On the Variability in the Application and Measurement of Supervised Machine Learning in Cyber Security

Omar Alshaikh; Simon Parkinson; Saad Khan

doi:10.1007/978-981-99-0272-9_38

On the Variability in the Application and Measurement of Supervised Machine Learning in Cyber Security

Omar Alshaikh, Simon Parkinson, Saad Khan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Supervised learning (SL) is being increasingly adopted to enhance capability and mitigate cyberattacks. Published literature containing empirical studies often demonstrates an optimistic viewpoint, with promising results achieving greater than 90% in terms of accuracy when detecting and mitigating cyberattacks. These results are often generated on well-refined test scenarios. Cyberattack statistics show a continued increase in occurrence and continue to result in significant damage. This is resulting in organisations becoming increasingly worried about suffering a cyberattack, increasing their desire to identify and adopt suitable solutions. The optimistic result presented in research studies might misrepresent the application’s true capabilities and set unreachable expectations. The purpose of this paper is to investigate how SL technique is applied to cybersecurity challenges and how it is evaluated. To pursue this aim, a literature review is undertaken, classifying the most common SL performance measurements in cybersecurity research. The key finding of this paper revealed that SL is mostly used because of its capabilities in detecting known patterns on a restrictive application challenge. This could therefore be misleading for those wanting to utilise such systems.

Original language	English
Title of host publication	Ubiquitous Security
Subtitle of host publication	Second International Conference, UbiSec 2022, Zhangjiajie, China, December 28–31, 2022, Revised Selected Papers
Editors	Guojun Wang, Kim-Kwang Raymond Choo, Jie Wu, Ernesto Damiani
Publisher	Springer Singapore
Pages	545-555
Number of pages	11
Volume	1768 CCIS
Edition	1st
ISBN (Electronic)	9789819902729
ISBN (Print)	9789819902712
DOIs	https://doi.org/10.1007/978-981-99-0272-9_38
Publication status	Published - 16 Feb 2023
Event	The 2nd International Conference on Ubiquitous Security - Zhangjiajie, China Duration: 28 Dec 2022 → 31 Dec 2022 Conference number: 2 http://ubisecurity.org/2022/

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer Singapore
Volume	1768 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	The 2nd International Conference on Ubiquitous Security
Abbreviated title	UbiSec 2022
Country/Territory	China
City	Zhangjiajie
Period	28/12/22 → 31/12/22
Internet address	http://ubisecurity.org/2022/

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10.1007/978-981-99-0272-9_38Licence: Unspecified

Cite this

Alshaikh, O., Parkinson, S., & Khan, S. (2023). On the Variability in the Application and Measurement of Supervised Machine Learning in Cyber Security. In G. Wang, K-K. R. Choo, J. Wu, & E. Damiani (Eds.), Ubiquitous Security: Second International Conference, UbiSec 2022, Zhangjiajie, China, December 28–31, 2022, Revised Selected Papers (1st ed., Vol. 1768 CCIS, pp. 545-555). (Communications in Computer and Information Science; Vol. 1768 CCIS). Springer Singapore. https://doi.org/10.1007/978-981-99-0272-9_38

Alshaikh, Omar ; Parkinson, Simon ; Khan, Saad. / On the Variability in the Application and Measurement of Supervised Machine Learning in Cyber Security. Ubiquitous Security: Second International Conference, UbiSec 2022, Zhangjiajie, China, December 28–31, 2022, Revised Selected Papers. editor / Guojun Wang ; Kim-Kwang Raymond Choo ; Jie Wu ; Ernesto Damiani. Vol. 1768 CCIS 1st. ed. Springer Singapore, 2023. pp. 545-555 (Communications in Computer and Information Science).

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title = "On the Variability in the Application and Measurement of Supervised Machine Learning in Cyber Security",

abstract = "Supervised learning (SL) is being increasingly adopted to enhance capability and mitigate cyberattacks. Published literature containing empirical studies often demonstrates an optimistic viewpoint, with promising results achieving greater than 90% in terms of accuracy when detecting and mitigating cyberattacks. These results are often generated on well-refined test scenarios. Cyberattack statistics show a continued increase in occurrence and continue to result in significant damage. This is resulting in organisations becoming increasingly worried about suffering a cyberattack, increasing their desire to identify and adopt suitable solutions. The optimistic result presented in research studies might misrepresent the application{\textquoteright}s true capabilities and set unreachable expectations. The purpose of this paper is to investigate how SL technique is applied to cybersecurity challenges and how it is evaluated. To pursue this aim, a literature review is undertaken, classifying the most common SL performance measurements in cybersecurity research. The key finding of this paper revealed that SL is mostly used because of its capabilities in detecting known patterns on a restrictive application challenge. This could therefore be misleading for those wanting to utilise such systems.",

keywords = "Machine learning, Cybersecurity, Cyberattacks, Performance measurements, Supervised learning",

author = "Omar Alshaikh and Simon Parkinson and Saad Khan",

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Alshaikh, O, Parkinson, S & Khan, S 2023, On the Variability in the Application and Measurement of Supervised Machine Learning in Cyber Security. in G Wang, K-KR Choo, J Wu & E Damiani (eds), Ubiquitous Security: Second International Conference, UbiSec 2022, Zhangjiajie, China, December 28–31, 2022, Revised Selected Papers. 1st edn, vol. 1768 CCIS, Communications in Computer and Information Science, vol. 1768 CCIS, Springer Singapore, pp. 545-555, The 2nd International Conference on Ubiquitous Security, Zhangjiajie, China, 28/12/22. https://doi.org/10.1007/978-981-99-0272-9_38

On the Variability in the Application and Measurement of Supervised Machine Learning in Cyber Security. / Alshaikh, Omar; Parkinson, Simon ; Khan, Saad.

Ubiquitous Security: Second International Conference, UbiSec 2022, Zhangjiajie, China, December 28–31, 2022, Revised Selected Papers. ed. / Guojun Wang; Kim-Kwang Raymond Choo; Jie Wu; Ernesto Damiani. Vol. 1768 CCIS 1st. ed. Springer Singapore, 2023. p. 545-555 (Communications in Computer and Information Science; Vol. 1768 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - On the Variability in the Application and Measurement of Supervised Machine Learning in Cyber Security

AU - Alshaikh, Omar

AU - Parkinson, Simon

AU - Khan, Saad

N1 - Conference code: 2

PY - 2023/2/16

Y1 - 2023/2/16

N2 - Supervised learning (SL) is being increasingly adopted to enhance capability and mitigate cyberattacks. Published literature containing empirical studies often demonstrates an optimistic viewpoint, with promising results achieving greater than 90% in terms of accuracy when detecting and mitigating cyberattacks. These results are often generated on well-refined test scenarios. Cyberattack statistics show a continued increase in occurrence and continue to result in significant damage. This is resulting in organisations becoming increasingly worried about suffering a cyberattack, increasing their desire to identify and adopt suitable solutions. The optimistic result presented in research studies might misrepresent the application’s true capabilities and set unreachable expectations. The purpose of this paper is to investigate how SL technique is applied to cybersecurity challenges and how it is evaluated. To pursue this aim, a literature review is undertaken, classifying the most common SL performance measurements in cybersecurity research. The key finding of this paper revealed that SL is mostly used because of its capabilities in detecting known patterns on a restrictive application challenge. This could therefore be misleading for those wanting to utilise such systems.

AB - Supervised learning (SL) is being increasingly adopted to enhance capability and mitigate cyberattacks. Published literature containing empirical studies often demonstrates an optimistic viewpoint, with promising results achieving greater than 90% in terms of accuracy when detecting and mitigating cyberattacks. These results are often generated on well-refined test scenarios. Cyberattack statistics show a continued increase in occurrence and continue to result in significant damage. This is resulting in organisations becoming increasingly worried about suffering a cyberattack, increasing their desire to identify and adopt suitable solutions. The optimistic result presented in research studies might misrepresent the application’s true capabilities and set unreachable expectations. The purpose of this paper is to investigate how SL technique is applied to cybersecurity challenges and how it is evaluated. To pursue this aim, a literature review is undertaken, classifying the most common SL performance measurements in cybersecurity research. The key finding of this paper revealed that SL is mostly used because of its capabilities in detecting known patterns on a restrictive application challenge. This could therefore be misleading for those wanting to utilise such systems.

KW - Machine learning

KW - Cybersecurity

KW - Cyberattacks

KW - Performance measurements

KW - Supervised learning

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DO - 10.1007/978-981-99-0272-9_38

M3 - Conference contribution

SN - 9789819902712

VL - 1768 CCIS

T3 - Communications in Computer and Information Science

SP - 545

EP - 555

BT - Ubiquitous Security

A2 - Wang, Guojun

A2 - Choo, Kim-Kwang Raymond

A2 - Wu, Jie

A2 - Damiani, Ernesto

PB - Springer Singapore

T2 - The 2nd International Conference on Ubiquitous Security

Y2 - 28 December 2022 through 31 December 2022

ER -

Alshaikh O, Parkinson S , Khan S. On the Variability in the Application and Measurement of Supervised Machine Learning in Cyber Security. In Wang G, Choo K-KR, Wu J, Damiani E, editors, Ubiquitous Security: Second International Conference, UbiSec 2022, Zhangjiajie, China, December 28–31, 2022, Revised Selected Papers. 1st ed. Vol. 1768 CCIS. Springer Singapore. 2023. p. 545-555. (Communications in Computer and Information Science). Epub 2023 Feb 15. doi: 10.1007/978-981-99-0272-9_38

On the Variability in the Application and Measurement of Supervised Machine Learning in Cyber Security

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