Cyber Attack Response Selection Using Loss Quantification
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Solution Overview
Problem
Traditional cybersecurity incident response playbooks fail to balance attack mitigation with business continuity, leading to significant disruptions and inefficiencies in managing cyber-attacks.
Innovation Solution
A system and method utilizing Monte Carlo simulations and artificial intelligence algorithms to quantify potential losses from cyber-attacks and business disruptions, selecting optimal responses that minimize total combined losses by employing cyber risk quantification principles and AI models like random forests, Deep Q-Networks, and Siamese neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional incident response playbooks are used to contain and eradicate cyber attacks, then attack mitigation is improved, but business continuity deteriorates due to significant disruptions
Solution Approach 1:
The system changes the parameters of incident response by introducing quantitative risk assessment metrics and probabilistic modeling. Instead of fixed playbook responses, the system dynamically adjusts response strategies based on calculated risk parameters, including attack success probability, business disruption likelihood, and loss quantification, thereby optimizing the balance between mitigation and continuity
Solution Approach 2:
The incident response system transitions from static playbooks to dynamic, adaptive response strategies. The system continuously updates response recommendations based on real-time risk assessments, Monte Carlo simulations, and evolving attack characteristics, allowing flexible adjustment of containment and eradication measures to minimize business disruption while maintaining security
2Reliability
If traditional playbooks implement containment and eradication measures, then cyber security is improved, but business disruption increases
Solution Approach 1:
The system performs preliminary risk assessment and loss quantification before implementing containment and eradication measures. By pre-calculating potential business disruptions through Monte Carlo simulations and probability distributions, the system prepares optimized response strategies that anticipate and minimize disruptive effects before they occur
Solution Approach 2:
The system implements feedback loops that continuously monitor attack progression, business impact, and response effectiveness. This feedback enables real-time adjustment of containment and eradication measures to achieve the minimum necessary disruption, using observed outcomes to refine future response decisions
3Reliability
If response measures are taken to mitigate cyber attacks, then attack success reduction is improved, but business disruption losses increase
Solution Approach 1:
The system applies parameter changes by quantifying both attack success reduction and business disruption costs using probabilistic models and Monte Carlo simulations. This allows the system to optimize response measures based on calculated expected values, adjusting containment and eradication strategies to achieve the highest risk reduction for the lowest expected disruption cost
Solution Approach 2:
The system evaluates whether partial response measures are sufficient or if excessive action is needed. By calculating probability distributions of attack outcomes and disruption costs, the system determines the minimum effective response level that achieves adequate attack success reduction without unnecessary business disruption, avoiding both under-response and over-response
Data Source
AI summary
A system includes a computer. The computer includes a processor and a memory. The memory includes instructions such that the processor is programmed to: determine a loss caused by at least one cyber-attack; determine a loss caused by business disruptions resulting from responses to the at least one cyber-attack; and select an optimal response to the at least one cyber-attack to minimize the losses.