Chapters authored
Perspective Chapter: Distinguishing Encrypted from Non-Encrypted Data
Discriminating between encrypted and non-encrypted information is desired for many purposes. Much of the efforts in this direction in the literature is focused on deploying machine learning methods for the discrimination in streamed data which is transmitted in packets in communication networks. Here, however, the focus and the methods are different. The retrieval of data from computer hard drives that have been seized from police busts against suspected criminals is sometimes not straightforward. Typically the incriminating code, which may be important evidence in subsequent trials, is encrypted and quick deleted. The cryptanalysis of what can be recovered from such hard drives is then subject to time-consuming brute forcing and password guessing. To this end methods for accurate classification of what is encrypted code and what is not is of the essence. Here a procedure for discriminating encrypted code from non-encrypted is derived. Two methods to detect where encrypted data is located in a hard disk drive are detailed using passive change-point detection. Measures of performance of such methods are discussed and a new property for evaluation is suggested. The methods are then evaluated and discussed according to the performance measures.
Part of the book: Lightweight Cryptographic Techniques and Cybersecurity Approaches