Attack Prediction Model Training for Next-Step APT Detection
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Solution Overview
Problem
Existing cyber defense systems struggle to predict the next action of sophisticated cyberattacks, particularly Advanced Persistent Threats (APT), making it difficult to respond effectively.
Innovation Solution
A method and device for training an attack prediction model by analyzing attack datasets based on tactics and techniques, generating sequences, and using a bidirectional LSTM to predict the next attack behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based defense techniques are used to defend against cyberattacks, then individual-level attacks can be effectively blocked, but sophisticated APT attacks with systematic and evolving compositions can evade detection
Solution Approach 1:
The system performs preliminary analysis of attack datasets to generate sequences representing attack patterns before actual attacks occur. By pre-training the prediction model with historical attack data organized into tactical sequences, the system prepares predictive capabilities in advance to anticipate and respond to sophisticated APT attacks before they fully execute.
Solution Approach 2:
The system implements a feedback mechanism where the attack prediction model continuously learns from new attack data. The model predicts next attack behaviors based on learned patterns, and these predictions are refined through ongoing training with updated attack datasets, creating a closed-loop system that adapts to evolving threats.
2Reliability
If traditional vulnerability searching and prevention methods are used, then security teams must proactively hunt for threats before attacks, but this approach cannot respond effectively to ever-changing APT attacks
Solution Approach 1:
The system performs preliminary analysis of attack datasets to generate sequences representing attack patterns before actual attacks occur. By pre-training the prediction model with historical attack data organized into tactical sequences, the system prepares predictive capabilities in advance to anticipate and respond to sophisticated APT attacks before they fully execute.
Solution Approach 2:
The system transitions from static vulnerability assessment to dynamic attack prediction. The model adapts its predictions based on learned attack patterns and can dynamically adjust to new threat scenarios, enabling real-time response to evolving APT attacks rather than relying on predetermined vulnerability lists.
3Measurement precision
If attack data is analyzed without structured sequencing based on tactics and techniques, then analysis is simpler, but the model cannot accurately predict next attack behavior
Solution Approach 1:
The system segments attack data into structured sequences based on tactical and technical components. By dividing complex attack datasets into organized sequences representing different stages and techniques of attacks, the model can process and predict attack behaviors more accurately while maintaining manageable data complexity through systematic categorization.
Data Source
AI summary
Provided is a method for training an attack prediction model, including obtaining an attack dataset including at least one attack datum, analyzing the attack dataset based on tactics and techniques, generating a sequence for the at least one attack datum based on the analysis, and training the attack prediction model based on the sequence, and provided is a device for training the digital attack prediction model.