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This allows us to extract several families of features. Some of them require careful feature engineering, while others are more general and follow well-known NLP techniques. Next, we survey prior art from the literature and discuss several natural approaches to this problem.
Finally, we suggest obfuscator-agnostic methods to build state-of-the-art machine learning classifier for this problem.