Edge Computing Based Traffic Analysis System Using Broad Learning

Current traffic analysis methods are executed on the cloud, which need to upload the traffic data and consume precious bandwidth resources. Edge computing is a more promising way to save the bandwidth resources and improve users’ privacy by offloading these tasks to the edge node. However, traffic analysis methods based on traditional machine learning need to retrain all traffic data when updating the trained model, which are not suitable for edge computing due to the poor computing power and low storage capacity of edge nodes. In this paper, we propose a novel edge computing based traffic analysis system using broad learning. For one thing, edge computing can provide a distributed architecture for saving the bandwidth resources and protecting users’ privacy. For another, we use broad learning to incrementally train the traffic data, which is more suitable for edge computing because it can support incremental updates of models on the edge nodes without retraining all data. We implement our system on the Raspberry Pi, and experimental results show that we have 98% probability to accurately identify these traffic data. Moreover, our method has the faster training speed compared with Convolutional Neural Network (CNN).

[1]  Erhan Guven,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2016, IEEE Communications Surveys & Tutorials.

[2]  Jun Zhang,et al.  Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions , 2023, IEEE Transactions on Information Forensics and Security.

[3]  Mianxiong Dong,et al.  DeepNFV: A Lightweight Framework for Intelligent Edge Network Functions Virtualization , 2018, IEEE Network.

[4]  Joydeep Biswas,et al.  Server-Side Traffic Analysis Reveals Mobile Location Information over the Internet , 2019, IEEE Transactions on Mobile Computing.

[5]  Weisong Shi,et al.  The Promise of Edge Computing , 2016, Computer.

[6]  Mianxiong Dong,et al.  Saving Energy on the Edge: In-Memory Caching for Multi-Tier Heterogeneous Networks , 2018, IEEE Communications Magazine.

[7]  Mianxiong Dong,et al.  ECCN: Orchestration of Edge-Centric Computing and Content-Centric Networking in the 5G Radio Access Network , 2018, IEEE Wireless Communications.

[8]  Blake Anderson,et al.  Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity , 2017, KDD.

[9]  Mounir Ghogho,et al.  Deep learning approach for Network Intrusion Detection in Software Defined Networking , 2016, 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM).

[10]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.

[11]  Marco Fiore,et al.  Large-Scale Mobile Traffic Analysis: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[12]  Cheng Li,et al.  Dense-Device-Enabled Cooperative Networks for Efficient and Secure Transmission , 2018, IEEE Network.

[13]  Atay Ozgovde,et al.  How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions , 2017, IEEE Communications Surveys & Tutorials.

[14]  Mauro Conti,et al.  Robust Smartphone App Identification via Encrypted Network Traffic Analysis , 2017, IEEE Transactions on Information Forensics and Security.

[15]  Amit P. Sheth,et al.  On Using the Intelligent Edge for IoT Analytics , 2017, IEEE Intelligent Systems.

[16]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[17]  Xiang Li,et al.  An Internet Traffic Classification Method Based on Semi-Supervised Support Vector Machine , 2011, 2011 IEEE International Conference on Communications (ICC).

[18]  Mugen Peng,et al.  Fog-computing-based radio access networks: issues and challenges , 2015, IEEE Network.

[19]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[20]  Rodrigo Roman,et al.  Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges , 2016, Future Gener. Comput. Syst..

[21]  Meikang Qiu,et al.  A Scalable and Quick-Response Software Defined Vehicular Network Assisted by Mobile Edge Computing , 2017, IEEE Communications Magazine.

[22]  Yuval Elovici,et al.  ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis , 2017, SAC.

[23]  Tansu Alpcan,et al.  Fog Computing May Help to Save Energy in Cloud Computing , 2016, IEEE Journal on Selected Areas in Communications.

[24]  Subbiah Sankari,et al.  Network Traffic Analysis of cloud data centre , 2015, 2015 International Conference on Computing and Communications Technologies (ICCCT).

[25]  C. L. Philip Chen,et al.  Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Andrzej Duda,et al.  Markov chain fingerprinting to classify encrypted traffic , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.