Machine Learning Model for Predicting Security Incidents
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional computing system security measures are inadequate in predicting future threats and detecting undefended vulnerabilities, often failing to alert users to security holes not addressed by currently installed products, leaving systems open to attacks.
Innovation Solution
A computer-implemented method using a machine learning model to gather and analyze event signatures and incident labels across consecutive time slots, training a latent feature to represent security postures and predict future incidents or events, enabling proactive security actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security products are installed on every computing system, then protection against active threats is improved, but cost and system complexity increase
Solution Approach 1:
A server acts as an intermediary between computing systems and security products. The server gathers security data from multiple sources, trains machine learning models, and generates predictions about future threats. This intermediary approach allows computing systems to benefit from advanced threat prediction without each system needing to run multiple complex security products locally
Solution Approach 2:
The system creates a virtual representation of security threats by training machine learning models on historical security data from multiple computing systems. The model learns patterns and generates predictions about future threats, effectively copying and generalizing security knowledge across the network without requiring physical installation of security products on every system
2Measurement precision
If traditional security systems monitor current threats, then detection accuracy is improved, but ability to predict future threats deteriorates
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical security data to predict future threats before they occur. The model analyzes patterns from past incidents and generates predictions about upcoming threats, allowing security administrators to take preventive measures before actual attacks happen, thus gaining time advantage
Solution Approach 2:
The system implements feedback loops where security data from multiple computing systems is continuously gathered, used to retrain and refine machine learning models, and then applied to generate improved predictions. This feedback mechanism enhances both detection accuracy and predictive capability over time, resolving the trade-off between current detection and future prediction
3Loss of energy
If security products are not installed on all systems, then cost is reduced, but vulnerability to undefended attacks increases
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it predicts future threats, identifies patterns across different computing systems, and provides security recommendations for systems without installed products. This universal approach allows a single centralized system to provide security benefits to multiple computing systems regardless of whether they have local security products installed
Solution Approach 2:
The server acts as an intermediary that compensates for missing local security products. By gathering data from multiple sources and generating predictive analytics, the intermediary provides security intelligence to computing systems that would otherwise be undefended, reducing their vulnerability without requiring costly product installations on every system
Data Source
AI summary
The disclosed computer-implemented method for making security-related predictions may include (i) gathering information that comprises both signatures of events that occurred on computing systems during consecutive time slots and incident labels about incidents on the computing systems during the consecutive time slots, (ii) using the gathered information to train a machine learning model, (iii) predicting, by the machine learning model, at least one of an incident label about an incident and a signature of an event on a computing system during a time slot, wherein the computing system does not comprise at least one of an application capable of generating the signature and information about events occurring during the time slot due to the time slot having not yet occurred, and (iv) performing an action in response to the prediction. Various other methods, systems, and computer-readable media are also disclosed.