A multi-layer perceptron approach for flow-based anomaly detection

Lennart Van Efferen; Amr M.T. Ali-Eldin

doi:10.1109/ISNCC.2017.8072036

A multi-layer perceptron approach for flow-based anomaly detection

Lennart Van Efferen, Amr M.T. Ali-Eldin

Information and Communication Technology

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

37 Citations (SciVal)

Abstract

The increase in successful cyber-attacks on systems with firewalls and encryption techniques has led to the creation of Intrusion Detection Systems (IDS). Machine learning techniques are often used for these systems to predict malicious behaviour in the vague and unbalanced data. Flow-based IDS monitors only the packet headers of the network traffic and not the attached data to keep up with the growing bandwidth of networks and to maintain the privacy of the users. In this context, a multilayer perceptron approach is analysed on two different datasets and compared to a J48 Decision Tree classifier. Obtained results confirm that flow-based systems seem to be, apart from inevitable, the right way for IDS in the future and that MLP can still be useful in flow-based detection.

Original language	English
Title of host publication	Proceedings of International Symposium on Networks, Computers and Communications, ISNCC 2017
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9781509042593
DOIs	https://doi.org/10.1109/ISNCC.2017.8072036
Publication status	Published - 2017
Event	2017 International Symposium on Networks, Computers and Communications, ISNCC 2017 - Marrakech, Morocco Duration: 16 May 2017 → 18 May 2017

Conference

Conference	2017 International Symposium on Networks, Computers and Communications, ISNCC 2017
Country/Territory	Morocco
City	Marrakech
Period	16/05/17 → 18/05/17

Keywords

anomaly detection
Artificial Neural Networks (ANNs)
Intrusion detection systems (IDS)
J48 decision tree
Multi-layer Perceptrons (MLP)

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10.1109/ISNCC.2017.8072036

Cite this

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title = "A multi-layer perceptron approach for flow-based anomaly detection",

abstract = "The increase in successful cyber-attacks on systems with firewalls and encryption techniques has led to the creation of Intrusion Detection Systems (IDS). Machine learning techniques are often used for these systems to predict malicious behaviour in the vague and unbalanced data. Flow-based IDS monitors only the packet headers of the network traffic and not the attached data to keep up with the growing bandwidth of networks and to maintain the privacy of the users. In this context, a multilayer perceptron approach is analysed on two different datasets and compared to a J48 Decision Tree classifier. Obtained results confirm that flow-based systems seem to be, apart from inevitable, the right way for IDS in the future and that MLP can still be useful in flow-based detection.",

keywords = "anomaly detection, Artificial Neural Networks (ANNs), Intrusion detection systems (IDS), J48 decision tree, Multi-layer Perceptrons (MLP)",

author = "{Van Efferen}, Lennart and Ali-Eldin, {Amr M.T.}",

year = "2017",

doi = "10.1109/ISNCC.2017.8072036",

language = "English",

booktitle = "Proceedings of International Symposium on Networks, Computers and Communications, ISNCC 2017",

publisher = "IEEE",

address = "United States",

note = "2017 International Symposium on Networks, Computers and Communications, ISNCC 2017 ; Conference date: 16-05-2017 Through 18-05-2017",

}

Van Efferen, L & Ali-Eldin, AMT 2017, A multi-layer perceptron approach for flow-based anomaly detection. in Proceedings of International Symposium on Networks, Computers and Communications, ISNCC 2017., 8072036, IEEE, 2017 International Symposium on Networks, Computers and Communications, ISNCC 2017, Marrakech, Morocco, 16/05/17. https://doi.org/10.1109/ISNCC.2017.8072036

TY - GEN

T1 - A multi-layer perceptron approach for flow-based anomaly detection

AU - Van Efferen, Lennart

AU - Ali-Eldin, Amr M.T.

PY - 2017

Y1 - 2017

N2 - The increase in successful cyber-attacks on systems with firewalls and encryption techniques has led to the creation of Intrusion Detection Systems (IDS). Machine learning techniques are often used for these systems to predict malicious behaviour in the vague and unbalanced data. Flow-based IDS monitors only the packet headers of the network traffic and not the attached data to keep up with the growing bandwidth of networks and to maintain the privacy of the users. In this context, a multilayer perceptron approach is analysed on two different datasets and compared to a J48 Decision Tree classifier. Obtained results confirm that flow-based systems seem to be, apart from inevitable, the right way for IDS in the future and that MLP can still be useful in flow-based detection.

AB - The increase in successful cyber-attacks on systems with firewalls and encryption techniques has led to the creation of Intrusion Detection Systems (IDS). Machine learning techniques are often used for these systems to predict malicious behaviour in the vague and unbalanced data. Flow-based IDS monitors only the packet headers of the network traffic and not the attached data to keep up with the growing bandwidth of networks and to maintain the privacy of the users. In this context, a multilayer perceptron approach is analysed on two different datasets and compared to a J48 Decision Tree classifier. Obtained results confirm that flow-based systems seem to be, apart from inevitable, the right way for IDS in the future and that MLP can still be useful in flow-based detection.

KW - anomaly detection

KW - Artificial Neural Networks (ANNs)

KW - Intrusion detection systems (IDS)

KW - J48 decision tree

KW - Multi-layer Perceptrons (MLP)

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M3 - Conference contribution

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BT - Proceedings of International Symposium on Networks, Computers and Communications, ISNCC 2017

PB - IEEE

T2 - 2017 International Symposium on Networks, Computers and Communications, ISNCC 2017

Y2 - 16 May 2017 through 18 May 2017

ER -

A multi-layer perceptron approach for flow-based anomaly detection

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Keywords

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