IoT Firmware Vulnerability Detection Across CPU Architectures
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Solution Overview
Problem
Current methods struggle to perform cross-architecture firmware security analysis of IoT devices due to closed-source, customized firmware and diverse hardware architectures, lacking a unified platform for analysis, and requiring costly physical equipment or manual reverse analysis.
Innovation Solution
A cross-architecture automated detection method and system that reverses and analyzes firmware without source code, using deep learning and pattern matching to identify and detect third-party components and vulnerabilities, leveraging a similarity model trained on binary and non-binary files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual reverse firmware analysis is used, then analysis capability is achieved, but analysis efficiency is low and workload is high
Solution Approach 1:
The system performs automated firmware analysis by extracting, disassembling, and detecting vulnerabilities without requiring manual intervention. The automated extraction module automatically extracts firmware from IoT devices, the disassembly module automatically disassembles firmware binaries into assembly code, and the detection module automatically detects third-party components and vulnerabilities, making the entire process self-service and eliminating manual reverse analysis workload
Solution Approach 2:
The patent replaces manual mechanical reverse analysis with an automated computational system. Instead of manual disassembly and analysis, the system uses automated extraction modules, disassembly modules that convert binaries to assembly code, and detection modules with pattern matching algorithms to automatically identify vulnerabilities, substituting human labor with automated mechanical and computational processes
2Reliability
If physical equipment is used for dynamic analysis, then detection capability is achieved, but equipment cost is high
Solution Approach 1:
The system creates a virtual copy of the firmware analysis process by extracting firmware images from devices and performing all analysis operations on these copies rather than requiring physical test equipment. The extraction module obtains firmware images, which are then disassembled and analyzed in a virtual environment, eliminating the need for expensive physical dynamic analysis equipment while maintaining detection capability
Solution Approach 2:
The patent introduces firmware images as an intermediary between the physical device and the analysis process. Instead of directly analyzing physical devices with expensive equipment, the system uses firmware images as intermediaries that contain all necessary information for analysis, enabling cost-effective virtual analysis while preserving detection capability
3Measurement precision
If top-down code analysis is used, then analysis accuracy is achieved, but applicability to closed-source firmware is poor
Solution Approach 1:
The patent inverts the traditional top-down code analysis approach by implementing bottom-up analysis. Instead of starting with source code and working down to binaries (which requires access to closed-source code), the system starts with firmware images and binaries, automatically extracts and disassembles them into assembly code, and then performs detection, enabling accurate analysis of closed-source firmware without requiring source code access
4Adaptability or versatility
If cross-architecture analysis platform is implemented, then versatility across different architectures is achieved, but system complexity increases
Solution Approach 1:
The system achieves cross-architecture compatibility through universal modules that can handle multiple architectures. The extraction module universally extracts firmware from various IoT devices, the disassembly module universally disassembles different architecture binaries (ARM, x86, MIPS, etc.) into assembly code, and the detection module universally detects third-party components across architectures using architecture-agnostic pattern matching, making one system serve multiple architectures without proportionally increasing complexity
Data Source
AI summary
The invention discloses a cross-architecture automated detection method and system for third-party components and security risks, comprising: identify and reverse the firmware of the IoT device, classify the resulting reverse products into binary and non-binary files; disassemble binary files to mine the semantic information in them; convert non-binary files into string text files; build a database containing third-party components and their known CVE; combine pattern matching to scan string text files automatically, collect third-party components in the firmware of IoT device, and collect and retrieve vulnerabilities of corresponding third-party components. Through organically combining the semantic information of the vulnerability assembly code and the semantic information of the firmware assembly code of IoT device, the similarity comparison across architectures and deep learning is realized, and the specific pattern vulnerability is mined and verified automatically. The invention does not require the acquisition of firmware source code, the detection process is automated, greatly reducing the difficulty and workload of manual analysis.