Technique for Investigating Attacks on a Company's Reputation on a Social Media Platform

In this paper, we present the technique for investigating attacks on a company's reputation on a social media platform as a part of an arsenal of digital forensics investigators. The technique consists of several methods, including (1) identifying the attack based on sentiment analysis, (2) identifying the actors of the attack, (3) determining the attack's impact, and (4) determining core actors to identify the strategy of the attacker, including (4a) usage of bots, (4b) attempts to conflict initiation, (4c) competitor promotion, (4d) uncoordinated user attack. In the paper we also present the evaluation of this technique using the real investigation of use-case, where we investigate the attack on a retail company X, that occurs after the company changed its policy dedicated to COVID-19 QR codes for their visitors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Learning Strategies for Sensitive Content Detection

Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.

Microbiology and Bioterrorism

It's been 2 decades since the posting of the anthrax letters in the United States in 2001. This event marked a pivotal point in our history. It highlighted the vulnerability of modern society to acts of bioterrorism and set countries on a course to develop capabilities to pre-empt, prevent, react to, investigate, and recover from acts of bioterrorism. The current COVID-19 pandemic is a stark reminder of the enormity of the impact that the release of a biological agent, natural or otherwise, can have on an immunological naïve society. The purpose of this article is to describe how microbiology is applied in the investigation of bioterrorism, highlighting the modern advances in technology, particularly the DNA technologies, which have assisted this discipline as a forensic practice. © 2023 Elsevier Ltd. All rights reserved.

Biorecuperation, the epidemic of violence and COVID-19 in Mexico

COVID-19 has reinstated the sovereign enclosures of corpse management that mothers of the disappeared had so successfully challenged in the past decade. To explore how moral duties toward the dead are being renegotiated due to COVID-19, this article puts forward the notion of biorecuperation, understood as an individualised form of forensic care for the dead made possible by the recovery of biological material. Public health imperatives that forbid direct contact with corpses due to the pandemic, interrupt the logics of biorecuperation. Our analysis is based on ten years of experience working with families of the disappeared in Mexico, ethnographic research within Mexico's forensic science system and online interviews conducted with medics and forensic scientists working at the forefront of Mexico City's pandemic. In the face of increasing risks of viral contagion and death, this article analyses old and new techniques designed to bypass the prohibitions imposed by the state and its monopoly over corpse management and identification.

Forensic treatment and human identification in the age of COVID-19: Difficulties and challenges for the forensic medical system in Mexico

As a result of the SARS-CoV-2 (COVID-19) pandemic, in 2020 forensic institutions in Mexico began using extreme measures in the treatment of bodies of confirmed or suspected cases, due to possible infection. A series of national protocols on how to deal with the virus were announced, yet forensic personnel have struggled to apply these, demonstrating the country's forensics crisis. This article aims to reflect on two points: (1) the impact that COVID-19 protocols have had on how bodies confirmed as or suspected of being infected with the virus are handled in the forensic medical system;and (2) the particular treatment in cases where the body of the victim is unidentified, and the different effects the pandemic has had in terms of the relationship between the institutional environment and the family members of those who have died as a result of infection, or suspected infection, from COVID-19.

Natural deaths in extraordinary times: Governing COVID dead in southern Arizona

Research into the governance of dead bodies, primarily focused on post-conflict contexts, has often focused on the aspects of the management of dead bodies that involve routinisation, bureaucratisation and order. Less attention has been paid to the governance of the dead in times of relative peace and, in particular, to the aspects of such work that are less bureaucratised and controlled. This article explores the governance of dead bodies in pandemic times – times which although extraordinary, put stress on ordinary systems in ways that are revealing of power and politics. Observations for this article come from over fifteen years of ethnographic research at a medical examiner's office in Arizona, along with ten focused interviews in 2020 with medico-legal authorities and funeral directors specifically about the COVID-19 pandemic. The author argues that the pandemic revealed the ways in which the deathcare industry in the United States is an unregulated, decentralised and ambiguous space.

Mobile Commerce - Analysis and Investigation of the Online Safety, Privacy, and Data Forensics of Amazon and Etsy Apps

The COVID19 pandemic has led to the proliferation of the use of online shopping applications among millions of customers worldwide. The enormous potential in technological advancements, particularly mobile technology, has directly impacted mobile commerce, where the shopping process has become so convenient. While the benefits of mobile commerce are multi-fold, the current privacy practices and the extent of user data residue in shopping apps have been less explored. In this paper, we conducted an in-depth, systematic analysis of two of the most popular mobile shopping apps - Amazon and Etsy. Our analysis led to the recovery of user data and shopping activity artifacts from Amazon and Etsy buyer and seller apps on Android/iOS devices. Based on the user data and artifacts found, we have also discussed the implications of default privacy settings, the importance of online safety policies prior to product listings, and implications for research and practice. © 2023 IEEE Computer Society. All rights reserved.

A robust tracking system for COVID-19 like pandemic using advanced hybrid technologies

In order to track patients in coronavirus (COVID-19) like pandemic, this paper proposes a novel model based on hybrid advance technologies, which is capable to trace and track COVID-19 affectees with high accuracy. The hybrid technologies include, cellular, cyber and low range wireless technologies. This technique is capable to trace patients through call data record using cellular technology, voice over Internet protocol calls using cyber technology and physical contact without having a call history using low range wireless technologies. The proposed model is also capable to trace COVID-19 suspects. In addition to tracking, the proposed model is capable to provide surveillance capability as well by geo tagging the patients. In case of any violation by the patients an alert is sent to the concerned department. The proposed model is cost effective and privacy preserved as the entire process is carried out under the umbrella of a concerned government department. The potential outcomes of the proposed model are tracking of COVID-19 patients, monitoring of isolated patients, tracking of suspected ones and inform the mass about the safest path to use. [ABSTRACT FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sustainability Characterization of Normal Voice Features with Voice Obtained After Wearing Different Masks Using Praat Software: A Comparative Study

The basic frequency, formants, pitch, and strength of an individual's voice are compared to a disguised voice using various masks in this study. The greatest obstacle arises when scenarios involving disguised speech samples are encountered for assessment and recognition. Such audio samples of anonymous callers are widespread in crimes such as abduction, blackmail, fake extortion, and many others, where the speaker makes a conscious effort to mask their identity due to fear of being discovered. Because the use of face masks during pandemics has resulted in voice-altering strategies that may mislead the identification of a person based on voice, the study was conducted on thirty male and female subjects from the Punjab region wearing various types of masks. As a result, the goal of this study is to compare the durability of features present in normal voice with sound from the same subject wearing various types of COVID-19 protective masks. The voices were analyzed using Praat software, and a comparison study was conducted. The results of spectrographic study of mono and stereo sounds of both male and female voices revealed considerable alterations in their overall spectrum. The entire study yielded significant results that may be useful to a forensic scientist studying instances involving voice identification and analysis. © 2023 American Institute of Physics Inc.. All rights reserved.

Deep neural network and model-based clustering technique for forensic electronic mail author attribution.

Electronic mail is the primary source of different cyber scams. Identifying the author of electronic mail is essential. It forms significant documentary evidence in the field of digital forensics. This paper presents a model for email author identification (or) attribution by utilizing deep neural networks and model-based clustering techniques. It is perceived that stylometry features in the authorship identification have gained a lot of importance as it enhances the author attribution task's accuracy. The experiments were performed on a publicly available benchmark Enron dataset, considering many authors. The proposed model achieves an accuracy of 94% on five authors, 90% on ten authors, 86% on 25 authors and 75% on the entire dataset for the Deep Neural Network technique, which is a good measure of accuracy on a highly imbalanced data. The second cluster-based technique yielded an excellent 86% accuracy on the entire dataset, considering the authors' number based on their contribution to the aggregate data.

Forensic investigation of Google Meet for memory and browser artifacts

Web applications have experienced a widespread adaptation owing to the agile Service Oriented Architecture (SOA) reflecting the ever-changing software needs of users. Google Meet is one of the top video conferencing applications, especially in the post-COVID19 era. Security and privacy concerns are therefore critical. This paper presents an extensive digital forensic analysis of Google Meet running on multiple browsers and software platforms including Google Chrome, Mozilla Firefox, and Microsoft Edge browsers in Windows 10 and Linux. Artifacts, traces of potential evidence, are extracted from different locations on a client's desktop, including the memory and browser. These include meeting records, communication records, email addresses, profile pictures, history, downloads, bookmarks, cache, cookies, etc. We explore how different Random Access Memory (RAM) sizes of client devices impact the persistence and format of extracted memory artifacts. A memory artifact extraction tool is developed to automate the extraction of artifacts identified via unstructured string analysis. Google Meet forensic artifacts are critical in that they are potential digital evidence in relevant criminal investigations. Additionally, they highlight that user data can be extracted despite implementing multiple privacy and security mechanisms. © 2022 The Author(s)

We are meeting on Microsoft Teams: Forensic analysis in Windows, Android, and iOS operating systems.

Microsoft released a new communication platform, Microsoft Teams, in 2017. Due in part to COVID-19, the popularity of communication platforms, like Microsoft Teams, increased exponentially. Given its user base and increased popularity, it seems likely that digital forensic investigators will encounter cases where Microsoft Teams is a relevant component. However, because Microsoft Teams is a relatively new application, there is limited forensic research on the application particularly focusing on mobile operating systems. To address this gap, an analysis of data stored at rest by Microsoft Teams was conducted on the Windows 10 operating system as well as on Android and Apple iOS mobile operating systems. Basic functionalities, such as messaging, sharing files, participating in video conferences, and other functionalities that Teams provides, were performed in an isolated testing environment. Cellebrite UFED Physical Analyzer and Magnet AXIOM Examine tools were used to analyze the mobile devices and the Windows device, respectively. Manual or non-automated investigation recovered, at least partially, the majority of artifacts across all three operating systems. In this study, a total of 77.6% of the populated artifacts were partially or fully recovered in the manual investigation. On the other hand, forensic tools used did not automatically recover many of the artifacts found with the manual investigation. Only 13.8% of artifacts were partially or fully recovered by the forensic tools across all three devices. These discovered artifacts and the results of the investigations are presented in order to aid digital forensic investigations.

Forensic investigation of Cisco WebEx desktop client, web, and Android smartphone applications.

Digital forensic analysis of videoconferencing applications has received considerable attention recently, owing to the wider adoption and diffusion of such applications following the recent COVID-19 pandemic. In this contribution, we present a detailed forensic analysis of Cisco WebEx which is among the top three videoconferencing applications available today. More precisely, we present the results of the forensic investigation of Cisco WebEx desktop client, web, and Android smartphone applications. We focus on three digital forensic areas, namely memory, disk space, and network forensics. From the extracted artifacts, it is evident that valuable user data can be retrieved from different data localities. These include user credentials, emails, user IDs, profile photos, chat messages, shared media, meeting information including meeting passwords, contacts, Advanced Encryption Standard (AES) keys, keyword searches, timestamps, and call logs. We develop a memory parsing tool for Cisco WebEx based on the extracted artifacts. Additionally, we identify anti-forensic artifacts such as deleted chat messages. Although network communications are encrypted, we successfully retrieve useful artifacts such as IPs of server domains and host devices along with message/event timestamps.

Minimizing Viral Transmission in COVID-19 Like Pandemics: Technologies, Challenges, and Opportunities.

Coronavirus (COVID-19) pandemic has incurred huge loss to human lives throughout the world. Scientists, researchers, and doctors are trying their best to develop and distribute the COVID-19 vaccine throughout the world at the earliest. In current circumstances, different tracking systems are utilized to control or stop the spread of the virus till the whole population of the world gets vaccinated. To track and trace patients in COVID-19 like pandemics, various tracking systems based on different technologies are discussed and compared in this paper. These technologies include, cellular, cyber, satellite-based radio navigation and low range wireless technologies. The main aim of this paper is to conduct a comprehensive survey that can overview all such tracking systems, which are used in minimizing the spread of COVID-19 like pandemics. This paper also highlights the shortcoming of each tracking systems and suggests new mechanisms to overcome such limitations. In addition, the authors propose some futuristic approaches to track patients in prospective pandemics, based on artificial intelligence and big data analysis. Potential research directions, challenges, and the introduction of next-generation tracking systems for minimizing the spread of prospective pandemics, are also discussed at the end.

Exploring the Suitability of Using Virtual Reality and Augmented Reality for Anatomy Training

Research on alternative ways to provide anatomy learning and training has increased over the past few years, especially since the COVID-19 pandemic. Virtual reality (VR) and augmented reality (AR) represent two promising alternatives in this regard. For this reason, in this work, we analyze the suitability of applying VR and AR for anatomy training, comparing an optical-based AR setup and a semi-immersive setup based on a VR table, using the same anatomy training software and the same interaction system. The AR-based setup uses a Magic Leap One, whereas the VR table is configured through the use of stereoscopic TV displays and a motion-capture system. This experiment builds on a previous one (Vergel et al., 2020) on which we have improved the AR-based setup and increased the complexity of one of the two tasks. The goal of this new experiment is to confirm whether the changes made in the setups modify the previous conclusions. Our hypothesis is that the improved AR-based setup will be more suitable, for anatomy training, than the VR-based setup. For this reason, we conducted an experimental research with 45 participants, comparing the use of an anatomy training software. Objective and subjective data were collected. The results show that the AR-based setup is the preferred choice. The differences in measurable performance were small but also favorable to the AR setup. In addition, participants provided better subjective ratings for the AR-based setup, confirming our initial hypothesis. Nevertheless, both setups offer a similar overall performance and provide excellent results in the subjective measures, with both systems approaching the highest possible values. IEEE

Forensic Data Analysis of Delivery and Transport Applications

The COVID-19 pandemic has changed many aspects of human life during last three years. One of these aspects is the adaption of new trends and technologies for everyday activities such as delivery and transportation. People now prefer to shop online and get their products delivered at home without wasting any time. Therefore, the security and importance of online and delivery applications is the main concern these days. The payment mode of these applications is online which involves personal data like bank information and user details. This problem led to the research contribution of our work. The main objective and implication of this study is to find personally identifiable information (PII) of users which uniquely identifies a person at personal and organizational scopes. In this paper, we present the forensics analysis of eight popular Android delivery and transport applications i.e. Daraz.pk, Foodpanda, Grocer app, Airlift express, Bykea, Indriver, Uber and Clicky shopping app. These applications have not been previously studied and investigated by other researchers. Furthermore, these applications are among the top android apps used by customers. It is expected that such an analysis can guide investigators towards obtaining useful information about a suspect who has used such an application on their device. The analysis process started with the installation of each application on a rooted Samsung S7 Edge smartphone. Then various activities were performed such as setting up an account, booking a ride, or ordering a delivery. After this, a physical image of the device was acquired. A detailed analysis of the image was carried out using Autopsy and all relevant artifacts were collected. A comparison of the results showed largest number of artifacts have been gathered from installation activity and the most number of unique artifacts have been collected from order and booking activity. A tabular form of analysis has also been shown with all of the source and path files from which the data has been gathered. © 2022 IEEE.

Multimodal Semantic Mismatch Detection in Social Media Posts

Short videos have become the most popular form of social media in recent years. In this work, we focus on the threat scenario where video, audio, and their text description are semantically mismatched to mislead the audience. We develop self-supervised methods to detect semantic mismatch across multiple modalities, namely video, audio and text. We use state-of-the-art language, video and audio models to extract dense features from each modality, and explore transformer architecture together with contrastive learning methods on a dataset of one million Twitter posts from 2021 to 2022. Our best-performing method benefits from the robustness of Noise-Contrastive loss and the context provided by fusing modalities together using a cross-transformer. It outperforms state-of-the-art by over 9% in accuracy. We further characterize the performance of our system on topic-specific datasets containing COVID-19 and Russia-Ukraine related tweets, and shows that it outperforms state-of-the-art by over 17% in accuracy.

Cyber Threats and the Security Risks They Pose to National Security: An Assessment of Cybersecurity Policy in the United Kingdom

The COVID-19 Pandemic has accelerated the digital transformation of organisations and services across the United Kingdom (UK) providing numerous opportunities for economic and social development in the UK. However, these opportunities also bring about unprecedented challenges for law enforcement agencies (LEAs), and has led to the progression of serious and advanced cyber threats. This chapter aims to analyse different types of cyber threats, identify the risk they pose to national security, and provide a critical evaluation of cybersecurity policy in the UK. The chapter will examine how current UK Government policies and practices effectively mitigate the cyber threats to national security, and will explore how these responses can be further developed, with reference to the National Cyber Security Centre, the Active Cyber Defence programme, and the National Cyber Security Strategy 2022–2030. The methodological approach for this chapter utilises a literature-based review to further develop research on the criminological issue of cyber threats, cybersecurity and national security in the UK. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The use of COVID-19 contact tracing app data as evidence of a crime.

This commentary draws attention to the introduction of data collected by COVID-19 tracing apps as evidence in criminal proceedings and the novel considerations this evidence presents for criminal justice agents and digital forensics professionals.

Considerations and Challenges for Real-World Deployment of an Acoustic-Based COVID-19 Screening System.

Coronavirus disease 2019 (COVID-19) has led to countless deaths and widespread global disruptions. Acoustic-based artificial intelligence (AI) tools could provide a simple, scalable, and prompt method to screen for COVID-19 using easily acquirable physiological sounds. These systems have been demonstrated previously and have shown promise but lack robust analysis of their deployment in real-world settings when faced with diverse recording equipment, noise environments, and test subjects. The primary aim of this work is to begin to understand the impacts of these real-world deployment challenges on the system performance. Using Mel-Frequency Cepstral Coefficients (MFCC) and RelAtive SpecTrAl-Perceptual Linear Prediction (RASTA-PLP) features extracted from cough, speech, and breathing sounds in a crowdsourced dataset, we present a baseline classification system that obtains an average receiver operating characteristic area under the curve (AUC-ROC) of 0.77 when discriminating between COVID-19 and non-COVID subjects. The classifier performance is then evaluated on four additional datasets, resulting in performance variations between 0.64 and 0.87 AUC-ROC, depending on the sound type. By analyzing subsets of the available recordings, it is noted that the system performance degrades with certain recording devices, noise contamination, and with symptom status. Furthermore, performance degrades when a uniform classification threshold from the training data is subsequently used across all datasets. However, the system performance is robust to confounding factors, such as gender, age group, and the presence of other respiratory conditions. Finally, when analyzing multiple speech recordings from the same subjects, the system achieves promising performance with an AUC-ROC of 0.78, though the classification does appear to be impacted by natural speech variations. Overall, the proposed system, and by extension other acoustic-based diagnostic aids in the literature, could provide comparable accuracy to rapid antigen testing but significant deployment challenges need to be understood and addressed prior to clinical use.