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A Serious Game for Simulating Cyberattacks to Teach Cybersecurity

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arxiv 2305.03062 v1 pith:5I22SWMQ submitted 2023-05-04 cs.CR cs.CYcs.HC

classification cs.CRcs.CYcs.HC
keywords cyberattacksattackgamephishingapproachawarenesscybercybersecurity
verification ladder T0 review T1 audit T2 compute T3 formal
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With the rising number of cyberattacks, such as ransomware attacks and cyber espionage, educating non-cybersecurity professionals to recognize threats has become more important than ever before. However, traditional training methods, such as phishing awareness campaigns, training videos and assessments have proven to be less effective over time. Therefore, it is time to rethink the approach on how to train cyber awareness. In this paper we suggest an alternative approach -- a serious game -- to educate awareness for common cyberattacks. While many serious games for cybersecurity education exist, all follow a very similar approach: showing people the effects of a cyber attack on their own system or company network. For example, one of the main tasks in these games is to sort out phishing mails. We developed and evaluated a new type of cybersecurity game: an attack simulator, which shows the entire setting from a different perspective. Instead of sorting out phishing mails the players should write phishing mails to trick potential victims and use other forms of cyberattacks. Our game explains the intention of each attack and shows the consequences of a successful attack. This way, we hope, players will get a better understanding on how to detect cyberattacks.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cracking Aegis: An Adversarial LLM-based Game for Raising Awareness of Vulnerabilities in Privacy Protection

    cs.HC 2025-05 conditional novelty 6.0 of 10

    Cracking Aegis, an adversarial LLM-driven dialogue game, led players to use manipulative language strategies and to self-report stronger awareness of privacy vulnerabilities after a single session.

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    cs.CL 2025-05 conditional novelty 4.0 of 10

    LLMs can produce fluent movie reviews that readers often mistake for human-written ones, but the models differ in emotional balance and depth.

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