51. | Karpisek, Filip; Baggili, Ibrahim; Breitinger, Frank WhatsApp network forensics: Decrypting and understanding the WhatsApp call signaling messages (Journal Article) In: Digital Investigation, vol. 15, pp. 110–118, 2015, ISSN: 1742-2876. @article{KBB15, Abstract WhatsApp is a widely adopted mobile messaging application with over 800 million users. Recently, a calling feature was added to the application and no comprehensive digital forensic analysis has been performed with regards to this feature at the time of writing this paper. In this work, we describe how we were able to decrypt the network traffic and obtain forensic artifacts that relate to this new calling feature which included the: a) WhatsApp phone numbers, b) WhatsApp server IPs, c) WhatsApp audio codec (Opus), d) WhatsApp call duration, and e) WhatsApp's call termination. We explain the methods and tools used to decrypt the traffic as well as thoroughly elaborate on our findings with respect to the WhatsApp signaling messages. Furthermore, we also provide the community with a tool that helps in the visualization of the WhatsApp protocol messages. |
52. | James, Joshua I.; Breitinger, Frank (Ed.) Springer, 2015, ISBN: 978-3-319-25511-8. @book{JB15, |
53. | Baggili, Ibrahim; Oduru, Jeff; Anthony, Kyle; Breitinger, Frank; McGee, Glenn Watch What You Wear: Preliminary Forensic Analysis of Smart Watches (Inproceedings) In: Availability, Reliability and Security (ARES), 2015 10th International Conference on, pp. 303-311, 2015. @inproceedings{BOA15, This work presents preliminary forensic analysis of two popular smart watches, the Samsung Gear 2 Neo and LG G. These wearable computing devices have the form factor of watches and sync with smart phones to display notifications, track footsteps and record voice messages. We posit that as smart watches are adopted by more users, the potential for them becoming a haven for digital evidence will increase thus providing utility for this preliminary work. In our work, we examined the forensic artifacts that are left on a Samsung Galaxy S4 Active phone that was used to sync with the Samsung Gear 2 Neo watch and the LG G watch. We further outline a methodology for physically acquiring data from the watches after gaining root access to them. Our results show that we can recover a swath of digital evidence directly form the watches when compared to the data on the phone that is synced with the watches. Furthermore, to root the LG G watch, the watch has to be reset to its factory settings which is alarming because the process may delete data of forensic relevance. Although this method is forensically intrusive, it may be used for acquiring data from already rooted LG watches. It is our observation that the data at the core of the functionality of at least the two tested smart watches, messages, health and fitness data, e-mails, contacts, events and notifications are accessible directly from the acquired images of the watches, which affirms our claim that the forensic value of evidence from smart watches is worthy of further study and should be investigated both at a high level and with greater specificity and granularity. |
54. | Walnycky, Daniel; Baggili, Ibrahim; Marrington, Andrew; Moore, Jason; Breitinger, Frank Network and device forensic analysis of Android social-messaging applications (Journal Article) In: Digital Investigation, vol. 14, Supplement 1, pp. 77–84, 2015, ISSN: 1742-2876, (The Proceedings of the Fifteenth Annual DFRWS Conference). @article{WBM15, Abstract In this research we forensically acquire and analyze the device-stored data and network traffic of 20 popular instant messaging applications for Android. We were able to reconstruct some or the entire message content from 16 of the 20 applications tested, which reflects poorly on the security and privacy measures employed by these applications but may be construed positively for evidence collection purposes by digital forensic practitioners. This work shows which features of these instant messaging applications leave evidentiary traces allowing for suspect data to be reconstructed or partially reconstructed, and whether network forensics or device forensics permits the reconstruction of that activity. We show that in most cases we were able to reconstruct or intercept data such as: passwords, screenshots taken by applications, pictures, videos, audio sent, messages sent, sketches, profile pictures and more. |
55. | Rathgeb, Christian; Breitinger, Frank; Baier, Harald; Busch, Christoph Towards Bloom filter-based indexing of iris biometric data (Inproceedings) In: Biometrics (ICB), 2015 International Conference on, pp. 422–429, IEEE 2015, (bf Siew-Sngiem Best Poster Award). @inproceedings{7139105, Conventional biometric identification systems require exhaustive 1 : N comparisons in order to identify a bio- metric probe, i.e. comparison time frequently dominates the overall computational workload. Biometric database indexing represents a challenging task since biometric data does not exhibit any natural sorting order. In this paper we present a preliminary study on the feasibility of applying Bloom filters for the purpose of iris biometric database indexing. It is shown that, by constructing a binary tree data structure of Bloom filters extracted from binary iris biometric templates (iris-codes), the search space can be reduced to O(log N ). In experiments, which are carried out on a medium-sized database of N = 256 subjects, biometric performance (accuracy) is maintained for different conventional identification systems. Further, perspectives on how to employ the proposed scheme on large-scale databases are given. |
56. | Satyendra, Gurjar; Baggili, Ibrahim; Breitinger, Frank; Fischer, Alice In: Proceedings of the Conference on Digital Forensics, Security and Law, pp. 57–68, 2015. @inproceedings{SBBF15, Hash functions are widespread in computer sciences and have a wide range of applications such as ensuring integrity in cryptographic protocols, structuring database entries (hash tables) or identifying known files in forensic investigations. Besides their cryptographic requirements, a fundamental property of hash functions is efficient and easy computation which is especially important in digital forensics due to the large amount of data that need to be processed in cases. In this paper, we correlate the runtime efficiency of common hashing algorithms (MD5, SHA-family) and their implementation. Our empirical comparison focuses on C-OpenSSL, Python, Ruby, Java on Windows and Linux and C and WinCrypto API on Windows. The purpose of this paper is to recommend appropriate programming languages and libraries for coding tools that include intensive hashing functionality. In each programming language, we compute the MD5, SHA-1, SHA-256 and SHA-512 digest on datasets from 2 MB to 1 GB. For each language, algorithm and data, we perform multiple runs and compute the average elapsed time. In our experiment, we observed that OpenSSL and languages utilizing OpenSSL (Python and Ruby) perform better across all the hashing algorithms and data sizes on Windows and Linux. However, on Windows, performance of Java (Oracle JDK) and C WinCrypto is comparable to OpenSSL and better for SHA-512. |
57. | Baggili, Ibrahim; Breitinger, Frank Data Sources for Advancing Cyber Forensics: What the Social World Has to Offer (Inproceedings) In: AAAI Spring Symposium Series, 2015. @inproceedings{BB15c, Cyber forensics is fairly new as a scientific discipline and deals with the acquisition, authentication and analysis of digital evidence. One of the biggest challenges in this domain has thus far been real data sources that are available for experimentation. Only a few data sources exist at the time writing of this paper. The authors in this paper deliberate how social media data sources may impact future directions in cyber forensics, and describe how these data sources may be used as new digital forensic artifacts in future investigations. The authors also deliberate how the scientific community may leverage publically accessible social media data to advance the state of the art in Cyber Forensics. |
58. | Breitinger, Frank; Liu, Huajian; Winter, Christian; Baier, Harald; Rybalchenko, Alexey; Steinebach, Martin Towards a Process Model for Hash Functions in Digital Forensics (Inproceedings) In: Gladyshev, Pavel; Marrington, Andrew; Baggili, Ibrahim (Ed.): Digital Forensics and Cyber Crime, pp. 170-186, Springer International Publishing, 2014, ISBN: 978-3-319-14288-3. @inproceedings{BLW14, Handling forensic investigations gets more and more difficult as the amount of data one has to analyze is increasing continuously. A common approach for automated file identification are hash functions. The proceeding is quite simple: a tool hashes all files of a seized device and compares them against a database. Depending on the database, this allows to discard non-relevant (whitelisting) or detect suspicious files (blacklisting). One can distinguish three kinds of algorithms: (cryptographic) hash functions, bytewise approximate matching and semantic approximate matching (a.k.a perceptual hashing) where the main difference is the operation level. The latter one operates on the semantic level while both other approaches consider the byte-level. Hence, investigators have three different approaches at hand to analyze a device. First, this paper gives a comprehensive overview of existing approaches for bytewise and semantic approximate matching (for semantic we focus on images functions). Second, we compare implementations and summarize the strengths and weaknesses of all approaches. Third, we show how to integrate these functions based on a sample use case into one existing process model, the computer forensics field triage process model. |
59. | Rathgeb, Christian; Breitinger, Frank; Busch, Christoph; Baier, Harald On application of bloom filters to iris biometrics (Journal Article) In: IET Biometrics, vol. 3, no. 4, pp. 207-218, 2014, ISSN: 2047-4938. @article{RBBB14, In this study, the application of adaptive Bloom filters to binary iris biometric feature vectors, that is, iris-codes, is proposed. Bloom filters, which have been established as a powerful tool in various fields of computer science, are applied in order to transform iris-codes to a rotation-invariant feature representation. Properties of the proposed Bloom filter-based transform concurrently enable (i) biometric template protection, (ii) compression of biometric data and (iii) acceleration of biometric identification, whereas at the same time no significant degradation of biometric performance is observed. According to these fields of application, detailed investigations are presented. Experiments are conducted on the CASIA-v3 iris database for different feature extraction algorithms. Confirming the soundness of the proposed approach, the application of adaptive Bloom filters achieves rotation-invariant cancelable templates maintaining biometric performance, a compression of templates down to 20-40% of original size and a reduction of bit-comparisons to less than 5% leading to a substantial speed-up of the biometric system in identification mode. |
60. | Breitinger, Frank; Rathgeb, Christian; Baier, Harald An Efficient Similarity Digests Database Lookup - A Logarithmic Divide & Conquer Approach (Journal Article) In: Journal of Digital Forensics, Security and Law (JDFSL), vol. 9, no. 2, pp. 155–166, 2014. @article{BRB14, 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 on the well-known divide and conquer paradigm and builds a Bloom filter-based tree data structure in order to enable an efficient lookup of similarity digests. Further, it is demonstrated that the presented technique is highly scalable operating a trade-off between storage requirements and computational efficiency. We perform a theoretical assessment based on recently published results and reasonable magnitudes of input data, and show that the complexity reduction achieved by the proposed technique yields a 220-fold acceleration of look-up costs. |
61. | Breitinger, Frank; Stivaktakis, Georgios; Baier, Harald FRASH: A Framework to Test Algorithms of Similarity Hashing (Journal Article) In: Digit. Investig., vol. 10, pp. S50–S58, 2014, ISSN: 1742-2876. @article{BSB13, Automated input identification is a very challenging, but also important task. Within computer forensics this reduces the amount of data an investigator has to look at by hand. Besides identifying exact duplicates, which is mostly solved using cryptographic hash functions, it is necessary to cope with similar inputs (e.g., different versions of a file), embedded objects (e.g., a JPG within a Word document), and fragments (e.g., network packets), too. Over the recent years a couple of different similarity hashing algorithms were published. However, due to the absence of a definition and a test framework, it is hardly possible to evaluate and compare these approaches to establish them in the community. The paper at hand aims at providing an assessment methodology and a sample implementation called FRASH: a framework to test algorithms of similarity hashing. First, we describe common use cases of a similarity hashing algorithm to motivate our two test classes efficiency and sensitivity & robustness. Next, our open and freely available framework is briefly described. Finally, we apply FRASH to the well-known similarity hashing approaches ssdeep and sdhash to show their strengths and weaknesses. |
62. | Breitinger, Frank Technical University Darmstadt, 2014. @phdthesis{FB-DISS, |
63. | Breitinger, Frank; Stivaktakis, Georgios; Roussev, Vassil Evaluating Detection Error Trade-offs for Bytewise Approximate Matching Algorithms (Journal Article) In: Digital Investigation, vol. 11, no. 2, pp. 81–89, 2014, ISSN: 1742-2876, (bf Best Paper Award). @article{BSR14, Bytewise approximate matching is a relatively new area within digital forensics, but its importance is growing quickly as practitioners are looking for fast methods to analyze the increasing amounts of data in forensic investigations. The essential idea is to complement the use of cryptographic hash functions to detect data objects with bytewise identical representation with the capability to find objects with bytewise similar representations. Unlike cryptographic hash functions, which have been studied and tested for a long time, approximate matching ones are still in their early development stages, and have been evaluated in a somewhat ad-hoc manner. Recently, the FRASH testing framework has been proposed as a vehicle for developing a set of standardized tests for approximate matching algorithms; the aim is to provide a useful guide for understanding and comparing the absolute and relative performance of different algorithms. The contribution of this work is twofold: a) expand FRASH with automated tests for quantifying approximate matching algorithm behavior with respect to precision and recall; and b) present a case study of two algorithms already in use-sdhash and ssdeep. |
64. | File Detection On Network Traffic Using Approximate Matching (Journal Article) In: Journal of Digital Forensics, Security and Law (JDFSL), vol. 9, no. 2, pp. 23–36, 2014, (bf Best Paper Award). @article{BB14, 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 fuzzy hashing) which is very common in digital forensics to correlate similar files. This paper demonstrates how to optimize and apply them on single network packets. Our contribution is a straightforward concept which does not need a comprehensive configuration: hash the file and store the digest in the database. Within our experiments we obtained false positive rates between 10−4 and 10−5 and an algorithm throughput of over 650 Mbit/s. |
65. | Breitinger, Frank; Guttman, Barbara; McCarrin, Michael; Roussev, Vassil; White, Douglas Approximate Matching: Definition and Terminology (Technical Report) National Institute of Standards and Technologies 2014. @techreport{AM-DEF, |
66. | Breitinger, Frank; Roussev, Vassil Automated evaluation of approximate matching algorithms on real data (Journal Article) In: Digital Investigation, vol. 11, Supplement 1, no. 0, pp. S10 - S17, 2014, ISSN: 1742-2876, (Proceedings of the First Annual DFRWS Europe). @article{BR14, Abstract Bytewise approximate matching is a relatively new area within digital forensics, but its importance is growing quickly as practitioners are looking for fast methods to screen and analyze the increasing amounts of data in forensic investigations. The essential idea is to complement the use of cryptographic hash functions to detect data objects with bytewise identical representation with the capability to find objects with bytewise similar representations. Unlike cryptographic hash functions, which have been studied and tested for a long time, approximate matching ones are still in their early development stages and evaluation methodology is still evolving. Broadly, prior approaches have used either a human in the loop to manually evaluate the goodness of similarity matches on real world data, or controlled (pseudo-random) data to perform automated evaluation. This work's contribution is to introduce automated approximate matching evaluation on real data by relating approximate matching results to the longest common substring (LCS). Specifically, we introduce a computationally efficient LCS approximation and use it to obtain ground truth on the t5 set. Using the results, we evaluate three existing approximate matching schemes relative to LCS and analyze their performance. |
67. | Breitinger, Frank; Baier, Harald; White, Douglas On the database lookup problem of approximate matching (Journal Article) In: Digital Investigation, vol. 11, Supplement 1, no. 0, pp. S1–S9, 2014, ISSN: 1742-2876, (Proceedings of the First Annual DFRWS Europe). @article{BBW14, Abstract Investigating seized devices within digital forensics gets more and more difficult due to the increasing amount of data. Hence, a common procedure uses automated file identification which reduces the amount of data an investigator has to look at by hand. Besides identifying exact duplicates, which is mostly solved using cryptographic hash functions, it is also helpful to detect similar data by applying approximate matching. Let x denote the number of digests in a database, then the lookup for a single similarity digest has the complexity of O(x). In other words, the digest has to be compared against all digests in the database. In contrast, cryptographic hash values are stored within binary trees or hash tables and hence the lookup complexity of a single digest is O(log2(x)) or O(1), respectively. In this paper we present and evaluate a concept to extend existing approximate matching algorithms, which reduces the lookup complexity from O(x) to O(1). Therefore, instead of using multiple small Bloom filters (which is the common procedure), we demonstrate that a single, huge Bloom filter has a far better performance. Our evaluation demonstrates that current approximate matching algorithms are too slow (e.g., over 21 min to compare 4457 digests of a common file corpus against each other) while the improved version solves this challenge within seconds. Studying the precision and recall rates shows that our approach works as reliably as the original implementations. We obtain this benefit by accuracy--the comparison is now a file-against-set comparison and thus it is not possible to see which file in the database is matched. |
68. | Breitinger, Frank; Ziroff, Georg; Lange, Steffen; Baier, Harald Similarity Hashing Based on Levenshtein Distances (Inproceedings) In: Peterson, Gilbert; Shenoi, Sujeet (Ed.): Advances in Digital Forensics X, pp. 133-147, Springer Berlin Heidelberg, 2014, ISBN: 978-3-662-44951-6. @inproceedings{BZLB14, It is increasingly common in forensic investigations to use automated pre-processing techniques to reduce the massive volumes of data that are encountered. This is typically accomplished by comparing fingerprints (typically cryptographic hashes) of files against existing databases. In addition to finding exact matches of cryptographic hashes, it is necessary to find approximate matches corresponding to similar files, such as different versions of a given file. This paper presents a new stand-alone similarity hashing approach called saHash, which has a modular design and operates in linear time. saHash is almost as fast as SHA-1 and more efficient than other approaches for approximate matching. The similarity hashing algorithm uses four sub-hash functions, each producing its own hash value. The four sub-hashes are concatenated to produce the final hash value. This modularity enables sub-hash functions to be added or removed, e.g., if an exploit for a sub-hash function is discovered. Given the hash values of two byte sequences, saHash returns a lower bound on the number of Levenshtein operations between the two byte sequences as their similarity score. The robustness of saHash is verified by comparing it with other approximate matching approaches such as sdhash. |
69. | Breitinger, Frank; Winter, Christian; Yannikos, York; Fink, Tobias; Seefried, Michael Using Approximate Matching to Reduce the Volume of Digital Data (Inproceedings) In: Peterson, Gilbert; Shenoi, Sujeet (Ed.): Advances in Digital Forensics X, pp. 149-163, Springer Berlin Heidelberg, 2014, ISBN: 978-3-662-44951-6. @inproceedings{BWY14, Digital forensic investigators frequently have to search for relevant files in massive digital corpora -- a task often compared to finding a needle in a haystack. To address this challenge, investigators typically apply cryptographic hash functions to identify known files. However, cryptographic hashing only allows the detection of files that exactly match the known file hash values or fingerprints. This paper demonstrates the benefits of using approximate matching to locate relevant files. The experiments described in this paper used three test images of Windows XP, Windows 7 and Ubuntu 12.04 systems to evaluate fingerprint-based comparisons. The results reveal that approximate matching can improve file identification -- in one case, increasing the identification rate from 1.82% to 23.76%. |
70. | Breitinger, Frank; Baier, Harald Similarity Preserving Hashing: Eligible Properties and a New Algorithm MRSH-v2 (Inproceedings) In: Rogers, Marcus; Seigfried-Spellar, KathrynC. (Ed.): Digital Forensics and Cyber Crime, pp. 167-182, Springer Berlin Heidelberg, 2013, ISBN: 978-3-642-39890-2. @inproceedings{BB12d, Hash functions are a widespread class of functions in computer science and used in several applications, e.g. in computer forensics to identify known files. One basic property of cryptographic hash func- tions is the avalanche effect that causes a significantly different output if an input is changed slightly. As some applications also need to identify similar files (e.g. spam/virus detection) this raised the need for similarity preserving hashing. In recent years, several approaches came up, all with different namings, properties, strengths and weaknesses which is due to a missing definition. Based on the properties and use cases of traditional hash functions this paper discusses a uniform naming and properties which is a first step towards a suitable definition of similarity preserving hashing. Additionally, we extend the algorithm MRSH for similarity preserving hashing to its successor MRSH-v2, which has three specialties. First, it fulfills all our proposed defining properties, second, it outperforms existing approaches especially with respect to run time performance and third it has two detections modes. The regular mode of MRSH-v2 is used to identify similar files whereas the f-mode is optimal for fragment detection, i.e. to identify similar parts of a file. |
71. | Rathgeb, Christian; Breitinger, Frank; Busch, Christoph Alignment-free cancelable iris biometric templates based on adaptive bloom filters (Inproceedings) In: Biometrics (ICB), 2013 International Conference on, pp. 1-8, 2013. @inproceedings{RBB13, Biometric characteristics are largely immutable, i.e. unprotected storage of biometric data provokes serious privacy threats, e.g. identity theft, limited re-newability, or cross-matching. In accordance with the ISO/IEC 24745 standard, technologies of cancelable biometrics offer solutions to biometric information protection by obscuring biometric signal in a non-invertible manner, while biometric comparisons are still feasible in the transformed domain. In the presented work alignment-free cancelable iris biometrics based on adaptive Bloom filters are proposed. Bloom filter-based representations of binary biometric templates (iris-codes) enable an efficient alignment-invariant biometric comparison while a successive mapping of parts of a binary biometric template to a Bloom filter represents an irreversible transform. In experiments, which are carried out on the CASIA - v 3 iris database, it is demonstrated that the proposed system maintains biometric performance for diverse iris recognition algorithms, protecting biometric templates at high security levels. |
72. | Breitinger, Frank; Astebøl, Knut; Baier, Harald; Busch, Christoph mvHash-B - A New Approach for Similarity Preserving Hashing (Inproceedings) In: IT Security Incident Management and IT Forensics (IMF), 2013 Seventh International Conference on, pp. 33-44, 2013. @inproceedings{BABB13, The handling of hundreds of thousands of files is a major challenge in today's IT forensic investigations. In order to cope with this information overload, investigators use fingerprints (hash values) to identify known files automatically using blacklists or whitelists. Besides detecting exact duplicates it is helpful to locate similar files by using similarity preserving hashing (SPH), too. We present a new algorithm for similarity preserving hashing. It is based on the idea of majority voting in conjunction with run length encoding to compress the input data and uses Bloom filters to represent the fingerprint. It is therefore called mvHash-B. Our assessment shows that mvHash-B is superior to other SPHs with respect to run time efficiency: It is almost as fast as SHA-1 and thus faster than any other SPH algorithm. Additionally the hash value length is approximately 0.5% of the input length and hence outperforms most existing algorithms. Finally, we show that the robustness of mvHash-B against active manipulation is sufficient for practical purposes. |
73. | Breitinger, Frank; Petrov, Kaloyan Reducing the Time Required for Hashing Operations (Inproceedings) In: Peterson, Gilbert; Shenoi, Sujeet (Ed.): Advances in Digital Forensics IX, pp. 101-117, Springer Berlin Heidelberg, 2013, ISBN: 978-3-642-41147-2. @inproceedings{BK13, Due to the increasingly massive amounts of data that need to be analyzed in digital forensic investigations, it is necessary to automatically recognize suspect files and filter out non-relevant files. To achieve this goal, digital forensic practitioners employ hashing algorithms to classify files into known-good, known-bad and unknown files. However, a typical personal computer may store hundreds of thousands of files and the task becomes extremely time-consuming. This paper attempts to address the problem using a framework that speeds up processing by using multiple threads. Unlike a typical multithreading approach, where the hashing algorithm is performed by multiple threads, the proposed framework incorporates a dedicated prefetcher thread that reads files from a device. Experimental results demonstrate a runtime efficiency of nearly 40% over single threading. |
74. | Breitinger, Frank; Baier, Harald Performance Issues About Context-Triggered Piecewise Hashing (Inproceedings) In: Gladyshev, Pavel; Rogers, MarcusK. (Ed.): Digital Forensics and Cyber Crime, pp. 141-155, Springer Berlin Heidelberg, 2012, ISBN: 978-3-642-35514-1. @inproceedings{BB12a, A hash function is a well-known method in computer science to map arbitrary large data to bit strings of a fixed short length. This property is used in computer forensics to identify known files on base of their hash value. As of today, in a pre-step process hash values of files are generated and stored in a database; typically a cryptographic hash function like MD5 or SHA-1 is used. Later the investigator computes hash values of files, which he finds on a storage medium, and performs look ups in his database. Due to security properties of cryptographic hash functions, they can not be used to identify similar files. Therefore Jesse Kornblum proposed a similarity preserving hash function to identify similar files. This paper discusses the efficiency of Kornblum's approach. We present some enhancements that increase the performance of his algorithm by 55% if applied to a real life scenario. Furthermore, we discuss some characteristics of a sample Windows XP system, which are relevant for the performance of Kornblum's approach. |
75. | Breitinger, Frank; Baier, Harald Properties of a similarity preserving hash function and their realization in sdhash (Inproceedings) In: Information Security for South Africa (ISSA), pp. 1-8, 2012. @inproceedings{BB12c, Finding similarities between byte sequences is a complex task and necessary in many areas of computer science, e.g., to identify malicious files or spam. Instead of comparing files against each other, one may apply a similarity preserving compression function (hash function) first and do the comparison for the hashes. Although we have different approaches, there is no clear definition / specification or needed properties of such algorithms available. This paper presents four basic properties for similarity pre- serving hash functions that are partly related to the properties of cryptographic hash functions. Compression and ease of computation are borrowed from traditional hash functions and define the hash value length and the performance. As every byte is expected to influence the hash value, we introduce coverage. Similarity score describes the need for a comparison function for hash values. We shortly discuss these properties with respect to three existing approaches and finally have a detailed view on the promising approach sdhash. However, we uncovered some bugs and other peculiarities of the implementation of sdhash. Finally we conclude that sdhash has the potential to be a robust similarity preserving digest algorithm, but there are some points that need to be improved. |
76. | Breitinger, Frank; Baier, Harald; Beckingham, Jesse Security and implementation analysis of the similarity digest sdhash (Inproceedings) In: First International Baltic Conference on Network Security & Forensics (NeSeFo), 2012. (BibTeX) @inproceedings{BBB12, |
77. | Breitinger, Frank; Baier, Harald A fuzzy hashing approach based on random sequences and hamming distance (Inproceedings) In: Proceedings of the Conference on Digital Forensics, Security and Law, pp. 89–100, 2012. @inproceedings{BB12b, Hash functions are well-known methods in computer science to map arbitrary large input to bit strings of a fixed length that serve as unique input identifier/fingerprints. A key property of cryptographic hash functions is that even if only one bit of the input is changed the output behaves pseudo randomly and therefore similar files cannot be identified. However, in the area of computer forensics it is also necessary to find similar files (e.g. different versions of a file), wherefore we need a similarity preserving hash function also called fuzzy hash function. In this paper we present a new approach for fuzzy hashing called bbHash. It is based on the idea to `rebuild' an input as good as possible using a fixed set of randomly chosen byte sequences called building blocks of byte length l (e.g. l = 128). The proceeding is as follows: slide through the input byte-by-byte, read out the current input byte sequence of length l, and compute the Hamming distances of all building blocks against the current input byte sequence. Each building block with Hamming distance smaller than a certain threshold contributes the file's bbHash. We discuss (dis-)advantages of our bbHash to further fuzzy hash approaches. A key property of bbHash is that it is the first fuzzy hashing approach based on a comparison to external data structures. |
78. | Baier, Harald; Breitinger, Frank Security Aspects of Piecewise Hashing in Computer Forensics (Inproceedings) In: IT Security Incident Management and IT Forensics (IMF), 2011 Sixth International Conference on, pp. 21-36, 2011. @inproceedings{BB11, Although hash functions are a well-known method in computer science to map arbitrary large data to bit strings of a fixed length, their use in computer forensics is currently very limited. As of today, in a pre-step process hash values of files are generated and stored in a database, typically a cryptographic hash function like MD5 or SHA-1 is used. Later the investigator computes hash values of files, which he finds on a storage medium, and performs look ups in his database. This approach has several drawbacks, which have been sketched in the community, and some alternative approaches have been proposed. The most popular one is due to Jesse Kornblum, who transferred ideas from spam detection to computer forensics in order to identify similar files. However, his proposal lacks a thorough security analysis. It is therefore one aim of the paper at hand to present some possible attack vectors of an active adversary to bypass Kornblum's approach. Furthermore, we present a pseudo random number generator being both more efficient and more random compared to Kornblum's pseudo random number generator. |
79. | Breitinger, Frank Security Aspects of fuzzy hashing (Masters Thesis) University of Applied Sciences Darmstadt, 2011. (BibTeX) @mastersthesis{FB-master, |
80. | Breitinger, Frank; Nickel, Claudia User Survey on Phone Security and Usage (Inproceedings) In: Brömme, Arslan; Busch, Christoph (Ed.): BIOSIG, pp. 139-144, GI, 2010, ISBN: 978-3-88579-258-1. @inproceedings{BN10, Mobile phones are widely used nowadays and during the last years devel- oped from simple phones to small computers with an increasing number of features. These result in a wide variety of data stored on the devices which could be a high security risk in case of unauthorized access. A comprehensive user survey was con- ducted to get information about what data is really stored on the mobile devices, how it is currently protected and if biometric authentication methods could improve the cur- rent state. This paper states the results from about 550 users of mobile devices. The analysis revealed a very low securtiy level of the devices. This is partly due to a low security awareness of their owners and partly due to the low acceptance of the offered authentication method based on PIN. Further results like the experiences with mobile thefts and the willingness to use biometric authentication methods as alternative to PIN authentication are also stated. |
All publications by year
51. | WhatsApp network forensics: Decrypting and understanding the WhatsApp call signaling messages (Journal Article) In: Digital Investigation, vol. 15, pp. 110–118, 2015, ISSN: 1742-2876. |
52. | Springer, 2015, ISBN: 978-3-319-25511-8. |
53. | Watch What You Wear: Preliminary Forensic Analysis of Smart Watches (Inproceedings) In: Availability, Reliability and Security (ARES), 2015 10th International Conference on, pp. 303-311, 2015. |
54. | Network and device forensic analysis of Android social-messaging applications (Journal Article) In: Digital Investigation, vol. 14, Supplement 1, pp. 77–84, 2015, ISSN: 1742-2876, (The Proceedings of the Fifteenth Annual DFRWS Conference). |
55. | Towards Bloom filter-based indexing of iris biometric data (Inproceedings) In: Biometrics (ICB), 2015 International Conference on, pp. 422–429, IEEE 2015, (bf Siew-Sngiem Best Poster Award). |
56. | In: Proceedings of the Conference on Digital Forensics, Security and Law, pp. 57–68, 2015. |
57. | Data Sources for Advancing Cyber Forensics: What the Social World Has to Offer (Inproceedings) In: AAAI Spring Symposium Series, 2015. |
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