CAPC: Packet-Based Network Service Classifier With Convolutional Autoencoder

Chiu, Kai-Cheng; Liu, Chien-Chang; Chou, Li-Der

doi:10.1109/access.2020.3041806

Cited by 11 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chiu et al [23] introduced a system known as the Convolutional Autoencoder Packet Classifier (CAPC), designed to promptly categorize incoming packets in both fine-grained and coarse-grained manners, distinguishing individual applications and broader categories, respectively, using DL. CAPC is a packet-based deep learning model comprising a 1D convolutional neural network and an autoencoder, enabling it to effectively manage dynamic ports, encrypted traffic, and even group similar applications.…”

Section: Related Workmentioning

confidence: 99%

A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking

Nuñez-Agurto,

Fuertes,

Marrone

et al. 2024

Future Internet

View full text Add to dashboard Cite

The ever-increasing diversity of Internet applications and the rapid evolution of network infrastructure due to emerging technologies have made network management more challenging. Effective traffic classification is critical for efficiently managing network resources and aligning with service quality and security demands. The centralized controller of software-defined networking provides a comprehensive network view, simplifying traffic analysis and offering direct programmability features. When combined with deep learning techniques, these characteristics enable the incorporation of intelligence into networks, leading to optimization and improved network management and maintenance. Therefore, this research aims to develop a model for traffic classification by application types and network attacks using deep learning techniques to enhance the quality of service and security in software-defined networking. The SEMMA method is employed to deploy the model, and the classifiers are trained with four algorithms, namely LSTM, BiLSTM, GRU, and BiGRU, using selected features from two public datasets. These results underscore the remarkable effectiveness of the GRU model in traffic classification. Hence, the outcomes achieved in this research surpass state-of-the-art methods and showcase the effectiveness of a deep learning model within a traffic classification in an SDN environment.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking

Nuñez-Agurto,

Fuertes,

Marrone

et al. 2024

Future Internet

View full text Add to dashboard Cite

show abstract

“…The authors mentioned that the best performance rates for the datasets were 96.75% and 86.92%, respectively. In another study, Chiu, et al [17] investigated faster processing of traffic packets. For this, they presented the package-based Convolutional Autoencoder Packet Classifier (CAPC) approach, which consists of 1D-CNN and AE models.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, Wang, et al [14] made evaluations on the ISCX VPN-Non VPN dataset in their study, but although the dataset used includes 206688 samples, there is also a difference in the number of classified applications. Similarly, Chiu, et al [17] made evaluations on the ISCX VPN-Non VPN dataset in their study, but the dataset used includes 222797 samples, but there is also a difference in the number of classified applications. Similar to these examples, when other studies such as Lotfollahi, et al [4], Bu, et al [8], Chen, et al [20], Izadi, et al [10] and Hu, et al [31] mentioned in Table 1 are examined, it will be seen that they have different dataset classes and sizes.…”

Section: Related Workmentioning

confidence: 99%

RFSE-GRU: Data Balanced Classification Model for Mobile Encrypted Traffic in Big Data Environment

Dener

2023

IEEE Access

View full text Add to dashboard Cite

With the widespread use of mobile technologies and the Internet, traffic in mobile networks is increasing. This situation has made the classification of traffic an important element for data security and network management. However, encryption of traffic in modern networks makes it difficult to classify traffic with traditional methods. In this study, a unique deep learning-based classification model is proposed for the classification of encrypted mobile traffic data. The proposed model is a classification model called RFSE-GRU, which combines the Gated Recurrent Units (GRU) algorithm, feature selection and data balancing. The features that are more meaningful in the classification process are determined by selecting the features with the Random Forest algorithm. In addition, Synthetic Minority Oversampling Technique (SMOTE) oversampling algorithm and Edited Nearest Neighbor (ENN) undersampling algorithm were used together to reduce the negative impact of data imbalance on classification performance. The study was carried out on Apache Spark big data platform in Google Colab environment. In the study, ISCX VPN-Non VPN and UTMobileNet2021 datasets were used. Binary and multiclass classifications were made for the ISCX VPN-Non VPN dataset, and multiclass classifications were made for the UTMobileNet2021 dataset by using various algorithms on the datasets. The proposed model has been compared with eleven different algorithms and hybrid methods. At the same time, the effect of data balancing and feature selection on classification performance is examined. As a result, the proposed model achieved 93.91%, 82.68% and 96.83% accuracy rates in ISCX VPN-Non VPN binary and multiclass, UTMobileNet2021 multiclass classifications, respectively.

show abstract

“…e classifier is a component of a router object that normalizes and classifies packets, and the classification result will be returned to the router. e normalization and training process are detailed in our previous work [19].…”

Section: Classifiermentioning

confidence: 99%

“…e data collection and processing methods, model construction and training, and performance evaluation of the TC model are presented in our previous work [19]. is study is the first to integrate the deep learning TC model within a network environment.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

Chiu

Liu

Chou

2022

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

The increasing quality and various requirements of network services are guaranteed because of the advancement of the emerging network paradigm, software-defined networking (SDN), and benefits from the centralized and software-defined architecture. The SDN not only facilitates the configuration of the network policies for traffic engineering but also brings convenience for network state obtainment. The traffic of numerous services is transmitted within a network, whereas each service may demand different network metrics, such as low latency or low packet loss rate. Corresponding quality of service policies must be enforced to meet the requirements of different services, and the balance of link utilization is also indispensable. In this research, Reinforcement Discrete Learning-Based Service-Oriented Multipath Routing (RED-STAR) has been proposed to understand the policy of distributing an optimal path for each service. The RED-STAR takes the network state and service type as input values to dynamically select the path a service must be forwarded. Custom protocols are designed for network state obtainment, and a deep learning-based traffic classification model is also integrated to identify network services. With the differentiated reward scheme for every service type, the reinforcement learning model in RED-STAR gradually achieves high reward values in various scenarios. The experimental results show that RED-STAR can adopt the dynamic network environment, obtaining the highest average reward value of 1.8579 and the lowest average maximum bandwidth utilization of 0.3601 among all path distribution schemes in a real-case scenario.

show abstract

CAPC: Packet-Based Network Service Classifier With Convolutional Autoencoder

Cited by 11 publications

References 45 publications

A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking

A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking

RFSE-GRU: Data Balanced Classification Model for Mobile Encrypted Traffic in Big Data Environment

Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN

Contact Info

Product

Resources

About