in mitigating all kinds of network attacks. This is because some intelligent-based approaches lack essential capabilities that render them reliable systems that are able to confront different types of network attacks. This was the main motivation behind this research, which evaluates contemporary intelligent-based research directions to address the gap that still exists in the field. The main components of any intelligent-based system are the training datasets, the algorithms, and the evaluation metrics; these were the main benchmark criteria used to assess the intelligent-based systems included in this research article. This research provides a rich source of references for scholars seeking to determine their scope of research in this field. Furthermore, although the paper does present a set of suggestions about future inductive directions, it leaves the reader free to derive additional insights about how to develop intelligent-based systems to counter current and future network attacks.

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Section: Common Datasets Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Aljabri

Aljameel

Mohammad

et al. 2021

Sensors

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“…They are particularly well-suited for peer-to-peer (P2P) traffic. However, they have some disadvantages and weaknesses [35,36]. They require very expensive hardware for the payload method search.…”

Section: Payload-based Classificationmentioning

confidence: 99%

SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

et al. 2021

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Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.

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Application-Based Traffic Classification by Employing Machine Learning Models on Software-Defined Networking

Nuñez-Agurto,

Fuertes,

Marrone

et al. 2024

Lecture Notes in Networks and Systems

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Network Traffic Classification Techniques-A Review

Cited by 13 publications

References 9 publications

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

Intelligent Techniques for Detecting Network Attacks: Review and Research Directions

SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models

Application-Based Traffic Classification by Employing Machine Learning Models on Software-Defined Networking

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