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SecBot: a Business-Driven Conversational Agent for Cybersecurity Planning and Management


Figueredo Franco, Muriel; Rodrigues, Bruno; John Scheid, Eder; Jacobs, Arthur; Killer, Christian; Granville, Lisandro Zambenedetti; Stiller, Burkhard (2020). SecBot: a Business-Driven Conversational Agent for Cybersecurity Planning and Management. In: 16th International Conference on Network and Service Management (CNSM 2020), Izmir, Turkey, 2 November 2020 - 6 November 2020. IFIP, 1-7.

Abstract

Businesses were moving during the past decades to-ward full digital models, which made companies face new threatsand cyberattacks affecting their services and, consequently, theirprofits. To avoid negative impacts, companies’ investments incybersecurity are increasing considerably. However, Small andMedium-sized Enterprises (SMEs) operate on small budgets,minimal technical expertise, and few personnel to address cy-bersecurity threats. In order to address such challenges, it isessential to promote novel approaches that can intuitively presentcybersecurity-related technical information.This paper introduces SecBot, a cybersecurity-driven conver-sational agent (i.e., chatbot) for the support of cybersecurityplanning and management. SecBot applies concepts of neuralnetworks and Natural Language Processing (NLP), to interactand extract information from a conversation. SecBot can(a)identify cyberattacks based on related symptoms,(b)indicatesolutions and configurations according to business demands,and(c)provide insightful information for the decision on cy-bersecurity investments and risks. A formal description hadbeen developed to describe states, transitions, a language, anda Proof-of-Concept (PoC) implementation. A case study and aperformance evaluation were conducted to provide evidence ofthe proposed solution’s feasibility and accuracy

Abstract

Businesses were moving during the past decades to-ward full digital models, which made companies face new threatsand cyberattacks affecting their services and, consequently, theirprofits. To avoid negative impacts, companies’ investments incybersecurity are increasing considerably. However, Small andMedium-sized Enterprises (SMEs) operate on small budgets,minimal technical expertise, and few personnel to address cy-bersecurity threats. In order to address such challenges, it isessential to promote novel approaches that can intuitively presentcybersecurity-related technical information.This paper introduces SecBot, a cybersecurity-driven conver-sational agent (i.e., chatbot) for the support of cybersecurityplanning and management. SecBot applies concepts of neuralnetworks and Natural Language Processing (NLP), to interactand extract information from a conversation. SecBot can(a)identify cyberattacks based on related symptoms,(b)indicatesolutions and configurations according to business demands,and(c)provide insightful information for the decision on cy-bersecurity investments and risks. A formal description hadbeen developed to describe states, transitions, a language, anda Proof-of-Concept (PoC) implementation. A case study and aperformance evaluation were conducted to provide evidence ofthe proposed solution’s feasibility and accuracy

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Hardware and Architecture
Physical Sciences > Software
Social Sciences & Humanities > Information Systems and Management
Physical Sciences > Artificial Intelligence
Physical Sciences > Computer Networks and Communications
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:6 November 2020
Deposited On:22 Jan 2021 07:11
Last Modified:06 Mar 2024 14:34
Publisher:IFIP
ISBN:978-3-903176-31-7
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.23919/CNSM50824.2020.9269037
Official URL:http://dl.ifip.org/db/conf/cnsm/cnsm2020/1570659569.pdf
Other Identification Number:merlin-id:20498
  • Content: Published Version