Evaluating Attacker Risk Behavior in an Internet of Things Ecosystem

• URL: http://arxiv.org/abs/2109.11592v1
• Date: Thu, 23 Sep 2021 18:53:41 GMT
• Title: Evaluating Attacker Risk Behavior in an Internet of Things Ecosystem
• Abstract summary: In cybersecurity, attackers range from brash, unsophisticated script kiddies to stealthy, patient advanced persistent threats. This work explores how an attacker's risk seeking or risk averse behavior affects their operations against detection-optimizing defenders.
• Score: 2.958532752589616
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In cybersecurity, attackers range from brash, unsophisticated script kiddies and cybercriminals to stealthy, patient advanced persistent threats. When modeling these attackers, we can observe that they demonstrate different risk-seeking and risk-averse behaviors. This work explores how an attacker's risk seeking or risk averse behavior affects their operations against detection-optimizing defenders in an Internet of Things ecosystem. Using an evaluation framework which uses real, parametrizable malware, we develop a game that is played by a defender against attackers with a suite of malware that is parameterized to be more aggressive and more stealthy. These results are evaluated under a framework of exponential utility according to their willingness to accept risk. We find that against a defender who must choose a single strategy up front, risk-seeking attackers gain more actual utility than risk-averse attackers, particularly in cases where the defender is better equipped than the two attackers anticipate. Additionally, we empirically confirm that high-risk, high-reward scenarios are more beneficial to risk-seeking attackers like cybercriminals, while low-risk, low-reward scenarios are more beneficial to risk-averse attackers like advanced persistent threats.

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