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Introduction:
Are you concerned about the potential risks posed by executable files?
Introducing SecureWoof, an AI-powered malware scanner designed to detect and analyze potentially harmful executable files with utmost efficiency and reliability. By leveraging a combination of techniques and open-source libraries, SecureWoof offers users an advanced solution for proactively addressing cybersecurity risks associated with these files.
With SecureWoof, you can upload an executable file and rest assured that it will undergo a comprehensive evaluation process. The tool begins by checking the file against a set of static Yara rules, identifying any known patterns or signatures associated with malicious code. It then unpacks the file using the Retdec unpacker and decompiles it into a single C file using Ghidra, enabling a thorough analysis of the code’s structure.
To further enhance the analysis, SecureWoof utilizes clang-tidy to format the decompiled code and adhere to coding standards. The decompiled code is also embedded using FastText, allowing the tool to understand the semantic context of the code. Finally, the file undergoes an evaluation for maliciousness using a trained RoBERTa transformer network.
The RoBERTa and FastText models employed by SecureWoof have been trained using the SOREL-20M malware dataset, enhancing their ability to accurately identify and classify potential threats within uploaded files. With its intelligent scanning capabilities, SecureWoof empowers users to stay one step ahead of cybersecurity risks associated with executable files.
Overview:
SecureWoof is an AI-powered malware scanner that provides users with an efficient and reliable method to detect and analyze potentially harmful executable files.
To accomplish this, the tool employs various techniques and open-source libraries. When a user uploads an executable file, SecureWoof conducts a series of steps to evaluate its safety.
First, the file is checked against a set of static Yara rules, which helps identify any known patterns or signatures associated with malicious code.
Next, the file is unpacked using the Retdec unpacker, a widely-used tool for decompressing files. After unpacking, the file is decompiled into a single C file using Ghidra, an open-source software package. This process enables the tool to analyze the code and understand its structure.
The decompiled file is then formatted using clang-tidy, a tool for ensuring code quality and adhering to coding standards. To further enhance the analysis, the decompiled code is embedded using FastText, a library that allows the tool to understand the semantic context of the code.
Finally, the file undergoes an evaluation for maliciousness using a trained RoBERTa transformer network. The RoBERTa and FastText models utilized by SecureWoof were trained using the SOREL-20M malware dataset, enhancing their ability to accurately identify and classify potential threats within uploaded files.
With its intelligent scanning capabilities, SecureWoof offers users an advanced solution for proactively addressing cybersecurity risks associated with executable files.
Benefits:
- Efficient and reliable method for detecting and analyzing potentially harmful executable files
- Uses static Yara rules to identify known patterns or signatures associated with malicious code
- Unpacks files using the Retdec unpacker to decompress them
- Decompiles files into a single C file using Ghidra to analyze the code and understand its structure
- Formats the decompiled code using clang-tidy to ensure code quality and adherence to coding standards
- Embeds the decompiled code using FastText to understand the semantic context of the code
- Evaluates files for maliciousness using a trained RoBERTa transformer network
- Utilizes the SOREL-20M malware dataset to enhance accuracy in identifying and classifying potential threats
- Offers an advanced solution for proactively addressing cybersecurity risks associated with executable files
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