Forecasting Network Attacks with Recurrent Neural Networks
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Solution Overview
Problem
Conventional methods for forecasting future attacks on network devices are labor-intensive, inaccurate, and require human input, failing to appreciate interconnectivity between attack events and distinguishing between benign noise and malicious attacks.
Innovation Solution
A computer-implemented method using a recurrent neural network, specifically a Long Short-Term Memory (LSTM) network, to forecast attack events by collecting and analyzing event sequences from multiple network devices, distinguishing between attack and noise events, and performing security actions to prevent harm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to forecast future attacks, then human input can guide and refine forecasts, but the process becomes too labor intensive and inaccurate to be effective
Solution Approach 1:
The system uses machine learning models (Random Forest, XGBoost, LSTM) that automatically learn from historical attack data and event sequences without requiring human experts to manually guide or refine forecasts. The model self-trains on collected data and autonomously generates attack forecasts, eliminating labor-intensive human involvement while maintaining high accuracy through automated pattern recognition in multi-stage attack sequences.
2Adaptability or versatility
If conventional methods forecast future attacks, then attempts are made to block future attacks, but the methods fail to appreciate interconnectivity between attack events in a multi-stage attack
Solution Approach 1:
The system segments attack events into discrete units within event sequences and uses machine learning models to analyze the connections between these segmented events. The LSTM model specifically processes sequential event data to recognize patterns and interconnectivity across multiple stages of attacks, allowing the system to understand how individual events connect to form multi-stage attack campaigns without losing the relational information between events.
3Measurement precision
If conventional methods forecast attacks, then forecasts are generated, but the methods are unable to adequately distinguish between benign noise events and attack events
Solution Approach 1:
The system implements feedback mechanisms where the machine learning models continuously learn from labeled event sequences, adjusting their classification thresholds and decision boundaries based on performance metrics. The models receive feedback from both positive examples (actual attack events) and negative examples (benign noise events), progressively improving their ability to distinguish between the two categories. This feedback-driven learning enables high classification accuracy while the modular model architecture keeps system complexity manageable.
Data Source
AI summary
Securing a network device by forecasting an attack event using a recurrent neural network. In one embodiment, a method may include collecting event sequences of events that occurred on multiple network devices, generating training sequences, validation sequences, and test sequences from the event sequences, training a recurrent neural network using the training sequences, the validation sequences, and the test sequences, collecting an event sequence of the most recent events that occurred on a target network device, forecasting, using the recurrent neural network and based on the event sequence of the most recent events that occurred on the target network device, the next event that will occur on the target network device, and in response to the forecasted next event being an attack event, performing a security action to prevent harm to the target network device from the attack event.