On the Need for Collaborative Intelligence in Cybersecurity

Trevor Martin

doi:10.3390/electronics11132067

On the Need for Collaborative Intelligence in Cybersecurity

Trevor Martin

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

1 Citation (Scopus)

Abstract

The success of artificial intelligence (and particularly data-driven machine learning) in classifying and making predictions from large bodies of data has led to an expectation that autonomous AI systems can be deployed in cybersecurity applications. In this position paper we outline some of the problems facing machine learning in cybersecurity and argue for a collaborative approach where humans contribute insight and understanding, whilst machines are used to gather, filter and process data into a convenient and understandable form. In turn this requires a convenient representation for exchanging information between machine and human, and we argue that graded concepts are suitable, allowing summarisation at multiple levels of discernibility (granularity).

Original language	English
Title of host publication	Selected Papers from the AI-CyberSec 2021 Workshop in the 41st SGAI International Conference on Artificial Intelligence
Publisher	MDPI AG
Number of pages	17
DOIs	https://doi.org/10.3390/electronics11132067
Publication status	Published - 30 Jun 2022
Event	2021 Workshop on AI and Cybersecurity, AI-Cybersec 2021 - Cambridge, United Kingdom Duration: 14 Dec 2021 → …

Publication series

Name	Electronics
Publisher	MDPI
ISSN (Electronic)	2079-9292

Conference

Conference	2021 Workshop on AI and Cybersecurity, AI-Cybersec 2021
Country/Territory	United Kingdom
City	Cambridge
Period	14/12/21 → …

Bibliographical note

Publisher Copyright:
© 2022 by the author.

Keywords

collaborative intelligence
Cybersecurity
explainability

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title = "On the Need for Collaborative Intelligence in Cybersecurity",

abstract = "The success of artificial intelligence (and particularly data-driven machine learning) in classifying and making predictions from large bodies of data has led to an expectation that autonomous AI systems can be deployed in cybersecurity applications. In this position paper we outline some of the problems facing machine learning in cybersecurity and argue for a collaborative approach where humans contribute insight and understanding, whilst machines are used to gather, filter and process data into a convenient and understandable form. In turn this requires a convenient representation for exchanging information between machine and human, and we argue that graded concepts are suitable, allowing summarisation at multiple levels of discernibility (granularity).",

keywords = "collaborative intelligence, Cybersecurity, explainability",

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AB - The success of artificial intelligence (and particularly data-driven machine learning) in classifying and making predictions from large bodies of data has led to an expectation that autonomous AI systems can be deployed in cybersecurity applications. In this position paper we outline some of the problems facing machine learning in cybersecurity and argue for a collaborative approach where humans contribute insight and understanding, whilst machines are used to gather, filter and process data into a convenient and understandable form. In turn this requires a convenient representation for exchanging information between machine and human, and we argue that graded concepts are suitable, allowing summarisation at multiple levels of discernibility (granularity).

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KW - explainability

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M3 - Conference Contribution (Conference Proceeding)

AN - SCOPUS:85128741948

T3 - Electronics

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ER -

On the Need for Collaborative Intelligence in Cybersecurity

Abstract

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