Classifying file type of stream ciphers in depth using neural networks

Summary form only given. Ciphers encrypted with the same key are called ciphers in depth. A depth attack is a form of cryptanalysis that takes advantage of finding ciphers in depth and could break a cryptosystem without even knowing the encryption algorithm. The first task in a depth attack is to cluster ciphers according to their common keys and is called depth detection. Then, one may want to know the file type of the underlying message of each cipher. In this research, depth detection is accomplished for stream ciphers with a hit rate of 99.5%. Next, ciphers in depth are further classified according to the file types of their underlying messages with an accuracy of over 90%. One important goal of this research is not to use the structure and key words of any specific file types as this allows the result to be applied to general file types. Also, the features extracted from the test samples for classification are simple ones, leaving room for improving the performance by adopting more complicated features.