research-article

Simulation Games Platform for Unintentional Perpetrator Attack Vector Identification

Authors:

Agata Kruzikova,

Barbora BuhnovaAuthors Info & Claims

ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

Pages 222 - 229

https://doi.org/10.1145/3387940.3391475

Published: 25 September 2020 Publication History

Abstract

Cyber-security protection of critical systems is one of the major challenges of today. Although the attacks typically originate from attackers with malicious intent, a substantial portion of attack vectors is enabled by unintentional perpetrators, i.e., insiders who cause an incident by negligence, carelessness, or lack of training. Prevention of these situations is challenging because insiders have better access to the organization's resources and hence, are more likely to cause harm. Moreover, the insider-mediated actions of an attack vector often come unrecognized by security admins as well as the insiders themselves.

In this paper, we focus on the identification of the attack vector of unintentional perpetrators. To this end, we propose to employ specialized games that simulate the working period, while the player faces multiple dangers that might cause harm in their company. From the analysis of their actions, we discover the attack vector, which could be addressed before an actual attack happens. To reflect a variety of insiders and company environments, we introduce a platform for designing variants of these games, together with its architecture, an example of a simple game that can be created using the platform, and the used analysis method.

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Cited By

Daubner LMacak MMatulevičius RBuhnova BMaksović SPitner T(2023)Addressing insider attacks via forensic-ready risk managementJournal of Information Security and Applications10.1016/j.jisa.2023.10343373:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.jisa.2023.103433
Macak MDaubner LSani MBuhnova B(2022)Cybersecurity Analysis via Process Mining: A Systematic Literature ReviewAdvanced Data Mining and Applications10.1007/978-3-030-95405-5_28(393-407)Online publication date: 31-Jan-2022
https://doi.org/10.1007/978-3-030-95405-5_28
Daubner LMacak MBuhnova BPitner T(2020)Towards verifiable evidence generation in forensic-ready systems2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378035(2264-2269)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378035

Index Terms

Simulation Games Platform for Unintentional Perpetrator Attack Vector Identification
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Social aspects of security and privacy
  2. Intrusion/anomaly detection and malware mitigation
    1. Social engineering attacks
2. Software and its engineering
  1. Software creation and management
    1. Designing software

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cover image ACM Conferences

ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

June 2020

831 pages

ISBN:9781450379632

DOI:10.1145/3387940

Copyright © 2020 ACM.

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SIGSOFT: ACM Special Interest Group on Software Engineering
KIISE: Korean Institute of Information Scientists and Engineers
IEEE CS

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New York, NY, United States

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Published: 25 September 2020

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ICSE '20: 42nd International Conference on Software Engineering

June 27 - July 19, 2020

Seoul, Republic of Korea

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Cited By

Daubner LMacak MMatulevičius RBuhnova BMaksović SPitner T(2023)Addressing insider attacks via forensic-ready risk managementJournal of Information Security and Applications10.1016/j.jisa.2023.10343373:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.jisa.2023.103433
Macak MDaubner LSani MBuhnova B(2022)Cybersecurity Analysis via Process Mining: A Systematic Literature ReviewAdvanced Data Mining and Applications10.1007/978-3-030-95405-5_28(393-407)Online publication date: 31-Jan-2022
https://doi.org/10.1007/978-3-030-95405-5_28
Daubner LMacak MBuhnova BPitner T(2020)Towards verifiable evidence generation in forensic-ready systems2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378035(2264-2269)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378035

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