scholarly journals On Complex Crimes and Digital Forensics

2014 ◽  
pp. 230-244 ◽  
Author(s):  
Martin S. Olivier
Keyword(s):  
Forensic Science ◽  
Digital Forensics ◽  
Scientific Basis ◽  
Error Rates ◽  
Decision Problems ◽  
Complexity Classes ◽  
Demarcation Criterion ◽  
Digital Forensic ◽  
Truth Claims ◽  
Investigative Technique

Science provides the basis for truth claims in forensics. Very little research has been done to explore the scientific basis of digital forensics. The work that has been done vary widely in what they propose; in most cases it is unclear how the philosophical remarks about such forensic science apply to digital forensics practice, or that the practical suggestions are a sufficient basis to claim that practice based on them is scientific. This chapter provides an initial exploration of the potential of decision problems from the field of algorithmics to form this scientific basis. There is no doubt that decision problems operate in the scientific domain and decision problems look similar to hypotheses to be of immediate practical use. The chapter suggests that, if decision problems are used in this manner, it is clear that current digital forensics have only scratched the surface of what is possible. Probabilistic complexity classes, for example, offer interesting possibilities for performing complex tests in relatively short times, with known error rates. Using decision problems as a demarcation criterion makes it possible to distinguish between digital forensic science (or simply digital forensics) and digital forensic craft, which should be called digital investigative technique or some other suitable term that does not imply that its use leads to scientific truths.

Disconnects of Specialized Mobile Digital Forensics within the Generalized Field of Digital Forensic Science

2020 ◽  
pp. 325-328
Author(s):  
Gregory H. Carlton ◽  
Gary C. Kessler
Keyword(s):  
Best Practices ◽  
Forensic Science ◽  
Digital Forensics ◽  
Legal Framework ◽  
Digital Devices ◽  
Digital Forensic

The study and practice of forensic science comprises many distinct areas that range from behavioral to biological to physical and to digital matters, and in each area forensic science is utilized to obtain evidence that will be admissible within the legal framework. This article focuses on inconsistencies within the accepted methodology of digital forensics when comparing the current best practices of mobile digital devices and traditional computer devices. Here the authors raise the awareness of this disconnect in methodology, and they posit that some specific tasks within the traditional best practices of digital forensic science are artifacts of ritual rather than based on scientific requirements.

Disconnects of Specialized Mobile Digital Forensics within the Generalized Field of Digital Forensic Science

2020 ◽  
pp. 593-596
Author(s):  
Gregory H. Carlton ◽  
Gary C. Kessler
Keyword(s):  
Best Practices ◽  
Forensic Science ◽  
Digital Forensics ◽  
Legal Framework ◽  
Digital Devices ◽  
Digital Forensic

The study and practice of forensic science comprises many distinct areas that range from behavioral to biological to physical and to digital matters, and in each area forensic science is utilized to obtain evidence that will be admissible within the legal framework. This article focuses on inconsistencies within the accepted methodology of digital forensics when comparing the current best practices of mobile digital devices and traditional computer devices. Here the authors raise the awareness of this disconnect in methodology, and they posit that some specific tasks within the traditional best practices of digital forensic science are artifacts of ritual rather than based on scientific requirements.

scholarly journals Review: Digital Forensics

2021 ◽  
pp. 171-175
Author(s):  
Anand Desai ◽  
Siddhesh Masurkar
Keyword(s):  
Forensic Science ◽  
Digital Forensics ◽  
The Internet ◽  
Digital Devices ◽  
Digital Forensic ◽  
Internet Users ◽  
Job Opportunities ◽  
Set Up ◽  
Investigation Process ◽  
Internet Network

With the advancement and growing science of technology and the internet, the threats to data and digital devices have been increasing due to hackers and data invigilators. So the branch of DIGITAL FORENSIC has been set up for the investigation of the cybercrimes committed through the means of the internet, network, digital devices, etc. There are millions of internet users worldwide who are targeted by these hackers, and they lose their data to these data thieves unknowingly. This data can be misused by cybercriminals for various purposes. This branch of forensic science tracks and investigates these cyber criminals and finds the appropriate evidence against them. This paper surveys the work of this branch gives you a brief explanation about the various sub-branches, job opportunities available, and several tools used in this investigation process.

scholarly journals Identifying Digital Forensic Frameworks Based on Processes Models

2021 ◽  
pp. 249-258
Author(s):  
Talib M. Jawad Abbas ◽  
Ahmed Salem Abdulmajeed
Keyword(s):  
Forensic Science ◽  
Digital Forensics ◽  
Digital Evidence ◽  
Digital Devices ◽  
Digital Forensic ◽  
Reliable Evidence ◽  
Basic Concepts ◽  
Investigation Process

Digital forensic is part of forensic science that implicitly covers crime related to computer and other digital devices. It‟s being for a while that academic studies are interested in digital forensics. The researchers aim to find out a discipline based on scientific structures that defines a model reflecting their observations. This paper suggests a model to improve the whole investigation process and obtaining an accurate and complete evidence and adopts securing the digital evidence by cryptography algorithms presenting a reliable evidence in a court of law. This paper presents the main and basic concepts of the frameworks and models used in digital forensics investigation.

Disconnects of Specialized Mobile Digital Forensics within the Generalized Field of Digital Forensic Science

2018 ◽  
Vol 10 (3) ◽  
pp. 62-65
Author(s):  
Gregory H. Carlton ◽  
Gary C. Kessler
Keyword(s):  
Best Practices ◽  
Forensic Science ◽  
Digital Forensics ◽  
Legal Framework ◽  
Digital Devices ◽  
Digital Forensic

The study and practice of forensic science comprises many distinct areas that range from behavioral to biological to physical and to digital matters, and in each area forensic science is utilized to obtain evidence that will be admissible within the legal framework. This article focuses on inconsistencies within the accepted methodology of digital forensics when comparing the current best practices of mobile digital devices and traditional computer devices. Here the authors raise the awareness of this disconnect in methodology, and they posit that some specific tasks within the traditional best practices of digital forensic science are artifacts of ritual rather than based on scientific requirements.

Forensic science evidence: Naive estimates of false positive error rates and reliability

2020 ◽  
Author(s):  
Kristy Martire ◽  
Agnes Bali ◽  
Kaye Ballantyne ◽  
Gary Edmond ◽  
Richard Kemp ◽  
...  
Keyword(s):  
Forensic Science ◽  
False Positive ◽  
Error Rates ◽  
Forensic Sciences ◽  
Positive Error ◽  
False Positive Error ◽  
Reliability Estimates ◽  
Positive Report ◽  
Science Evidence ◽  
Science Disciplines

We do not know how often false positive reports are made in a range of forensic science disciplines. In the absence of this information it is important to understand the naive beliefs held by potential jurors about forensic science evidence reliability. It is these beliefs that will shape evaluations at trial. This descriptive study adds to our knowledge about naive beliefs by: 1) measuring jury-eligible (lay) perceptions of reliability for the largest range of forensic science disciplines to date, over three waves of data collection between 2011 and 2016 (n = 674); 2) calibrating reliability ratings with false positive report estimates; and 3) comparing lay reliability estimates with those of an opportunity sample of forensic practitioners (n = 53). Overall the data suggest that both jury-eligible participants and practitioners consider forensic evidence highly reliable. When compared to best or plausible estimates of reliability and error in the forensic sciences these views appear to overestimate reliability and underestimate the frequency of false positive errors. This result highlights the importance of collecting and disseminating empirically derived estimates of false positive error rates to ensure that practitioners and potential jurors have a realistic impression of the value of forensic science evidence.

Logical Decision Problems and Complexity of Logic Programs

1987 ◽  
Vol 10 (1) ◽  
pp. 1-33
Author(s):  
Egon Börger ◽  
Ulrich Löwen
Keyword(s):  
Fixed Point ◽  
Computational Complexity ◽  
Decision Problem ◽  
Decision Problems ◽  
Complexity Classes ◽  
Logic Programs ◽  
Finite Structures ◽  
Syntactic Restrictions

We survey and give new results on logical characterizations of complexity classes in terms of the computational complexity of decision problems of various classes of logical formulas. There are two main approaches to obtain such results: The first approach yields logical descriptions of complexity classes by semantic restrictions (to e.g. finite structures) together with syntactic enrichment of logic by new expressive means (like e.g. fixed point operators). The second approach characterizes complexity classes by (the decision problem of) classes of formulas determined by purely syntactic restrictions on the formation of formulas.

scholarly journals A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 87
Author(s):  
Sara Ferreira ◽  
Mário Antunes ◽  
Manuel E. Correia
Keyword(s):  
Machine Learning ◽  
Digital Forensics ◽  
State Of The Art ◽  
Forensic Analysis ◽  
Third Party ◽  
Support Vector ◽  
Multimedia Content ◽  
Digital Forensic ◽  
Video Frames ◽  
Forensic Tools

Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

scholarly journals Improving Forensic Triage Efficiency through Cyber Threat Intelligence

2019 ◽  
Vol 11 (7) ◽  
pp. 162 ◽  
Author(s):  
Nikolaos Serketzis ◽  
Vasilios Katos ◽  
Christos Ilioudis ◽  
Dimitrios Baltatzis ◽  
Georgios Pangalos
Keyword(s):  
Information Security ◽  
Digital Forensics ◽  
Incident Response ◽  
Security Controls ◽  
Digital Forensic ◽  
Threat Intelligence ◽  
Cyber Threat ◽  
Series Of Experiments ◽  
Cyber Threat Intelligence ◽  
Security Incidents

The complication of information technology and the proliferation of heterogeneous security devices that produce increased volumes of data coupled with the ever-changing threat landscape challenges have an adverse impact on the efficiency of information security controls and digital forensics, as well as incident response approaches. Cyber Threat Intelligence (CTI)and forensic preparedness are the two parts of the so-called managed security services that defendants can employ to repel, mitigate or investigate security incidents. Despite their success, there is no known effort that has combined these two approaches to enhance Digital Forensic Readiness (DFR) and thus decrease the time and cost of incident response and investigation. This paper builds upon and extends a DFR model that utilises actionable CTI to improve the maturity levels of DFR. The effectiveness and applicability of this model are evaluated through a series of experiments that employ malware-related network data simulating real-world attack scenarios. To this extent, the model manages to identify the root causes of information security incidents with high accuracy (90.73%), precision (96.17%) and recall (93.61%), while managing to decrease significantly the volume of data digital forensic investigators need to examine. The contribution of this paper is twofold. First, it indicates that CTI can be employed by digital forensics processes. Second, it demonstrates and evaluates an efficient mechanism that enhances operational DFR.

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