LSTM Network Cyber-Attack Phase Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cyber-attack detection methods typically occur in later phases of an attack and lack the ability to determine the attack type and origin in real-time, failing to predict future phases and affected regions within data processing environments.
Innovation Solution
A method utilizing Long Short-Term Memory (LSTM) networks to classify cyber-attack phases by analyzing feature vectors from different regions, predicting future phases and affected areas through the use of feature vectors indicative of ongoing and past events, and incorporating adjacent region data to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional cyber-attack detection methods are used, then detection can be performed, but detection occurs only in later phases of attacks and cannot predict future phases or affected regions
Solution Approach 1:
The system performs preliminary classification of attack phases by analyzing feature vectors from current and adjacent regions before the attack fully manifests. The LSTM network processes historical and real-time data to predict future attack phases and affected regions, enabling proactive defense measures to be taken before the attack reaches critical stages.
Solution Approach 2:
The system extends detection from a single region to multiple adjacent regions by incorporating spatial dimensionality. Feature vectors from neighboring regions are fed into the LSTM network alongside current region data, enabling the system to predict attack propagation across different regions and identify future affected areas.
2Measurement precision
If real-time analysis of multiple regions is performed, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system divides the data processing environment into discrete regions, each with its own feature vector representation. The LSTM network processes these segmented regional data independently but in parallel, allowing scalable analysis of multiple regions without exponentially increasing complexity. Each region can be analyzed separately while maintaining overall system coherence.
Solution Approach 2:
The LSTM network serves as an intermediary that processes and integrates feature vectors from multiple regions. Instead of directly analyzing raw data from all regions simultaneously, the LSTM acts as a mediator that transforms complex multi-regional data into classified attack phase predictions, simplifying the overall processing architecture.
Data Source
AI summary
A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The events corresponding to the collections are classified, using an LTSM network, into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase that corresponds to the class is predicted as likely to occur during the future time window in the region.