Autoencoder Initialization Vector Identification for Encrypted Malware Detection
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Solution Overview
Problem
Current methods for detecting malicious network traffic, especially encrypted traffic, are ineffective due to the inability to distinguish between malicious and non-malicious encrypted communications, leading to false positives and failures in identifying encrypted malware.
Innovation Solution
A method using an autoencoder to identify malicious encrypted network traffic by defining a portion of network traffic, extracting and training on it, and selecting candidate locations for initialization vectors in encrypted communications, allowing for the differentiation of malicious traffic patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deep packet inspection (DPI) is used to detect malicious traffic, then detection accuracy for unencrypted traffic is improved, but detection of encrypted malware traffic fails completely
Solution Approach 1:
The patent introduces an intermediary approach by using autoencoders to learn representations of encrypted traffic without requiring decryption. The system captures encrypted traffic and uses neural network models to detect malware patterns while the traffic remains encrypted, serving as a mediator between encrypted communication and detection capabilities
Solution Approach 2:
The patent replaces traditional mechanical DPI inspection with a machine learning-based approach. Instead of manually inspecting packet contents, the system uses autoencoders and neural networks to automatically learn and detect malicious patterns in encrypted traffic, substituting mechanical inspection with intelligent automated analysis
2Reliability
If encryption is applied to malware traffic to protect communications, then security and privacy are improved, but detection of malicious traffic becomes impossible
Solution Approach 1:
The patent converts the harmful effect of encryption (hiding malicious content) into a beneficial approach by training autoencoders on encrypted traffic characteristics. The system learns to detect malware based on patterns in encrypted data itself, turning the encryption barrier into a detectable signal rather than an undetectable shield
Solution Approach 2:
The patent changes the detection parameters from content-based analysis to metadata and pattern-based analysis. Instead of analyzing packet payloads for malicious code, the system detects changes in traffic flow patterns, timing, and statistical properties that indicate malware communication while maintaining encryption
3Measurement precision
If signature-based detection is used to identify malware, then detection of known malware is improved, but detection of new or modified malware fails
Solution Approach 1:
The patent implements a dynamic detection system using neural networks that can adapt to new malware variants. The autoencoders are trained on multiple malware samples and continuously learn new patterns, allowing the system to detect both known and previously unseen malware without requiring manual signature updates
Solution Approach 2:
The system performs preliminary training with diverse malware samples before deployment, creating robust detection models that can identify both known and new threats. The autoencoders are pre-trained on a wide variety of malware types and encryption methods, enabling them to generalize to new variants they have never encountered
Data Source
AI summary
A method for identifying malicious encrypted network traffic associated with a malware software component communicating via a network, the method including, for the malware, a portion of network traffic including a plurality of contiguous bytes occurring at a predefined offset in a network communication of the malware; extracting the defined portion of network traffic for each of a plurality of disparate encrypted network connections for the malware; training an autoencoder based on each extracted portion of network traffic, wherein the autoencoder includes: a set of input units each for representing information from a byte of an extracted portion; output units each for storing an output of the autoencoder; and a set of hidden units smaller in number than the set of input units and each interconnecting all input and all output units with weighted interconnections, such that the autoencoder is trainable to provide an approximated reconstruction of values of the input units at the output units; selecting a set of one or more offsets in the definition of a portion of network traffic as candidate locations for communication of an initialization vector for encryption of the network traffic, the selection being based on weights of interconnections in the autoencoder; and identifying malicious network traffic based on an identification of an initialization vector in the network traffic at one of the candidate locations.