This repository contains code and resources related to the detection of embedded Malware/Trojan in hardware devices used in the Power Sector. The project focuses on identifying and mitigating cybersecurity threats in the semiconductor industry.
- The Project describes the use of power and side channel analysis for detecting hardware Trojans by monitoring power usage and path delays.
- Automatic Test Pattern Generation (ATPG) is utilized to reveal potential malicious circuitry and avoid exhaustive test cases.
- The D Algorithm is mentioned for obtaining actual test cases to deal with faults like Stuck at 0 & Stuck at 1.
- A feature is highlighted to represent the circuit into a graph for simulation and storing information about neighboring elements.
- We introduces the Embedded C Flaw Finder and Python code analysis for identifying security flaws in C/C++ code.
- FlawFinder is used to detect potential vulnerabilities in the source code and generate a summary report for developers to secure their software.
- The Raspberry Pi Foundation's preference for Python is mentioned due to its power, versatility, and ease of use.
- Tools like CProfile, Memory Profiler, and Line Profiler are listed for code analysis and optimization.
An innovative Graph Neural Network (GNN)-based method is introduced for Trojan detection in RTL and gate-level netlists using Data Flow Graph (DFG) representations. The method eliminates the need for a golden reference in Trojan detection. Steps include DFG extraction from hardware design, feature extraction, and classification using a GNN framework with graph convolution layers and attention-based graph pooling.
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The project theme is focused on Blockchain & Cybersecurity, addressing the problem statement of detecting embedded Malware/Trojan in hardware devices used in the Power Sector. Decentralized Security and Data Storage:
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We have used the Inter Planetary File System (IPFS) as a decentralized database storage mechanism similar to blockchain networks but more cost-effective and secure.