936 research outputs found

    A systematic survey of online data mining technology intended for law enforcement

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    As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies

    Forensics Writer Identification using Text Mining and Machine Learning

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    Constant technological growth has resulted in the danger and seriousness of cyber-attacks, which has recently unmistakably developed in various institutions that have complex Information Technology (IT) infrastructure. For instance, for the last three (3) years, the most horrendous instances of cybercrimes were perceived globally with enormous information breaks, fake news spreading, cyberbullying, crypto-jacking, and cloud computing services. To this end, various agencies improvised techniques to curb this vice and bring perpetrators, both real and perceived, to book in relation to such serious cybersecurity issues. Consequently, Forensic Writer Identification was introduced as one of the most effective remedies to the concerned issue through a stylometry application. Indeed, the Forensic Writer Identification is a complex forensic science technology that utilizes Artificial Intelligence (AI) technology to safeguard, recognize proof, extraction, and documentation of the computer or digital explicit proof that can be utilized by the official courtroom, especially, the investigative officers in case of a criminal issue or just for data analytics. This research\u27s fundamental objective was to scrutinize Forensic Writer Identification technology aspects in twitter authorship analytics of various users globally and apply it to reduce the time to find criminals by providing the Police with the most accurate methodology. As well as compare the accuracy of different techniques. The report shall analytically follow a logical literature review that observes the vital text analysis techniques. Additionally, the research applied agile text mining methodology to extract and analyze various Twitter users\u27 texts. In essence, digital exploration for appropriate academics and scholarly artifacts was affected in various online and offline databases to expedite this research. Forensic Writer Identification for text extraction, analytics have recently appreciated reestablished attention, with extremely encouraging outcomes. In fact, this research presents an overall foundation and reason for text and author identification techniques. Scope of current techniques and applications are given, additionally tending to the issue of execution assessment. Results on various strategies are summed up, and a more inside and out illustration of two consolidated methodologies are introduced. By encompassing textural, algorithms, and allographic, emerging technologies are beginning to show valuable execution levels. Nevertheless, user acknowledgment would play a vital role with regards to the future of technology. To this end, the goal of coming up with a project proposal was to come up with an analytical system that would automate the process of authorship identification methodology in various Web 2.0 Technologies aspects globally, hence addressing the contemporary cybercrime issues

    Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

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    Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

    A multi-disciplinary framework for cyber attribution

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    Effective Cyber security is critical to the prosperity of any nation in the modern world. We have become dependant upon this interconnected network of systems for a number of critical functions within society. As our reliance upon this technology has increased, as has the prospective gains for malicious actors who would abuse these systems for their own personal benefit, at the cost of legitimate users. The result has been an explosion of cyber attacks, or cyber enabled crimes. The threat from hackers, organised criminals and even nations states is ever increasing. One of the critical enablers to our cyber security is that of cyber attribution, the ability to tell who is acting against our systems. A purely technical approach to cyber attribution has been found to be ineffective in the majority of cases, taking too narrow approach to the attribution problem. A purely technical approach will provide Indicators Of Compromise (IOC) which is suitable for the immediate recovery and clean up of a cyber event. It fails however to ask the deeper questions of the origin of the attack. This can be derived from a wider set of analysis and additional sources of data. Unfortunately due to the wide range of data types and highly specialist skills required to perform the deep level analysis there is currently no common framework for analysts to work together towards resolving the attribution problem. This is further exasperated by a communication barrier between the highly specialised fields and no obviously compatible data types. The aim of the project is to develop a common framework upon which experts from a number of disciplines can add to the overall attribution picture. These experts will add their input in the form of a library. Firstly a process was developed to enable the creation of compatible libraries in different specialist fields. A series of libraries can be used by an analyst to create an overarching attribution picture. The framework will highlight any intelligence gaps and additionally an analyst can use the list of libraries to suggest a tool or method to fill that intelligence gap. By the end of the project a working framework had been developed with a number of libraries from a wide range of technical attribution disciplines. These libraries were used to feed in real time intelligence to both technical and nontechnical analysts who were then able to use this information to perform in depth attribution analysis. The pictorial format of the framework was found to assist in the breaking down of the communication barrier between disciplines and was suitable as an intelligence product in its own right, providing a useful visual aid to briefings. The simplicity of the library based system meant that the process was easy to learn with only a short introduction to the framework required

    Software Piracy Forensics: Impact and Implications of Post‐Piracy Modifications

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    Piracy is potentially possible at any stage of the lifetime of the software. In a post-piracy situation, however, the growth of the respective versions of the software (both the original and pirated) is expected to be in different directions as a result of expectedly different implementation strategies. This paper shows how such post-piracy modifications are of special interest to a cyber crime expert investigating software piracy and suggests that the present software piracy forensic (or software copyright infringement investigation) approaches require amendments to take in such modifications. For this purpose, the paper also presents a format that is jargon-free, so as to present the findings in a more intelligible form to the judicial authorities. Keywords: Piracy, post-piracy modifications, software piracy, source code, copyright, software copyright infringement, software piracy forensics, database forensics, MIS forensics, AFC, SCAP, technical expert, substantial similarity test, CDA

    A Survey of Social Network Forensics

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    Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

    Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

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    The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process
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