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Full-Text Articles in Social and Behavioral Sciences
Bytewise Approximate Matching: The Good, The Bad, And The Unknown, Vikram S. Harichandran, Frank Breitinger, Ibrahim Baggili
Bytewise Approximate Matching: The Good, The Bad, And The Unknown, Vikram S. Harichandran, Frank Breitinger, Ibrahim Baggili
Journal of Digital Forensics, Security and Law
Hash functions are established and well-known in digital forensics, where they are commonly used for proving integrity and file identification (i.e., hash all files on a seized device and compare the fingerprints against a reference database). However, with respect to the latter operation, an active adversary can easily overcome this approach because traditional hashes are designed to be sensitive to altering an input; output will significantly change if a single bit is flipped. Therefore, researchers developed approximate matching, which is a rather new, less prominent area but was conceived as a more robust counterpart to traditional hashing. Since the conception …
File Detection On Network Traffic Using Approximate Matching, Frank Breitinger, Ibrahim Baggili
File Detection On Network Traffic Using Approximate Matching, Frank Breitinger, Ibrahim Baggili
Journal of Digital Forensics, Security and Law
In recent years, Internet technologies changed enormously and allow faster Internet connections, higher data rates and mobile usage. Hence, it is possible to send huge amounts of data / files easily which is often used by insiders or attackers to steal intellectual property. As a consequence, data leakage prevention systems (DLPS) have been developed which analyze network traffic and alert in case of a data leak. Although the overall concepts of the detection techniques are known, the systems are mostly closed and commercial. Within this paper we present a new technique for network traffic analysis based on approximate matching (a.k.a …
An Efficient Similarity Digests Database Lookup – A Logarithmic Divide & Conquer Approach, Frank Breitinger, Christian Rathgeb, Harald Baier
An Efficient Similarity Digests Database Lookup – A Logarithmic Divide & Conquer Approach, Frank Breitinger, Christian Rathgeb, Harald Baier
Journal of Digital Forensics, Security and Law
Investigating seized devices within digital forensics represents a challenging task due to the increasing amount of data. Common procedures utilize automated file identification, which reduces the amount of data an investigator has to examine manually. In the past years the research field of approximate matching arises to detect similar data. However, if n denotes the number of similarity digests in a database, then the lookup for a single similarity digest is of complexity of O(n). This paper presents a concept to extend existing approximate matching algorithms, which reduces the lookup complexity from O(n) to O(log(n)). Our proposed approach is based …