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A Deep Learning Approach for Network Intrusion Detection System

Authors:

Mansoor AlamAuthors Info & Claims

BICT'15: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

Pages 21 - 26

https://doi.org/10.4108/eai.3-12-2015.2262516

Published: 24 May 2016 Publication History

Abstract

A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in

their organizations. However, many challenges arise while

developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS.

We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD - a benchmark dataset for network

intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.

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Cited By

Lin LZhong QQiu JLiang Z(2025)E-GRACL: an IoT intrusion detection system based on graph neural networksThe Journal of Supercomputing10.1007/s11227-024-06471-581:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06471-5
Zamanzadeh Darban ZWebb GPan SAggarwal CSalehi M(2024)Deep Learning for Time Series Anomaly Detection: A SurveyACM Computing Surveys10.1145/369133857:1(1-42)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3691338
Liu LSun TYang LBai J(2024)TAM-CNN: An Enhanced Malicious Encrypted Traffic Detection Method with Feature-Threshold ImagesProceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674458(171-176)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3674399.3674458
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A Deep Learning Approach for Network Intrusion Detection System
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

cover image Guide Proceedings

BICT'15: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

May 2016

618 pages

ISBN:9781631901003

Editors:
Junichi Suzuki
Associate Professor, Dept of Comp. Science, University of Massachusetts, Boston, 10 Morrissey Blvd., Boston, MA 02125, USA
,
Tadashi Nakano
Associate Professor, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan
,
Henry Hess
Associate Professor, Department of Biomedical Engineering, Columbia University, 351L Engineering Terrace, MC 8904, 1210 Amsterdam Avenue, New York, NY 10027

Publisher

ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

Brussels, Belgium

Publication History

Published: 24 May 2016

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Cited By

Lin LZhong QQiu JLiang Z(2025)E-GRACL: an IoT intrusion detection system based on graph neural networksThe Journal of Supercomputing10.1007/s11227-024-06471-581:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06471-5
Zamanzadeh Darban ZWebb GPan SAggarwal CSalehi M(2024)Deep Learning for Time Series Anomaly Detection: A SurveyACM Computing Surveys10.1145/369133857:1(1-42)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3691338
Liu LSun TYang LBai J(2024)TAM-CNN: An Enhanced Malicious Encrypted Traffic Detection Method with Feature-Threshold ImagesProceedings of the ACM Turing Award Celebration Conference - China 202410.1145/3674399.3674458(171-176)Online publication date: 5-Jul-2024
https://dl.acm.org/doi/10.1145/3674399.3674458
Wang YYan R(2024)Pixel Power: Harnessing Image Processing for Optimizing Few-Shot Malicious Traffic DetectionProceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition10.1145/3663976.3664005(1-6)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3663976.3664005
Maseer ZKadhim QAl‐Bander BYusof RSaif A(2024)Meta‐analysis and systematic review for anomaly network intrusion detection systemsIET Networks10.1049/ntw2.1212813:5-6(339-376)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1049/ntw2.12128
Fu XJiang CLi CLi JZhu XLi F(2024)A hybrid approach for Android malware detection using improved multi-scale convolutional neural networks and residual networksExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123675249:PBOnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123675
Sharma ASingh M(2024)Batch reinforcement learning approach using recursive feature elimination for network intrusion detectionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109013136:PBOnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.109013
Zheng YDang ZLian XPeng CGao X(2024)Multi-view multi-label network traffic classification based on MLP-Mixer neural networkComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110746253:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110746
Wang YWang J(2023)Elastic Detection Mechanism Aimed at Hybrid DDoS AttackProceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning10.1145/3590003.3590031(154-160)Online publication date: 17-Mar-2023
https://dl.acm.org/doi/10.1145/3590003.3590031
Apruzzese GLaskov PMontes de Oca EMallouli WBrdalo Rapa LGrammatopoulos ADi Franco F(2023)The Role of Machine Learning in CybersecurityDigital Threats: Research and Practice10.1145/35455744:1(1-38)Online publication date: 7-Mar-2023
https://dl.acm.org/doi/10.1145/3545574
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