All publications by year

@article{PB20,
title = {Artifacts for detecting timestamp manipulation in NTFS on Windows and their reliability},
author = {David Palmbach and Frank Breitinger},
issn = {1742-2876},
year = {2020},
date = {2020-03-13},
journal = {Digital Investigation},
abstract = {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.},
note = {(To appear at the Digital Forensics Research Workshop EU (Oxford, March 2020))},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{MOIA2020101676,
title = {The impact of excluding common blocks for approximate matching},
author = {Vitor Hugo Galhardo Moia and Frank Breitinger and Marco Aurélio Amaral Henriques},
url = {http://www.sciencedirect.com/science/article/pii/S0167404819302159},
doi = {https://doi.org/10.1016/j.cose.2019.101676},
issn = {0167-4048},
year = {2019},
date = {2019-11-28},
journal = {Computers & Security},
volume = {89},
pages = {101676},
abstract = {Approximate matching functions allow the identification of similarity (bytewise level) in a very efficient way, by creating and comparing compact representations of objects (a.k.a digests). However, many similarity matches occur due to common data that repeats over many different files and consist of inner structure, header and footer information, color tables, font specifications, etc.; data created by applications and not generated by users. Most of the times, this sort of information is less relevant from an investigator perspective and should be avoided. In this work, we show how the common data can be identified and filtered out by using approximate matching, as well as how they are spread over different file types and their frequency. We assess the impact on similarity when removing it (i.e., in the number of matches) and the effects on performance. Our results show that for a small price on performance, a reduction about 87% on the number of matches can be achieved when removing such data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{BTH20,
title = {A survey on smartphone user's security choices, awareness and education},
author = {Frank Breitinger and Ryan Tully-Doyle and Courtney Hassenfeldt},
url = {http://www.sciencedirect.com/science/article/pii/S0167404819301919},
doi = {10.1016/j.cose.2019.101647},
issn = {0167-4048},
year = {2019},
date = {2019-10-11},
journal = {Computers & Security},
volume = {88},
pages = {101647},
abstract = {Smartphones contain a significant amount of personal data. Additionally, they are always in the user's possession, which allows them to be abused for tracking (e.g., GPS, Bluetooth or WiFi tracking). In order to not reveal private information, smartphone users should secure their devices by setting lock screen protection, using third party security applications, and choosing appropriate security settings (often, default settings are inadequate). In this paper, we mount a survey to explore user choices, awareness and education with respect to cybersecurity. In comparison with prior work, we take the user's cybersecurity familiarity into consideration in the analysis of user practices as well as have a strong focus on the younger generations, Y and Z. Our survey findings suggest that most users have appropriate lock screen settings to protect their phones from physical access; however, they disregard other security best practices, e.g., not using a VPN when connecting to a public WiFi or turning off unused features (regardless of level of expertise). Compared to desktop computers, smartphones are less secured and fewer third party security products are installed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{LSB19,
title = {On efficiency of artifact lookup strategies in digital forensics},
author = {Lorenz Liebler and Patrick Schmitt and Harald Baier and Frank Breitinger},
url = {http://www.sciencedirect.com/science/article/pii/S1742287619300301},
doi = {https://doi.org/10.1016/j.diin.2019.01.020},
issn = {1742-2876},
year = {2019},
date = {2019-04-24},
journal = {Digital Investigation},
volume = {28},
pages = {S116 - S125},
abstract = {In recent years different strategies have been proposed to handle the problem of ever-growing digital forensic databases. One concept to deal with this data overload is data reduction, which essentially means to separate the wheat from the chaff, e.g., to filter in forensically relevant data. A prominent technique in the context of data reduction are hash-based solutions. Data reduction is achieved because hash values (of possibly large data input) are much smaller than the original input. Today's approaches of storing hash-based data fragments reach from large scale multithreaded databases to simple Bloom filter representations. One main focus was put on the field of approximate matching, where sorting is a problem due to the fuzzy nature of the approximate hashes. A crucial step during digital forensic analysis is to achieve fast query times during lookup (e.g., against a blacklist), especially in the scope of small or ordinary resource availability. However, a comparison of different database and lookup approaches is considerably hard, as most techniques partially differ in considered use-case and integrated features, respectively. In this work we discuss, reassess and extend three widespread lookup strategies suitable for storing hash-based fragments: (1) Hashdatabase for hash-based carving (hashdb), (2) hierarchical Bloom filter trees (hbft) and (3) flat hash maps (fhmap). We outline the capabilities of the different approaches, integrate new extensions, discuss possible features and perform a detailed evaluation with a special focus on runtime efficiency. Our results reveal major advantages for fhmap in case of runtime performance and applicability. hbft showed a comparable runtime efficiency in case of lookups, but hbft suffers from pitfalls with respect to extensibility and maintenance. Finally, hashdb performs worst in case of a single core environment in all evaluation scenarios. However, hashdb is the only candidate which offers full parallelization capabilities, transactional features, and a Single-level storage.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{RBB19,
title = {Blockchain-Based Distributed Cloud Storage Digital Forensics: Where's the Beef?},
author = {Joseph Ricci and Ibrahim Baggili and Frank Breitinger},
doi = {10.1109/MSEC.2018.2875877},
issn = {1540-7993},
year = {2019},
date = {2019-01-01},
journal = {IEEE Security & is Privacy},
volume = {17},
number = {1},
pages = {34-42},
abstract = {The current state of the art in digital forensics has primarily focused on the acquisition of data from cloud storage. Here, we present a new challenge in digital forensics: blockchain-based distributed cloud storage, using STORJ as a technology example.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Debinski2018,
title = {Timeline2GUI: A Log2Timeline CSV parser and training scenarios},
author = {Mark Debinski and Frank Breitinger and Parvathy Mohan},
url = {http://www.sciencedirect.com/science/article/pii/S1742287618303232},
doi = {10.1016/j.diin.2018.12.004},
issn = {1742-2876},
year = {2018},
date = {2018-12-31},
journal = {Digital Investigation},
volume = {28},
pages = {34 - 43},
abstract = {Crimes involving digital evidence are getting more complex due to the increasing storage capacities and utilization of devices. Event reconstruction (i.e., understanding the timeline) is an essential step for investigators to understand a case where a prominent tool is Log2Timeline (a tool that creates super timelines which is a combination of several log files and events throughout a system). While these timelines provide great evidence and help to understand a case, they are complex and require tools as well as training scenarios. In this paper we present Timeline2GUI an easy-to-use python implementation to analyze CSV log files create by Log2Timeline. Additionally, we present three training scenarios -- beginner, intermediate and advanced -- to practice timeline analysis skills as well as familiarity with visualization tools. Lastly, we provide a comprehensive overview of tools.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{GSB18,
title = {Experience constructing the Artifact Genome Project (AGP): Managing the domain's knowledge one artifact at a time},
author = {Cinthya Grajeda and Laura Sanchez and Ibrahim Baggili and Devon Clark and Frank Breitinger},
url = {http://www.sciencedirect.com/science/article/pii/S1742287618302007},
doi = {10.1016/j.diin.2018.04.021},
issn = {1742-2876},
year = {2018},
date = {2018-07-15},
journal = {Digital Investigation},
volume = {26},
pages = {S47 - S58},
abstract = {While various tools have been created to assist the digital forensics community with acquiring, processing, and organizing evidence and indicating the existence of artifacts, very few attempts have been made to establish a centralized system for archiving artifacts. The Artifact Genome Project (AGP) has aimed to create the largest vetted and freely available digital forensics repository for Curated Forensic Artifacts (CuFAs). This paper details the experience of building, implementing, and maintaining such a system by sharing design decisions, lessons learned, and future work. We also discuss the impact of AGP in both the professional and academic realms of digital forensics. Our work shows promise in the digital forensics academic community to champion the effort in curating digital forensic artifacts by integrating AGP into courses, research endeavors, and collaborative projects.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{ricci2018survey,
title = {Survey results on adults and cybersecurity education},
author = {Ricci, Joseph and Breitinger, Frank and Baggili, Ibrahim},
url = {https://doi.org/10.1007/s10639-018-9765-8},
doi = {10.1007/s10639-018-9765-8},
issn = {1360-2357},
year = {2018},
date = {2018-07-11},
journal = {Education and Information Technologies},
pages = {1--19},
abstract = {Cyberattacks and identity theft are common problems nowadays where researchers often say that humans are the weakest link the security chain. Therefore, this survey focused on analyzing the interest for adults for `cyber threat eduction seminars', e.g., how to project themselves and their loved ones. Specifically, we asked questions to understand a possible audience, willingness for paying / time commitment, or fields of interest as well as background and previous training experience. The survey was conducted in late 2016 and taken by 233 participants. The results show that many are worried about cyber threats and about their children exploring the online domain. However, seminars do not seem to be a priority as many individuals were only willing to spend 1-1.5h on seminars.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{MBB17a,
title = {Availability of datasets for digital forensics -- And what is missing},
author = {Cinthya Grajeda and Frank Breitinger and Ibrahim Baggili},
url = {http://www.sciencedirect.com/science/article/pii/S1742287617301913},
doi = {10.1016/j.diin.2017.06.004},
issn = {1742-2876},
year = {2017},
date = {2017-08-05},
journal = {Digital Investigation},
volume = {22, Supplement},
pages = {S94 - S105},
abstract = {This paper targets two main goals. First, we want to provide an overview of available datasets that can be used by researchers and where to find them. Second, we want to stress the importance of sharing datasets to allow researchers to replicate results and improve the state of the art. To answer the first goal, we analyzed 715 peer-reviewed research articles from 2010 to 2015 with focus and relevance to digital forensics to see what datasets are available and focused on three major aspects: (1) the origin of the dataset (e.g., real world vs. synthetic), (2) if datasets were released by researchers and (3) the types of datasets that exist. Additionally, we broadened our results to include the outcome of online search results. We also discuss what we think is missing. Overall, our results show that the majority of datasets are experiment generated (56.4%) followed by real world data (36.7%). On the other hand, 54.4% of the articles use existing datasets while the rest created their own. In the latter case, only 3.8% actually released their datasets. Finally, we conclude that there are many datasets for use out there but finding them can be challenging.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{DKBB17,
title = {Leveraging the SRTP protocol for over-the-network memory acquisition of a GE Fanuc Series 90-30},
author = {George Denton and Filip Karpisek and Frank Breitinger and Ibrahim Baggili},
url = {http://www.sciencedirect.com/science/article/pii/S1742287617301925},
doi = {10.1016/j.diin.2017.06.005},
issn = {1742-2876},
year = {2017},
date = {2017-08-05},
journal = {Digital Investigation},
volume = {22, Supplement},
pages = {S26 - S38},
abstract = {Abstract Programmable Logic Controllers (PLCs) are common components implemented across many industries such as manufacturing, water management, travel, aerospace and hospitals to name a few. Given their broad deployment in critical systems, they became and still are a common target for cyber attacks; the most prominent one being Stuxnet. Often PLCs (especially older ones) are only protected by an outer line of defense (e.g., a firewall) but once an attacker gains access to the system or the network, there might not be any other defense layers. In this scenario, a forensic investigator should not rely on the existing software as it might have been compromised. Therefore, we reverse engineered the GE-SRTP network protocol using a GE Fanuc Series 90-30 PLC and provide two major contributions: We first describe the Service Request Transport protocol (GE-SRTP) which was invented by General Electric (GE) and is used by many of their Ethernet connected controllers. Note, to the best of our knowledge, prior to this work, no publicly available documentation on the protocol was available affording users' security by obscurity. Second, based on our understanding of the protocol, we implemented a software application that allows direct network-based communication with the PLC (no intermediate server is needed). While the tool's forensic mode is harmless and only allows for reading registers, we discovered that one can manipulate/write to the registers in its default configuration, e.g., turn off the PLC, or manipulate the items/processes it controls.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{CMBB17,
title = {DROP (DRone Open source Parser) your drone: Forensic analysis of the DJI Phantom III},
author = {Devon R. Clark and Christopher Meffert and Ibrahim Baggili and Frank Breitinger},
url = {http://www.sciencedirect.com/science/article/pii/S1742287617302001},
doi = {10.1016/j.diin.2017.06.013},
issn = {1742-2876},
year = {2017},
date = {2017-08-05},
journal = {Digital Investigation},
volume = {22, Supplement},
pages = {S3 - S14},
abstract = {Abstract The DJI Phantom III drone has already been used for malicious activities (to drop bombs, remote surveillance and plane watching) in 2016 and 2017. At the time of writing, DJI was the drone manufacturer with the largest market share. Our work presents the primary thorough forensic analysis of the DJI Phantom III drone, and the primary account for proprietary file structures stored by the examined drone. It also presents the forensically sound open source tool DRone Open source Parser (DROP) that parses proprietary DAT files extracted from the drone's nonvolatile internal storage. These DAT files are encrypted and encoded. The work also shares preliminary findings on TXT files, which are also proprietary, encrypted, encoded, files found on the mobile device controlling the drone. These files provided a slew of data such as GPS locations, battery, flight time, etc. By extracting data from the controlling mobile device, and the drone, we were able to correlate data and link the user to a specific device based on extracted metadata. Furthermore, results showed that the best mechanism to forensically acquire data from the tested drone is to manually extract the SD card by disassembling the drone. Our findings illustrated that the drone should not be turned on as turning it on changes data on the drone by creating a new DAT file, but may also delete stored data if the drone's internal storage is full.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{ZBB17,
title = {Breaking into the vault: privacy, security and forensic analysis of android vault applications},
author = {Xiaolu Zhang and Ibrahim Baggili and Frank Breitinger},
url = {http://www.sciencedirect.com/science/article/pii/S0167404817301529},
doi = {10.1016/j.cose.2017.07.011},
issn = {0167-4048},
year = {2017},
date = {2017-08-02},
journal = {Computers & Security},
volume = {70},
pages = {516 - 531},
abstract = {Abstract In this work we share the first account for the forensic analysis, security and privacy of Android vault applications. Vaults are designed to be privacy enhancing as they allow users to hide personal data but may also be misused to hide incriminating files. Our work has already helped law enforcement in the state of Connecticut to reconstruct 66 incriminating images and 18 videos in a single criminal case. We present case studies and results from analyzing 18 Android vault applications (accounting for nearly 220 million downloads from the Google Play store) by reverse engineering them and examining the forensic artifacts they produce. Our results showed that Image 1 obfuscated their code and Image 2 applications used native libraries hindering the reverse engineering process of these applications. However, we still recovered data from the applications without root access to the Android device as we were able to ascertain hidden data on the device without rooting for Image 3 of the applications. Image 4 of the vault applications were found to not encrypt photos they stored, and Image 5 were found to not encrypt videos. Image 6 of the applications were found to store passwords in cleartext. We were able to also implement a swap attack on Image 7 applications where we achieved unauthorized access to the data by swapping the files that contained the password with a self-created one. In some cases, our findings illustrate unfavorable security implementations of privacy enhancing applications, but also showcase practical mechanisms for investigators to gain access to data of evidentiary value. In essence, we broke into the vaults.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{MBB17,
title = {Find Me If You Can: Mobile GPS Mapping Applications Forensics Analysis & SNAVP The Open Source, Modular, Extensible Parser},
author = {Jason Moore AND Ibrahim Baggili AND Frank Breitinger},
url = {https://doi.org/10.15394/jdfsl.2017.1414},
doi = {10.15394/jdfsl.2017.1414},
year = {2017},
date = {2017-06-13},
journal = {Journal of Digital Forensics, Security and Law (JDFSL)},
volume = {12},
number = {1},
pages = {7},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{jeong2016towards,
title = {Towards Syntactic Approximate Matching-A Pre-Processing Experiment},
author = {Doowon Jeong AND Frank Breitinger AND Hari Kang AND Sangjin Lee},
url = {https://doi.org/10.15394/jdfsl.2016.1381},
doi = {10.15394/jdfsl.2016.1381},
year = {2016},
date = {2016-12-26},
journal = {The Journal of Digital Forensics, Security and Law: JDFSL},
volume = {11},
number = {2},
pages = {97--110},
publisher = {Association of Digital Forensics, Security and Law},
abstract = {Over the past few years, the popularity of approximate matching algorithms (a.k.a. fuzzy hashing) has increased. Especially within the area of bytewise approximate matching, several algorithms were published, tested, and improved. It has been shown that these algorithms are powerful, however they are sometimes too precise for real world investigations. That is, even very small commonalities (e.g., in the header of a file) can cause a match. While this is a desired property, it may also lead to unwanted results. In this paper, we show that by using simple pre-processing, we significantly can influence the outcome. Although our test set is based on text-based file types (cause of an easy processing), this technique can be used for other, well-documented types as well. Our results show that it can be beneficial to focus on the content of files only (depending on the use-case). While for this experiment we utilized text files, Additionally, we present a small, self-created dataset that can be used in the future for approximate matching algorithms since it is labeled (we know which files are similar and how).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{SCA16,
title = {Exploring Deviant Hacker Networks (DHN) On Social Media Platforms},
author = {Samer Al-khateeb AND Kevin J. Conlan AND Nitin Agarwal And Ibrahim Baggili AND Frank Breitinger},
doi = {10.15394/jdfsl.2016.1375},
year = {2016},
date = {2016-12-26},
journal = {Journal of Digital Forensics, Security and Law},
volume = {11},
number = {2},
pages = {7--20},
abstract = {Online Social Networks (OSNs) have grown exponentially over the past decade. The initial use of social media for benign purposes (e.g., to socialize with friends, browse pictures and photographs, and communicate with family members overseas) has now transitioned to include malicious activities (e.g., cybercrime, cyberterrorism, and cyberwarfare). These nefarious uses of OSNs poses a significant threat to society, and thus requires research attention. In this exploratory work, we study activities of one deviant groups: hacker groups on social media, which we term Deviant Hacker Networks (DHN). We investigated the connection between different DHNs on Twitter: how they are connected, identified the powerful nodes, which nodes sourced information, and which nodes act as "bridges" between different network components. From this, we were able to identify and articulate specific examples of DHNs communicating with each other, with the goal of committing some form of deviant act online. In our work, we also attempted to bridge the gap between the empirical study of OSNs and cyber forensics, as the growth of OSNs is now bringing these two domains together, due to OSNs continuously generating vast amounts of evidentiary data.},
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pubstate = {published},
tppubtype = {article}
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@article{HBB16,
title = {Bytewise Approximate Matching: The Good, The Bad, and The Unknown},
author = {Vikram S. Harichandran AND Frank Breitinger AND Ibrahim Baggili},
doi = {10.15394/jdfsl.2016.1379},
year = {2016},
date = {2016-12-26},
journal = {Journal of Digital Forensics, Security and Law},
volume = {11},
number = {2},
pages = {59--78},
abstract = {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 of approximate matching, the community has constructed numerous algorithms, extensions, and additional applications for this technology, and are still working on novel concepts to improve the status quo. In this survey article, we conduct a high-level review of the existing literature from a non-technical perspective and summarize the existing body of knowledge in approximate matching, with special focus on bytewise algorithms. Our contribution allows researchers and practitioners to receive an overview of the state of the art of approximate matching so that they may understand the capabilities and challenges of the field. Simply, we present the terminology, use cases, classification, requirements, testing methods, algorithms, applications, and a list of primary and secondary literature.},
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@article{MBB16,
title = {Deleting collected digital evidence by exploiting a widely adopted hardware write blocker},
author = {Christopher S. Meffert and Ibrahim Baggili and Frank Breitinger},
url = {http://www.sciencedirect.com/science/article/pii/S1742287616300354},
doi = {10.1016/j.diin.2016.04.004},
issn = {1742-2876},
year = {2016},
date = {2016-08-07},
journal = {Digital Investigation},
volume = {18},
pages = {87--96},
abstract = {In this primary work we call for the importance of integrating security testing into the process of testing digital forensic tools. We postulate that digital forensic tools are increasing in features (such as network imaging), becoming networkable, and are being proposed as forensic cloud services. This raises the need for testing the security of these tools, especially since digital evidence integrity is of paramount importance. At the time of conducting this work, little to no published anti-forensic research had focused on attacks against the forensic tools/process. We used the TD3, a popular, validated, touch screen disk duplicator and hardware write blocker with networking capabilities and designed an attack that corrupted the integrity of the destination drive (drive with the duplicated evidence) without the user's knowledge. By also modifying and repackaging the firmware update, we illustrated that a potential adversary is capable of leveraging a phishing attack scenario in order to fake digital forensic practitioners into updating the device with a malicious operating system. The same attack scenario may also be practiced by a disgruntled insider. The results also raise the question of whether security standards should be drafted and adopted by digital forensic tool makers.},
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pubstate = {published},
tppubtype = {article}
}

@article{HWB16,
title = {CuFA: A more formal definition for digital forensic artifacts},
author = {Vikram S. Harichandran and Daniel Walnycky and Ibrahim Baggili and Frank Breitinger},
url = {http://www.sciencedirect.com/science/article/pii/S1742287616300366},
doi = {10.1016/j.diin.2016.04.005},
issn = {1742-2876},
year = {2016},
date = {2016-08-07},
journal = {Digital Investigation},
volume = {18},
pages = {125--137},
abstract = {The term ``artifact'' currently does not have a formal definition within the domain of cyber/digital forensics, resulting in a lack of standardized reporting, linguistic understanding between professionals, and efficiency. In this paper we propose a new definition based on a survey we conducted, literature usage, prior definitions of the word itself, and similarities with archival science. This definition includes required fields that all artifacts must have and encompasses the notion of curation. Thus, we propose using a new term -- curated forensic artifact (CuFA) -- to address items which have been cleared for entry into a CuFA database (one implementation, the Artifact Genome Project, abbreviated as AGP, is under development and briefly outlined). An ontological model encapsulates these required fields while utilizing a lower-level taxonomic schema. We use the Cyber Observable eXpression (CybOX) project due to its rising popularity and rigorous classifications of forensic objects. Additionally, we suggest some improvements on its integration into our model and identify higher-level location categories to illustrate tracing an object from creation through investigative leads. Finally, a step-wise procedure for researching and logging CuFAs is devised to accompany the model.},
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pubstate = {published},
tppubtype = {article}
}

@article{CBB16,
title = {Anti-forensics: Furthering digital forensic science through a new extended, granular taxonomy},
author = {Kevin Conlan and Ibrahim Baggili and Frank Breitinger},
url = {http://www.sciencedirect.com/science/article/pii/S1742287616300378},
doi = {10.1016/j.diin.2016.04.006},
issn = {1742-2876},
year = {2016},
date = {2016-08-07},
journal = {Digital Investigation},
volume = {18},
pages = {66--75},
abstract = {Anti-forensic tools, techniques and methods are becoming a formidable obstacle for the digital forensic community. Thus, new research initiatives and strategies must be formulated to address this growing problem. In this work we first collect and categorize 308 anti-digital forensic tools to survey the field. We then devise an extended anti-forensic taxonomy to the one proposed by Rogers (2006) in order to create a more comprehensive taxonomy and facilitate linguistic standardization. Our work also takes into consideration anti-forensic activity which utilizes tools that were not originally designed for anti-forensic purposes, but can still be used with malicious intent. This category was labeled as Possible indications of anti-forensic activity, as certain software, scenarios, and digital artifacts could indicate anti-forensic activity on a system. We also publicly share our data sets, which includes categorical data on 308 collected anti-forensic tools, as well as 2780 unique hash values related to the installation files of 191 publicly available anti-forensic tools. As part of our analysis, the collected hash set was ran against the National Institute of Standards and Technology's 2016 National Software Reference Library, and only 423 matches were found out of the 2780 hashes. Our findings indicate a need for future endeavors in creating and maintaining exhaustive anti-forensic hash data sets.},
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}

@article{ZBB16,
title = {Rapid Android Parser for Investigating DEX files (RAPID)},
author = {Xiaolu Zhang and Frank Breitinger and Ibrahim Baggili},
url = {http://www.sciencedirect.com/science/article/pii/S1742287616300305},
doi = {10.1016/j.diin.2016.03.002},
issn = {1742-2876},
year = {2016},
date = {2016-03-25},
journal = {Digital Investigation},
volume = {17},
pages = {28--39},
abstract = {Abstract Android malware is a well-known challenging problem and many researchers/vendors/practitioners have tried to address this issue through application analysis techniques. In order to analyze Android applications, tools decompress APK files and extract relevant data from the Dalvik EXecutable (DEX) files. To acquire the data, investigators either use decompiled intermediate code generated by existing tools, e.g., Baksmali or Dex2jar or write their own parsers/dissemblers. Thus, they either need additional time because of decompiling the application into an intermediate representation and then parsing text files, or they reinvent the wheel by implementing their own parsers. In this article, we present Rapid Android Parser for Investigating DEX files (RAPID) which is an open source and easy-to-use JAVA library for parsing DEX files. RAPID comes with well-documented APIs which allow users to query data directly from the DEX binary files. Our experiments reveal that RAPID outperforms existing approaches in terms of runtime efficiency, provides better reliability (does not crash) and can support dynamic analysis by finding critical offsets. Notably, the processing time for our sample set of 22.35 GB was only 1.5 h with RAPID while the traditional approaches needed about 23 h (parsing and querying).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{HBB15,
title = {A cyber forensics needs analysis survey: Revisiting the domain's needs a decade later},
author = {Vikram S. Harichandran and Frank Breitinger and Ibrahim Baggili and Andrew Marrington},
url = {http://www.sciencedirect.com/science/article/pii/S0167404815001595},
doi = {10.1016/j.cose.2015.10.007},
issn = {0167-4048},
year = {2015},
date = {2015-11-10},
journal = {Computers & Security},
volume = {57},
pages = {1--13},
abstract = {Abstract The number of successful cyber attacks continues to increase, threatening financial and personal security worldwide. Cyber/digital forensics is undergoing a paradigm shift in which evidence is frequently massive in size, demands live acquisition, and may be insufficient to convict a criminal residing in another legal jurisdiction. This paper presents the findings of the first broad needs analysis survey in cyber forensics in nearly a decade, aimed at obtaining an updated consensus of professional attitudes in order to optimize resource allocation and to prioritize problems and possible solutions more efficiently. Results from the 99 respondents gave compelling testimony that the following will be necessary in the future: (1) better education/training/certification (opportunities, standardization, and skill-sets); (2) support for cloud and mobile forensics; (3) backing for and improvement of open-source tools (4) research on encryption, malware, and trail obfuscation; (5) revised laws (specific, up-to-date, and which protect user privacy); (6) better communication, especially between/with law enforcement (including establishing new frameworks to mitigate problematic communication); (7) more personnel and funding.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{KBB15,
title = {WhatsApp network forensics: Decrypting and understanding the WhatsApp call signaling messages},
author = {Filip Karpisek and Ibrahim Baggili and Frank Breitinger},
url = {http://www.sciencedirect.com/science/article/pii/S1742287615000985},
doi = {10.1016/j.diin.2015.09.002},
issn = {1742-2876},
year = {2015},
date = {2015-10-10},
journal = {Digital Investigation},
volume = {15},
pages = {110--118},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{WBM15,
title = {Network and device forensic analysis of Android social-messaging applications},
author = {Daniel Walnycky and Ibrahim Baggili and Andrew Marrington and Jason Moore and Frank Breitinger},
url = {http://www.sciencedirect.com/science/article/pii/S1742287615000547},
doi = {10.1016/j.diin.2015.05.009},
issn = {1742-2876},
year = {2015},
date = {2015-08-09},
journal = {Digital Investigation},
volume = {14, Supplement 1},
pages = {77--84},
abstract = {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.},
note = {The Proceedings of the Fifteenth Annual DFRWS Conference},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{RBBB14,
title = {On application of bloom filters to iris biometrics},
author = {Christian Rathgeb and Frank Breitinger and Christoph Busch and Harald Baier},
doi = {10.1049/iet-bmt.2013.0049},
issn = {2047-4938},
year = {2014},
date = {2014-12-18},
journal = {IET Biometrics},
volume = {3},
number = {4},
pages = {207-218},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{BRB14,
title = {An Efficient Similarity Digests Database Lookup - A Logarithmic Divide & Conquer Approach},
author = {Frank Breitinger and Christian Rathgeb and Harald Baier},
url = {https://doi.org/10.15394/jdfsl.2014.1178},
doi = {doi.org/10.15394/jdfsl.2014.1178},
year = {2014},
date = {2014-09-01},
journal = {Journal of Digital Forensics, Security and Law (JDFSL)},
volume = {9},
number = {2},
pages = {155--166},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{BSB13,
title = {FRASH: A Framework to Test Algorithms of Similarity Hashing},
author = {Frank Breitinger and Georgios Stivaktakis and Harald Baier},
url = {http://dx.doi.org/10.1016/j.diin.2013.06.006},
doi = {10.1016/j.diin.2013.06.006},
issn = {1742-2876},
year = {2014},
date = {2014-08-03},
journal = {Digit. Investig.},
volume = {10},
pages = {S50--S58},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{BSR14,
title = {Evaluating Detection Error Trade-offs for Bytewise Approximate Matching Algorithms},
author = {Frank Breitinger AND Georgios Stivaktakis AND Vassil Roussev},
url = {http://dx.doi.org/10.1016/j.diin.2014.05.002},
doi = {10.1016/j.diin.2014.05.002},
issn = {1742-2876},
year = {2014},
date = {2014-06-08},
journal = {Digital Investigation},
volume = {11},
number = {2},
pages = {81--89},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
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 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.},
note = {bf Best Paper Award},
keywords = {},
pubstate = {published},
tppubtype = {article}
}