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POSTER: (Semi)-Supervised Machine Learning Approaches for Network Security in High-Dimensional Network Data

Published: 24 October 2016 Publication History

Abstract

Network security represents a keystone to ISPs, who need to cope with an increasing number of network attacks that put the network's integrity at risk. The high-dimensionality of network data provided by current network monitoring systems opens the door to the massive application of machine learning approaches to improve the detection and classification of network attacks. In this paper we devise a novel attacks detection and classification technique based on semi-supervised Machine Learning (ML) algorithms to automatically detect and diagnose network attacks with minimal training, and compare its performance to that achieved by other well-known supervised learning detectors. The proposed solution is evaluated using real network measurements coming from the WIDE backbone network, using the well-known MAWILab dataset for attacks labeling.

References

[1]
R. Fontugne et al., "MAWILab: Combining Diverse Anomaly Detectors for Automated Anomaly Labeling and Performance Benchmarking", in ACM CoNEXT, 2010.
[2]
A. K. Jain, "Data Clustering: 50 Years Beyond K-Means", in Pattern Recognition Letters, vol. 31 (8), 2010.
[3]
P. Casas et al., "MINETRAC: Mining Flows for Unsupervised Analysis & Semi-Supervised Classification", in ITC, 2011.
[4]
T. Nguyen et al., "A Survey of Techniques for Internet Traffic Classification using Machine Learning", in IEEE Comm, Surv. & Tut., vol. 10 (4), pp. 56--76, 2008.
[5]
P. Casas et al., "Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge", in Com. Comm., vol. 35 (7), 2011.

Cited By

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  • (2024)A Survey on the Applications of Semi-supervised Learning to Cyber-securityACM Computing Surveys10.1145/365764756:10(1-41)Online publication date: 22-Jun-2024
  • (2022)A Novel 3D Intelligent Cluster Method for Malicious Traffic Fine-Grained ClassificationAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-95384-3_25(385-401)Online publication date: 23-Feb-2022
  • (2020)Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous DatasetElectronics10.3390/electronics91117719:11(1771)Online publication date: 26-Oct-2020
  • Show More Cited By

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  1. POSTER: (Semi)-Supervised Machine Learning Approaches for Network Security in High-Dimensional Network Data

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        cover image ACM Conferences
        CCS '16: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security
        October 2016
        1924 pages
        ISBN:9781450341394
        DOI:10.1145/2976749
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        New York, NY, United States

        Publication History

        Published: 24 October 2016

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        Author Tags

        1. clustering
        2. high-dimensional data
        3. machine learning
        4. mawilab
        5. network attacks

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        CCS '16 Paper Acceptance Rate 137 of 831 submissions, 16%;
        Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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        Cited By

        View all
        • (2024)A Survey on the Applications of Semi-supervised Learning to Cyber-securityACM Computing Surveys10.1145/365764756:10(1-41)Online publication date: 22-Jun-2024
        • (2022)A Novel 3D Intelligent Cluster Method for Malicious Traffic Fine-Grained ClassificationAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-95384-3_25(385-401)Online publication date: 23-Feb-2022
        • (2020)Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous DatasetElectronics10.3390/electronics91117719:11(1771)Online publication date: 26-Oct-2020
        • (2019)TitAntProceedings of the VLDB Endowment10.14778/3352063.335212612:12(2082-2093)Online publication date: 1-Aug-2019
        • (2019)DeepSec meets RawPower - Deep Learning for Detection of Network Attacks Using Raw RepresentationsACM SIGMETRICS Performance Evaluation Review10.1145/3308897.330896046:3(147-150)Online publication date: 25-Jan-2019
        • (2018)RawPowerProceedings of the ACM SIGCOMM 2018 Conference on Posters and Demos10.1145/3234200.3234238(75-77)Online publication date: 7-Aug-2018
        • (2017)Ensemble-learning Approaches for Network Security and Anomaly DetectionProceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks10.1145/3098593.3098594(1-6)Online publication date: 7-Aug-2017
        • (2017)Super learning for anomaly detection in cellular networks2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMOB.2017.8115784(1-8)Online publication date: Oct-2017
        • (2017)Network security and anomaly detection with Big-DAMA, a big data analytics framework2017 IEEE 6th International Conference on Cloud Networking (CloudNet)10.1109/CloudNet.2017.8071525(1-7)Online publication date: Sep-2017

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