Visible to the public SecBot: a Business-Driven Conversational Agent for Cybersecurity Planning and Management

TitleSecBot: a Business-Driven Conversational Agent for Cybersecurity Planning and Management
Publication TypeConference Paper
Year of Publication2020
AuthorsFranco, Muriel Figueredo, Rodrigues, Bruno, Scheid, Eder John, Jacobs, Arthur, Killer, Christian, Granville, Lisandro Zambenedetti, Stiller, Burkhard
Conference Name2020 16th International Conference on Network and Service Management (CNSM)
Date Publishednov
KeywordsBusiness, chatbot, Companies, Computer crime, conversational agents, data mining, Human Behavior, Metrics, pubcrawl, Scalability, Social Agents, social networking (online), Training
AbstractBusinesses were moving during the past decades to-ward full digital models, which made companies face new threats and cyberattacks affecting their services and, consequently, their profits. To avoid negative impacts, companies' investments in cybersecurity are increasing considerably. However, Small and Medium-sized Enterprises (SMEs) operate on small budgets, minimal technical expertise, and few personnel to address cybersecurity threats. In order to address such challenges, it is essential to promote novel approaches that can intuitively present cybersecurity-related technical information.This paper introduces SecBot, a cybersecurity-driven conversational agent (i.e., chatbot) for the support of cybersecurity planning and management. SecBot applies concepts of neural networks and Natural Language Processing (NLP), to interact and extract information from a conversation. SecBot can (a) identify cyberattacks based on related symptoms, (b) indicate solutions and configurations according to business demands, and (c) provide insightful information for the decision on cybersecurity investments and risks. A formal description had been developed to describe states, transitions, a language, and a Proof-of-Concept (PoC) implementation. A case study and a performance evaluation were conducted to provide evidence of the proposed solution's feasibility and accuracy.
DOI10.23919/CNSM50824.2020.9269037
Citation Keyfranco_secbot_2020