Crossover Neural Embeddings for Proactive Cyber-Attack Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cyber security solutions are reactionary and fail to proactively predict cyber-attacks, relying on rule and signature-based methods that struggle to adapt to changing Advanced Persistent Threat (APT) vectors and new attack methods, limiting their ability to prevent or alert against unknown threats in real-time.
Innovation Solution
The method employs crossover neural embeddings by constructing feature vectors from analytical and Pattern of Life (POL) data, introducing noise, and using a trained neural network to forecast and classify potential cyber-attacks by predicting feature values at future times, enabling proactive defense.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule and signature-based methods are used for cyber security detection, then the system can detect known threats, but it fails to adapt to changing APT vectors and new attack methods
Solution Approach 1:
The patent transitions from static rule-based detection to dynamic machine learning models that continuously adapt to new attack patterns. The system uses evolving feature vectors and retraining mechanisms to maintain detection accuracy against emerging threats while preserving reliability for known threats.
Solution Approach 2:
The system changes the parameters of detection by moving from fixed signature matching to probabilistic predictions based on multiple dynamic features. The neural network adjusts weights and thresholds based on training data, enabling adaptation to new attack vectors while maintaining detection capability for known threats.
2Adaptability or versatility
If machine learning techniques are implemented to predict future attacks, then the system can proactively defend against evolving threats, but the complexity of the detection system increases
Solution Approach 1:
The patent segments the complex prediction task into distinct components: feature extraction from multiple data sources, feature vector construction, aging/forecasting module, and neural network classification. This modular approach manages system complexity while enabling sophisticated threat prediction capabilities.
Solution Approach 2:
The system introduces intermediate feature vectors that bridge raw security data and final threat predictions. These feature vectors serve as mediators that transform complex multi-source data into structured inputs for the neural network, managing complexity through layered processing.
3Measurement precision
If multiple data sources and feature vectors are combined for prediction, then the prediction accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing data into structured feature vectors and pre-training neural network models offline. This preparation work reduces real-time processing requirements, enabling accurate predictions without excessive computational delays during actual security operations.
Solution Approach 2:
The patent implements partial processing by focusing computational resources on the most predictive features and using approximation techniques for less critical data sources. This selective approach maintains prediction accuracy while reducing overall processing time and resource consumption.
Data Source
AI summary
A first collection including a first feature vector and a Q&A feature vector is constructed. A second collection is constructed from the first collection by inserting noise in at least one of the vectors. A third collection is constructed by crossing over a feature vectors of the second collection with a corresponding feature vector of a fourth collection. The second and the fourth collections have a property similar to one another. Using a forecasting configuration, a vector of the third collection is aged to generate a changed feature vector, the changed feature vector containing feature values expected at a future time. The changed feature vector is input into a trained neural network to predict a probability of the cyber-attack occurring at the future time.