Giorgio Giacinto
3 indexed papers
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The paper proposes a model-agnostic framework to evaluate combining Active Learning (AL) and Semi-Supervised Learning (SSL) techniques for malware detection, demonstrating that these combined methods can reduce manual labeling costs by up to 90% while maintaining high detection performance.
This paper empirically demonstrates that current Static Application Security Testing (SAST) tools are fundamentally unreliable against common JavaScript obfuscation techniques, showing that obfuscation can lead to near-total evasion of vulnerability detection.
The paper proposes a privacy-by-design pipeline for Android malware detection that achieves strong performance by avoiding the collection of sensitive user data entirely.
Papers
Don't Trust Us: A privacy-by-design android malware detection pipeline
The paper proposes a privacy-by-design pipeline for Android malware detection that achieves strong performance by avoiding the collection of sensitive user data entirely.