Neural Network Malware Signature Generation via NLP
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing volume of network data makes it difficult to distinguish malicious packets from benign ones, especially for advanced persistent threat (APT) malware that spreads across multiple network sessions, as traditional signature generation schemes are ineffective in identifying these complex patterns.
Innovation Solution
A neural network-based method that performs natural language processing on known malware traffic to generate signatures by analyzing syntax, semantic, and contextual information from malware data packets, using a word2vec model to create recognizable patterns for identifying malicious communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signature generation schemes are used, then the detection process is simple, but the detection precision deteriorates due to the increasing volume of network data and the ability of APT malware to spread across multiple network sessions
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between traditional signature generation and malware detection. By converting packet data into natural language sentences and using NLP models to extract semantic features, the system bridges the gap between simple traditional methods and the need for complex APT detection across multiple sessions
Solution Approach 2:
The patent replaces traditional mechanical signature matching mechanisms with neural network-based natural language processing. Instead of using fixed pattern matching, the system uses deep learning models (BERT, GPT) to understand the semantic meaning of network communications, enabling detection of sophisticated APT malware that spreads across multiple sessions
2Reliability
If the number of malware packets increases relative to total packets, then the detection coverage improves, but the difficulty of separating malicious packets from benign ones increases
Solution Approach 1:
The patent changes the parameter space for detection by transforming packet data into natural language sentences with specific grammatical structures. By encoding packet information into sentences with subjects, verbs, and objects that represent network entities and actions, the system creates new features that improve separability between malicious and benign traffic
Solution Approach 2:
Natural language processing serves as an intermediary that transforms raw packet data into semantically meaningful representations. This transformation layer extracts contextual information and relationships that are not apparent in traditional packet fields, making it easier to distinguish malicious from benign packets even as volumes increase
Data Source
AI summary
The technology disclosed herein enables the generation of malware traffic signatures by performing natural language processing on known malware traffic using a neural network. In a particular embodiment, a method provides generating sentences comprising first information obtained from a plurality of fields in each of a plurality of known malware data packets in a first malware family. The method further provides inputting the sentences into a first neural network for natural language processing of the sentences and generating one or more signatures for the first malware family from results of the natural language processing of the sentences.