Security Engine Reinforcement Learning for Post-Attack Hardening
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Solution Overview
Problem
Conventional security measures for software applications apply broad security features before an attack, leading to performance degradation, as they are not tailored to specific vulnerabilities exploited during an attack, resulting in unnecessary performance trade-offs.
Innovation Solution
The Security Engine employs a reinforcement learning algorithm to selectively apply security hardening passes to an application post-attack, balancing security enhancements with maintaining optimal performance by analyzing the pre-attack state and applying targeted security measures to address specific threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If broad security features are applied to the entire application source code before an attack, then security coverage is improved, but application performance degradation increases
Solution Approach 1:
The system applies security hardening passes selectively to specific local areas of the application code that are vulnerable to detected attack patterns, rather than applying broad security features to the entire codebase. This localized approach addresses specific vulnerabilities while preserving the performance of unaffected code sections.
Solution Approach 2:
The security hardening process is divided into multiple discrete passes, each targeting specific vulnerability types or code regions. The reinforcement learning algorithm selects and sequences individual hardening passes based on the detected attack patterns, allowing granular control over which security measures are applied where.
2Measurement precision
If security hardening passes are applied to address specific vulnerabilities, then security precision is improved, but the complexity of selecting and applying passes increases
Solution Approach 1:
The reinforcement learning algorithm learns from feedback about which security hardening passes effectively address detected attack patterns. The system observes the outcomes of applying passes and uses this feedback to optimize future pass selection, automatically adapting to new attack patterns without manual configuration.
Solution Approach 2:
The system automatically selects and applies the appropriate sequence of security hardening passes based on the detected attack patterns, without requiring manual intervention. The reinforcement learning model autonomously determines which passes to apply and in what order, reducing the complexity burden on users.
Data Source
AI summary
Various embodiments of methods, systems and computer program products described herein are directed to a Security Engine. The Security Engine provides for post-attack security upgrades of an application by selecting specific security hardening passes to be applied to a pre-attack state of the application. According to various embodiments, the Security Engine receives a pre-attack state of a first instance of an application in response to an action by an attack source. The Security Engine selects one or more security hardening passes to be applied to the pre-attack state. The Security Engine sends an identification of the selected security hardening passes to be applied to a second instance of the application running at the pre-attack state.