Malware Classification using Locality Sensitive Hashing and Neural Networks - Friborg, Ludwig; Peiser, Stefan Carl (My University, 2019)
In this thesis, we explore the idea of using locality sensitive hashes as input features to a feedforward neural network to perform static analysis to detect JavaScript malware. An experiment is conducted using a dataset containing 1.5M evenly distributed benign and malicious samples provided by the anti-malware company Cyren, which is the industry collaborator for this thesis. Four different locality sensitive hashing algorithms are tested and evaluated: Nilsimsa, ssdeep, TLSH, and SDHASH. The results show a high prediction accuracy of 98.05% and low false positive and negative rates of 0.94% and 2.69% for the best performing models. These results show that LSH based neural networks are a competitive option against other state-of-the-art JavaScript malware classification solutions.
Keywords: locality sensitive hashing; static analysis; malware detection; artificial neural networks; machine learning; feature extraction