Network intrusion detection leveraging machine learning and feature selection

Arshid Ali; Shahtaj Shaukat; Muhammad Tayyab; Muazzam A Khan; Jan Sher Khan; null Arshad; Jawad Ahmad

doi:10.1109/honet50430.2020.9322813

Network intrusion detection leveraging machine learning and feature selection

Arshid Ali, Shahtaj Shaukat, Muhammad Tayyab, Muazzam A Khan, Jan Sher Khan, Arshad, Jawad Ahmad

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

16 Citations (Scopus)

16 Downloads (Pure)

Abstract

Handling superfluous and insignificant features in high-dimension data sets incidents led to a long-term demand for system anomaly detection. Ignoring such elements with spectral instruction not speeds up the analysis process but again facilitates classifiers to make accurate selections during attack perception stage, when wrestling with huge-scale and heterogeneous data. In this paper, for dimensionality reduction of data, we use Correlation-based Feature Selection (CFS) and Naïve Bayes (NB) classifier techniques. The proposed Intrusion Detection System (IDS) classifies attacks using a Multilayer Perceptron (MLP) and Instance-Based Learning algorithm (IBK). The accuracy of the introduced IDS is 99.87% and 99.82% with only 5 and 3 features out of 78 features for IBK. Other metrics such as precision, Recall, F-measure, and Receiver Operating Curve (ROC) also confirm the principal performance of IBK compared to MLP.

Original language	English
Title of host publication	2020 IEEE 17th International Conference on Smart Communities
Subtitle of host publication	Improving Quality of Life Using ICT, IoT and AI (HONET)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9780738105277
ISBN (Print)	9781665423007
DOIs	https://doi.org/10.1109/honet50430.2020.9322813
Publication status	Published - 21 Jan 2021
Event	IEEE 17th International Conference on Smart Communities: Improving Quality of Life using ICT, Iot and AI - Duration: 14 Dec 2020 → 16 Dec 2020 http://www.honet.uncc.edu/cfp.html

Conference

Conference	IEEE 17th International Conference on Smart Communities
Abbreviated title	IEEE HONET 2020
Period	14/12/20 → 16/12/20
Internet address	http://www.honet.uncc.edu/cfp.html

Keywords

intrusion detection system (IDS)
correlation based feature (CFS)
classifier subset evaluation
multilayer perceptron (MLP)
instance-based learning algorithm (IBK)

Access to Document

10.1109/honet50430.2020.9322813

Ali-etal-IEEE-2021-Network-intrusion-detection-leveraging-machine-learningAccepted author manuscript, 329 KBLicence: Strath_1

Cite this

@inproceedings{4dea86a6db6d4715b0a59b9c7e86b34e,

title = "Network intrusion detection leveraging machine learning and feature selection",

abstract = "Handling superfluous and insignificant features in high-dimension data sets incidents led to a long-term demand for system anomaly detection. Ignoring such elements with spectral instruction not speeds up the analysis process but again facilitates classifiers to make accurate selections during attack perception stage, when wrestling with huge-scale and heterogeneous data. In this paper, for dimensionality reduction of data, we use Correlation-based Feature Selection (CFS) and Na{\"i}ve Bayes (NB) classifier techniques. The proposed Intrusion Detection System (IDS) classifies attacks using a Multilayer Perceptron (MLP) and Instance-Based Learning algorithm (IBK). The accuracy of the introduced IDS is 99.87% and 99.82% with only 5 and 3 features out of 78 features for IBK. Other metrics such as precision, Recall, F-measure, and Receiver Operating Curve (ROC) also confirm the principal performance of IBK compared to MLP.",

keywords = "intrusion detection system (IDS), correlation based feature (CFS), classifier subset evaluation, multilayer perceptron (MLP), instance-based learning algorithm (IBK)",

author = "Arshid Ali and Shahtaj Shaukat and Muhammad Tayyab and Khan, {Muazzam A} and Khan, {Jan Sher} and Arshad and Jawad Ahmad",

note = "{\textcopyright} 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; IEEE 17th International Conference on Smart Communities : Improving Quality of Life using ICT, Iot and AI, IEEE HONET 2020 ; Conference date: 14-12-2020 Through 16-12-2020",

year = "2021",

month = jan,

day = "21",

doi = "10.1109/honet50430.2020.9322813",

language = "English",

isbn = "9781665423007",

booktitle = "2020 IEEE 17th International Conference on Smart Communities",

publisher = "IEEE",

url = "http://www.honet.uncc.edu/cfp.html",

}

Ali, A, Shaukat, S, Tayyab, M, Khan, MA, Khan, JS, Arshad & Ahmad, J 2021, Network intrusion detection leveraging machine learning and feature selection. in 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET). IEEE, Piscataway, NJ, IEEE 17th International Conference on Smart Communities, 14/12/20. https://doi.org/10.1109/honet50430.2020.9322813

Network intrusion detection leveraging machine learning and feature selection. / Ali, Arshid; Shaukat, Shahtaj; Tayyab, Muhammad et al.
2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET). Piscataway, NJ: IEEE, 2021.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Network intrusion detection leveraging machine learning and feature selection

AU - Ali, Arshid

AU - Shaukat, Shahtaj

AU - Tayyab, Muhammad

AU - Khan, Muazzam A

AU - Khan, Jan Sher

AU - Arshad, null

AU - Ahmad, Jawad

N1 - © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2021/1/21

Y1 - 2021/1/21

N2 - Handling superfluous and insignificant features in high-dimension data sets incidents led to a long-term demand for system anomaly detection. Ignoring such elements with spectral instruction not speeds up the analysis process but again facilitates classifiers to make accurate selections during attack perception stage, when wrestling with huge-scale and heterogeneous data. In this paper, for dimensionality reduction of data, we use Correlation-based Feature Selection (CFS) and Naïve Bayes (NB) classifier techniques. The proposed Intrusion Detection System (IDS) classifies attacks using a Multilayer Perceptron (MLP) and Instance-Based Learning algorithm (IBK). The accuracy of the introduced IDS is 99.87% and 99.82% with only 5 and 3 features out of 78 features for IBK. Other metrics such as precision, Recall, F-measure, and Receiver Operating Curve (ROC) also confirm the principal performance of IBK compared to MLP.

AB - Handling superfluous and insignificant features in high-dimension data sets incidents led to a long-term demand for system anomaly detection. Ignoring such elements with spectral instruction not speeds up the analysis process but again facilitates classifiers to make accurate selections during attack perception stage, when wrestling with huge-scale and heterogeneous data. In this paper, for dimensionality reduction of data, we use Correlation-based Feature Selection (CFS) and Naïve Bayes (NB) classifier techniques. The proposed Intrusion Detection System (IDS) classifies attacks using a Multilayer Perceptron (MLP) and Instance-Based Learning algorithm (IBK). The accuracy of the introduced IDS is 99.87% and 99.82% with only 5 and 3 features out of 78 features for IBK. Other metrics such as precision, Recall, F-measure, and Receiver Operating Curve (ROC) also confirm the principal performance of IBK compared to MLP.

KW - intrusion detection system (IDS)

KW - correlation based feature (CFS)

KW - classifier subset evaluation

KW - multilayer perceptron (MLP)

KW - instance-based learning algorithm (IBK)

U2 - 10.1109/honet50430.2020.9322813

DO - 10.1109/honet50430.2020.9322813

M3 - Conference contribution book

SN - 9781665423007

BT - 2020 IEEE 17th International Conference on Smart Communities

PB - IEEE

CY - Piscataway, NJ

T2 - IEEE 17th International Conference on Smart Communities

Y2 - 14 December 2020 through 16 December 2020

ER -

Network intrusion detection leveraging machine learning and feature selection

Abstract

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Keywords

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