The use of Artificial Intelligence in digital forensics: An introduction

Mitchell, Faye

doi:10.14296/deeslr.v7i0.1922

Cited by 20 publications

(14 citation statements)

References 2 publications

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“…The process of providing users with information or services related to their work is called a "situational awareness system". By using a mobile device to collect data, and then classifying the data collected into categories such as the user's status, the physical environment status, the computing system status, and other statuses to extract the desired data [2], [3] in figure 6. Existing studies include those that study information collected by acceleration sensors [4]; those utilize the accumulated data in smart phones' location-based services to analyze the relationship between time and users' movement for marketing studies [5]; those that analyze the context of the situation by analyzing the users' movements using smart devices [6]; and those that study zone deviations for personal safety that includes research [7] on methods of recognition for of crisis situations when users have deviated from the expected movement path [8].…”

Section: Methodsmentioning

confidence: 99%

Pattern analysis of risk situations using multi-sensor

Noh¹,

Cha²

2018

IJET

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Background/Objectives: Existing crime prevention systems manually monitor risk situations using street lights, CCTVs, and security equipment. Since there are many areas where the workforce is held responsible, it is difficult to closely manage all systems due to work overload.Methods/Statistical analysis: The data of maps constructed and continuously updated through the system will allow more accurate predictions of crime and contribute to crime prevention strategies. Furthermore, replacing existing patrol manpower to unmanned drones will allow for more efficient human resources management as well as contribute to the crime prevention infrastructure, thereby minimizing the existence of blind spots in the current system.Findings: It is not easy to diffuse the initial situation in the case of an emergency through prompt notifications. Therefore, a low-cost, integrated management system is needed to prevent major accidents and to minimize damage by detecting crime and fire risks in the early stage. It will be easier to judge risks if we use the multi-sensor and pattern analysis algorithms proposed in this study. Occurrences of crime and fire have been rapidly rising with the quick pace of industrialization. This has resulted in an increase of unease among citizens as well as a rising demand for security and safety in residential environments. As the times change, it is necessary to develop advanced science technology that can predict crimes in order to construct crime preventing environments. The Risk Notification Service can promptly respond to the current status and situation of the user by forwarding the status to the administrator or guardian. Police activity can be strengthened by building a high-tech science and security system to monitor areas susceptible to crime in real-time.Improvements/Applications: This study looks into problems of the existing monitoring system and proposes an integrated control system for crime prevention. Keywords:Background/Objectives: Existing crime prevention systems manually monitor risk situations using street lights, CCTVs, and security equipment. Since there are many areas where the workforce is held responsible, it is difficult to closely manage all systems due to work overload.Methods/Statistical analysis: The data of maps constructed and continuously updated through the system will allow more accurate predictions of crime and contribute to crime prevention strategies. Furthermore, replacing existing patrol manpower to unmanned drones will allow for more efficient human resources management as well as contribute to the crime prevention infrastructure, thereby minimizing the existence of blind spots in the current system.Findings: It is not easy to diffuse the initial situation in the case of an emergency through prompt notifications. Therefore, a low-cost, integrated management system is needed to prevent major accidents and to minimize damage by detecting crime and fire risks in the early stage. It will be easier to judge risks if we use the multi-sensor and pattern analysis algorithms proposed in this study. Occurrences of crime and fire have been rapidly rising with the quick pace of industrialization. This has resulted in an increase of unease among citizens as well as a rising demand for security and safety in residential environments. As the times change, it is necessary to develop advanced science technology that can predict crimes in order to construct crime preventing environments. The Risk Notification Service can promptly respond to the current status and situation of the user by forwarding the status to the administrator or guardian. Police activity can be strengthened by building a high-tech science and security system to monitor areas susceptible to crime in real-time.Improvements/Applications: This study looks into problems of the existing monitoring system and proposes an integrated control system for crime prevention.

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Section: Methodsmentioning

confidence: 99%

Pattern analysis of risk situations using multi-sensor

Noh¹,

Cha²

2018

IJET

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“…La Inteligencia Artificial ha sido usada en otros campos en los que se puede extrapolar un estudio forense, como lo son: el reconocimiento de patrones en línea [29] [30], el descubrimiento de conocimiento [31], la computación forense sobre Internet de las Cosas (IoT) [32], la correlación de eventos almacenados en los elementos materiales probatorios [33], entre otras aplicaciones.…”

Section: Otras Aplicacionesunclassified

“…Un tercer campo de la AI que se puede aprovechar en el ámbito forense es la minería de datos y descubrimiento de conocimiento en bases de datos. Aunque ambos conceptos son diferentes, se usan para referirse al mismo procedimiento de recolección de grandes cantidades de datos [31].…”

Section: Otras Aplicacionesunclassified

Inteligencia Artificial aplicada al Análisis Forense Digital: Una revisión preliminar

Cano¹,

Miranda²,

Pinzón³

2020

Seguridad Informática. X Congreso Iberoamericano, CIBSI 2020

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Resumen-The digital forensic analysis seeks the application of scientific and statistical techniques to identify, collect, preserve and present the relevant digital evidence that allows the hypothesis to be affirmed or rejected against a possible criminal act. The current methods of digital forensic analysis, although effective for the visual analysis of the material evidence, do not allow to execute in an automated way and for large volumes of data, correlation studies on the obtained files, validation of metadata and identification of anomalies in files of text, graphics or audiovisual. It is for this reason that artificial intelligence techniques have been proposed for data processing, identifying patterns and trends that allow noticing aspects that are not visually perceptible. This paper discusses the role that artificial intelligence can play in digital forensic analysis, proposing a review of the literature, in order to illustrate the areas of computer forensics in which artificial intelligence techniques have been used to date. This, to identify a new work niche in this area, hoping that the ideas in this document can represent promising directions for the development of more efficient and effective computer forensic tools.

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“…A single workstation has limited computational and storage capabilities, and it is not easily scalable. Attempts to mitigate against the aforementioned are still at research stage, and examples include digital forensics as service (DFaaS) [18][19][20], data reduction techniques [21][22][23], triage [24][25][26][27][28][29], artificial intelligence [30][31][32][33] and data mining [34][35][36]22]. However, they all suffer various limitations as some are designed to address specific problems, e.g., data reduction and triage.…”

Section: Volumementioning

confidence: 99%

On Big Data Forensic - A Case For Forensic Cloud Environment

Tabona¹,

Maupong²,

Ramokapane³

et al. 2020

Preprint

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Background The high rise in electronic devices in modern-day society has resulted in crimes in cyber-related crimes as criminals resort to hacking, illegal use of these devices. This is primarily due to perceived high rewards and low chances of being apprehended. The rise in cyber crimes poses a significant challenge to forensic investigators as now they have to process huge volumes of data from a variety of sources within a limited time. This results in investigators taking longer to process cases and in some instances missing links as they deal with data from a variety of sources. Findings In this paper, we provide a definition of big data forensics, and then we discuss the challenges associated with digital forensics investigations when dealing with big data. We provide details on how volume, variety, and velocity all pose a huge challenge in digital forensics investigations. We then discuss how a novel solution called Forensic Cloud Environment (FCE) leverages the power of Hadoop, HBase, and MapReduce to provide a solution for big data forensic challenges. Conclusion In conclusion, the fact that FCE provides an environment to store huge volumes of data from a variety of sources allows for an improved processing time of data. Hence, providing an environment for big data forensics for the future.

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The use of Artificial Intelligence in digital forensics: An introduction

Cited by 20 publications

References 2 publications

Pattern analysis of risk situations using multi-sensor

Pattern analysis of risk situations using multi-sensor

Inteligencia Artificial aplicada al Análisis Forense Digital: Una revisión preliminar

On Big Data Forensic - A Case For Forensic Cloud Environment

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