Command Line Reconnaissance Detection Using Machine Learning
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Solution Overview
Problem
Current rule-based techniques for detecting internal reconnaissance attacks on computer systems suffer from high false positive rates due to the overlap between malicious and legitimate command-line activities, making it difficult to distinguish between internal reconnaissance and legitimate behavior.
Innovation Solution
Employing machine learning approaches, such as Recurrence-based Neural Networks, Convolution-based Neural Networks, and Transformer-based Neural Networks, to classify command collections and identify potential reconnaissance attacks by analyzing command-line inputs as a language, processing sequences of commands, and mapping API calls to language models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based techniques are used to detect internal reconnaissance attacks, then detection capability is provided, but false positive rates become substantially high
Solution Approach 1:
The patent replaces rule-based detection mechanisms with machine learning models (LSTM, GRU, Transformer) that automatically learn patterns from command-line data. This substitution enables the system to distinguish between legitimate and malicious commands more accurately by capturing complex sequential dependencies, thereby reducing false positives while maintaining detection capability.
Solution Approach 2:
The patent transforms the detection approach by changing from fixed rule-based parameters to dynamic parameters learned from data. The machine learning models adapt their detection thresholds and patterns based on training data, allowing the system to adjust to varying attack patterns and reduce false alarms caused by rigid rule matching.
2Device complexity
If deterministic detection approaches are used, then simple detection logic is maintained, but difficulty in distinguishing internal reconnaissance from legitimate behavior increases
Solution Approach 1:
The patent replaces simple deterministic detection logic with probabilistic machine learning models that can capture nuanced behavioral patterns. These models analyze sequences of commands and their contexts, enabling the system to distinguish between legitimate and malicious activities based on learned patterns rather than rigid rules, thereby reducing the difficulty of behavior distinction.
Solution Approach 2:
The patent adds temporal and contextual dimensions to command analysis by processing sequences of commands using recurrent and transformer models. This multi-dimensional analysis captures the evolution of command patterns over time and their relationships within sequences, providing richer features for distinguishing between legitimate and malicious behaviors beyond simple command matching.
Data Source
AI summary
Command line inputs to a system by a user or automated script can comprise a number of legitimate commands but, as a series, reveal a reconnaissance attack, such as to gain knowledge of a system without a legitimate reason to do so. A trained artificial intelligence monitors the command line inputs to the system, as a series, and determines therefrom whether a match exists to a reconnaissance attack. The match may be a non-exact match, such as a match determined by a long short-term memory (LSTM) machine learning model. A reconnaissance attack response may then be initiated upon determining a match is present.