Temporal Word Embeddings for Evolving Cyberattack Detection
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Solution Overview
Problem
Modern cyberattacks are complex and evolve over time, making it difficult to identify and protect against them due to changes in attack strategies and vulnerabilities, leading to costly unauthorized access and exploitation of endpoints.
Innovation Solution
A computer-implemented method using temporal word embeddings to identify sequences of security events, embed them into low-dimensional vectors, and analyze these vectors to detect evolved cyberattacks, allowing for remedial actions to be taken on endpoint devices, such as blocking access or rolling back changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cyberattack detection methods are used, then simple attacks can be identified, but evolved cyberattacks cannot be detected due to their changing characteristics
Solution Approach 1:
The system uses dynamic word embeddings that capture temporal relationships and evolve with new attack patterns. The embeddings are updated over time to reflect changing attack methodologies, allowing the detection system to adapt dynamically rather than relying on static signatures.
Solution Approach 2:
The system transforms attack detection from binary pattern matching to continuous vector space analysis. By representing security events as vectors with multiple dimensions capturing various attack characteristics, the system can detect subtle parameter changes that indicate evolved attacks.
2Measurement precision
If security event sequences are analyzed in detail, then accurate attack identification is achieved, but computational complexity increases
Solution Approach 1:
The system introduces word embeddings as an intermediary representation layer between raw security events and attack detection algorithms. These embeddings condense complex event sequences into compact vector representations that preserve essential patterns while reducing computational burden.
Solution Approach 2:
The system replaces traditional mechanical pattern-matching approaches with neural network-based embedding models. This substitution enables the system to learn complex patterns automatically from data rather than relying on manually crafted rules, improving both accuracy and efficiency.
Data Source
AI summary
Identifying and protecting against evolving cyberattacks using temporal word embeddings. In some embodiments, a method may include identifying sequences of security events that occurred over time on endpoint devices. The method may also include embedding each of the sequences of security events into low dimensional vectors, such that each of the sequences of security events is treated as a sentence, and such that each of the security events is treated as a word in the corresponding sentence. The method may further include analyzing the low dimensional vectors to identify a first cyberattack represented by a first sequence of security events and a second cyberattack represented by a second sequence of security events that is different from the first sequence of security events, the second cyberattack being an evolved version of the first cyberattack. The method may also include, in response to identifying the second cyberattack, protecting against the second cyberattack.