Leveraging CNNs and Entropy-Based Feature Selection to Identify Potential Malware Artifacts of Interest
This diary explores a novel methodology for classifying malware by integrating entropy-driven feature selection with a specialized Convolutional Neural Network (CNN). Motivated by the increasing obfuscation tactics used by modern malware authors, we will focus on capturing high-entropy segments within files, regions most likely to harbor malicious functionality, and feeding these distinct byte patterns into our model.
Malware found on npm infecting local package with reverse shell
Researchers at Reversinglabs found two malicious NPM packages, ethers-provider2, and ethers-providerz that patch the well known (and not malicious) ethers package to add a reverse shell and downloader.
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