Encryption scheme classification: a deep learning approach Online publication date: Sat, 14-Oct-2017
by Jonathan Pan
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 9, No. 4, 2017
Abstract: Encryption has an important role in protecting cyber assets. However the use of weak encryption algorithms is a vulnerability that may be exploited. When exploited, detecting this vulnerability from encrypted data is a very difficult task to undertake. This research explores the use of recent advancement in machine learning algorithms specifically deep learning algorithms to classify encryption schemes based on entropy measurements of encrypted data with no feature engineering. Past research works using various machine learning algorithms have failed to achieve good accuracy results in classification. The research entails applying popular encryption algorithms with block cipher modes over the image dataset from CIFAR10. Two ImageNet winning convolutional neural network deep learning models were used to perform the classification. Transfer learning and layer modification were applied to evaluate the classification effectiveness. This research concludes that deep learning algorithms can be used to perform such classification where other algorithms have failed.
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