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To explore the meaning, methods and techniques associated with the subject of computer forensics and consider the implications of computer forensics for records managers and recordkeeping.

Critically analyses the principles of computer forensics in the context of records characteristics – authenticity, reliability, integrity and usability – and the UK Association of Chief Police Officers (ACPO) principles and procedures for the collection of digital evidence.

The disciplines of records management and computer forensics are potentially mutually compatible. Computer forensics allows for identification of incidents, gathering of evidence, analysis of evidence and potentially recovery of records. Records managers can utilise computer forensics principles to positively enhance records management and have valuable knowledge and expertise to share with their computer forensics colleagues; e.g. metadata expertise, functional requirements for electronic records management, recordkeeping systems design and implementation methodologies, digital preservation and retention management.

Discusses how computer forensics can be used to highlight inadequate recordkeeping and provide a different perspective on records management based on an analysis of principles and concepts rather than empirical data.

Highlights the need for records managers to understand computer forensics and computer forensic scientists to understand recordkeeping to support better records management in the electronic environment; raises the implications for educators, trainers and professional societies.

Very little has been published on the discussion of the potential implications of computer forensics for records managers or how computer forensics can enhance the records management discipline; this paper addresses the gap.

Based on this holistic model, the authors propose and analyze seven key issues related to the admissibility of digital media in cross-border trials considering four Latin American countries.

The authors apply the modeling process of the soft systems methodology by Checkland in order to develop a holistic model focused on human situation problems involving digital media and information technology devices or systems.

The authors discuss the status of the identified key issues in each country and offer a perspective on the integration of cross-border work analyzing the contribution of these key issues to the collaboration between countries criminal cases or the use of foreign digital artifacts in domestic trials.

In this study, the authors assumed that the problems of official interaction between agencies of different countries are considered solved. However, for future studies or research, the authors recommend that these issues can be considered as relevant, since they are related to cross-border cooperation topics that will necessarily require unavoidable official arrangements, agreements and formalities.

This work is aimed at defining and analyzing the key issues that can contribute to the application of current techniques and methodologies in digital forensics as a tool to support the legal framework of each country, considering cross-border trials. Finally, the authors highlight the implications of this study lie in the identification and analysis of the key issues that must be considered for digital forensics as a support tool for the admissibility of digital evidence in cross-border trials.

The authors consider that digital forensic will have high demand in cross-border trials, and it will depend on the people mobility between the countries considered in this study.

This paper shows that the soft systems methodology allows elaborating a holistic model focused on social problems involving digital media and informatics devices.

This article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions.

The corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling.

Several results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security.

Some critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses.

The corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities.

The work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.

The purpose of this paper is twofold: first, to develop, test and validate a set of dimensions that can verify whether any specific online survey tool can be effectively developed and deployed; second, to provide a framework and working topology for web-based survey tool selection.

A panel comprised of five experts determined the validity of the proposed dimensions the authors compiled from extensive feature research of the top online survey software identified by Alexa and Datanyze, which allows for web survey data to be pulled in a customizable fashion over a selected period of time. The validated dimensions were then ranked via a paper survey (n = 98) in a controlled environment using a 9-point Likert scale.

There was no strong correlation between highest-ranked dimensions and the market share and use of a particular online survey tool. However, overall dimension ranking dominance did predict an online survey tool obtaining higher market implementation and use. In addition, the influence of business roles on dimension weights should be considered when selecting survey software. Finally, two additional dimensions not prevalent in existing research – data analysis and technical support – must be considered in survey tool selection.

Online survey tools are increasingly supplementing or replacing random telephone-based opinion and polling surveys for data collection on important social issues, political candidacies, etc. Representative samples yielding the most accurate results are more easily obtained via mixed-mode methods that incorporate online survey tools.

The paper's findings suggest which dimensions must be present for widespread acceptance and implementation of a successful web-based online survey tool. Organizations must be able to assess a particular survey tool's viability for their specific purposes. The dimensions presented here can be developed into an effective adoption heuristic to meet an organization's particular requirements. Findings suggest that when evaluating survey software, one must remain cognizant of the various business roles associated with survey software to better account for decision-maker tendencies. For example, managers place greater emphasis on overall cost whereas developers may value survey creation and integration features.

The existing authentication procedures (pin, pattern, password) are not very secure. Therefore, the Gait pattern authentication scheme is introduced to verify the own user. The current research proposes a running Gaussian grey wolf boosting (RGGWB) model to recognize the owner.

The biometrics system plays an important role in smartphones in securing confidential data stored in them. Moreover, the authentication schemes such as passwords and patterns are widely used in smartphones.

To validate this research model, the unauthenticated user's Gait was trained and tested simultaneously with owner gaits. Furthermore, if the gait matches, the smartphone unlocks automatically; otherwise, it rejects it.

Finally, the effectiveness of the proposed model is proved by attaining better accuracy and less error rate.

In-depth knowledge about specific national approaches to using digital evidence in investigations is scarce. A clearer insight into the organisational barriers and professional challenges experienced, alongside a more detailed picture of how digital evidence can help police investigations are required to empirically substantiate claims about how digital technologies are changing the face of criminal investigations. The paper aims to focus on the introduction of digital media investigators to support investigating officers with the collection and interpretation of digital evidence.

Drawing on ethnographic and interview data collected as part of an Economic and Social Research Council-funded project on the application of digital forensics expertise in policing in England and Wales, this paper examines the changing face of investigations in relation to escalating digital demand.

The analysis presents the national and regional organisational parameters of deploying digital expertise in criminal investigation and examines some of the challenges of being a digital media investigator (DMI). Through testimonies from DMIs, digital forensic practitioners, investigating and senior officers and forensic managers, the analysis explores the organisational tensions in the collection, processing, interpretation and use of information from digital devices for evidential purposes.

The paper offers an empirical basis for the comparative study of how the DMI role has been implemented by law enforcement agencies and its fit within broader institutional considerations and processes.

The development of the DMI role has raised questions about the supply of digital expertise, especially to volume crime investigations, and tensions around occupational divisions between scientific and operational units.

The findings show that while the introduction of the DMI role was much needed, the development of this valuable provision within each force and the resources available require sustained and coordinated support to protect these professionals and retain their skills.

This study contributes to the growing sociological and criminological literature with an ethnographically based perspective into the organisational and occupational tensions in the identification and processing of digital evidence in England and Wales.

Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis.

Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers.

Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails.

It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.

Most source recording device identification models for Web media forensics are based on a single feature to complete the identification task and often have the disadvantages of long time and poor accuracy. The purpose of this paper is to propose a new method for end-to-end network source identification of multi-feature fusion devices.

This paper proposes an efficient multi-feature fusion source recording device identification method based on end-to-end and attention mechanism, so as to achieve efficient and convenient identification of recording devices of Web media forensics.

The authors conducted sufficient experiments to prove the effectiveness of the models that they have proposed. The experiments show that the end-to-end system is improved by 7.1% compared to the baseline i-vector system, compared to the authors’ previous system, the accuracy is improved by 0.4%, and the training time is reduced by 50%.

With the development of Web media forensics and internet technology, the use of Web media as evidence is increasing. Among them, it is particularly important to study the authenticity and accuracy of Web media audio.

This paper aims to promote the development of source recording device identification and provide effective technology for Web media forensics and judicial record evidence that need to apply device source identification technology.

Introduction: Big data is that disruptive force that affects businesses, industries, and the economy. In 2021, insurance analytics will include more than simply analysing statistics. According to current trends, new insurance big data analytics (BDA) methods will enable firms to do more with their data. The insurance business has traditionally been conservative, but adopting new technology is no longer only a current trend; it must be competitive. Big data technologies aid in processing a huge amount of data, improve workflow efficiency, and lower operating costs.

Purpose: Some of the most recent developments in big data for insurance and how insurers may use the information to stay ahead of their competitors are discussed in this chapter. This chapter’s prime purpose is to analyse how artificial intelligence (AI), blockchain, and mobile technology change the outlook and working of the insurance sector.

Methodology: To achieve our research purpose, we analyse case studies and literature that emphasise how BDA revolutionises the insurance market. For this purpose, various articles and studies on BDA in the insurance market will be selected and studied.

Findings: From the analysis, we find that the use of big data in the insurance business is growing. The development of BDA has proven to be a game-changing technology in insurance, with a slew of benefits. The insurance sector is now grappling with the risks and opportunities that modern technology presents. Big data offers opportunities that every company must avail of. We can safely argue that big data has transformed the insurance sector for the better. The BDA’s consequences have enabled insurers to target clients more accurately. This chapter highlights that new tools and technologies of big data in the insurance market are increasing. AI is emerging as a powerful technology that can alter the entire insurance value stream. The transmission of any type of digital proof for underwriting, including the use of digital health data, might be a blockchain use case (electronic health record (EHR)). As digital forensics becomes easier to include in underwriting, it must expect price and product design changes in the future. In the future, the internet of things (IoT) and AI will combine to automate insurance processes, causing our sector to transform dramatically. We highlight that these technologies transformed insurance practices and revolutionalised the insurance market.

Various organizational landscapes have evolved to improve their business processes, increase production speed and reduce the cost of distribution and have integrated their Internet with small and medium scale enterprises (SMEs) and third-party vendors to improve business growth and increase global market share, including changing organizational requirements and business process collaborations. Benefits include a reduction in the cost of production, online services, online payments, product distribution channels and delivery in a supply chain environment. However, the integration has led to an exponential increase in cybercrimes, with adversaries using various attack methods to penetrate and exploit the organizational network. Thus, identifying the attack vectors in the event of cyberattacks is very important in mitigating cybercrimes effectively and has become inevitable. However, the invincibility nature of cybercrimes makes it challenging to detect and predict the threat probabilities and the cascading impact in an evolving organization landscape leading to malware, ransomware, data theft and denial of service attacks, among others. The paper explores the cybercrime threat landscape, considers the impact of the attacks and identifies mitigating circumstances to improve security controls in an evolving organizational landscape.

The approach follows two main cybercrime framework design principles that focus on existing attack detection phases and proposes a cybercrime mitigation framework (CCMF) that uses detect, assess, analyze, evaluate and respond phases and subphases to reduce the attack surface. The methods and implementation processes were derived by identifying an organizational goal, attack vectors, threat landscape, identification of attacks and models and validation of framework standards to improve security. The novelty contribution of this paper is threefold: first, the authors explore the existing threat landscapes, various cybercrimes, models and the methods that adversaries are deploying on organizations. Second, the authors propose a threat model required for mitigating the risk factors. Finally, the authors recommend control mechanisms in line with security standards to improve security.

The results show that cybercrimes can be mitigated using a CCMF to detect, assess, analyze, evaluate and respond to cybercrimes to improve security in an evolving organizational threat landscape.

The paper does not consider the organizational size between large organizations and SMEs. The challenges facing the evolving organizational threat landscape include vulnerabilities brought about by the integrations of various network nodes. Factor influencing these vulnerabilities includes inadequate threat intelligence gathering, a lack of third-party auditing and inadequate control mechanisms leading to various manipulations, exploitations, exfiltration and obfuscations.

Attack methods are applied to a case study for the implementation to evaluate the model based on the design principles. Inadequate cyber threat intelligence (CTI) gathering, inadequate attack modeling and security misconfigurations are some of the key factors leading to practical implications in mitigating cybercrimes.

There are no social implications; however, cybercrimes have severe consequences for organizations and third-party vendors that integrate their network systems, leading to legal and reputational damage.

The paper’s originality considers mitigating cybercrimes in an evolving organization landscape that requires strategic, tactical and operational management imperative using the proposed framework phases, including detect, assess, analyze, evaluate and respond phases and subphases to reduce the attack surface, which is currently inadequate.