20 research outputs found
Development and validation of a porcine artificial colonic mucus model reflecting the properties of native colonic mucus in pigs
Colonic mucus plays a key role in colonic drug absorption. Mucus permeation assays could therefore provide useful insights and support rational formulation development in the early stages of drug development. However, the collection of native colonic mucus from animal sources is labor-intensive, does not yield amounts that allow for routine experimentation, and raises ethical concerns. In the present study, we developed an in vitro porcine artificial colonic mucus model based on the characterization of native colonic mucus. The structural properties of the artificial colonic mucus were validated against the native secretion for their ability to capture key diffusion patterns of macromolecules in native mucus. Moreover, the artificial colonic mucus could be stored under common laboratory conditions, without compromising its barrier properties. In conclusion, the porcine artificial colonic mucus model can be considered a biorelevant way to study the diffusion behavior of drug candidates in colonic mucus. It is a cost-efficient screening tool easily incorporated into the early stages of drug development and it contributes to the implementation of the 3Rs (refinement, reduction, and replacement of animals) in the drug development process
User relationship classification of facebook messenger mobile data using WEKA
© Springer Nature Switzerland AG 2018. Mobile devices are a wealth of information about its user and their digital and physical activities (e.g. online browsing and physical location). Therefore, in any crime investigation artifacts obtained from a mobile device can be extremely crucial. However, the variety of mobile platforms, applications (apps) and the significant size of data compound existing challenges in forensic investigations. In this paper, we explore the potential of machine learning in mobile forensics, and specifically in the context of Facebook messenger artifact acquisition and analysis. Using Quick and Choo (2017)’s Digital Forensic Intelligence Analysis Cycle (DFIAC) as the guiding framework, we demonstrate how one can acquire Facebook messenger app artifacts from an Android device and an iOS device (the latter is, using existing forensic tools. Based on the acquired evidence, we create 199 data-instances to train WEKA classifiers (i.e. ZeroR, J48 and Random tree) with the aim of classifying the device owner’s contacts and determine their mutual relationship strength
ForEmSys: An integration of live forensic acquisition methods in next generation PPDR systems
Tese no âmbito do Programa de Doutoramento em Ciências e Tecnologias da Informação, apresentada ao Departamento de Engenharia Informática da Faculdade de Ciências e Tecnologia da Universidade de Coimbra.Os dispositivos móveis substituÃram os computadores pessoais e portáteis em
muitos aspectos da rotina diária das pessoas. Na practica, eles transformaramse em impressões digitais que carregam uma quantidade crÃtica de informações
pessoais, que variam desde conteúdo multimedia e registos de comunicação,
a geolocalização e dados de transações eletrônicas. No entanto, o uso de
dispositivos móveis não se limita à s interacções pessoais de um indivÃduo.
Os dispositivos móveis podem constituir partes de redes de comunicação
corporativas ou dedicadas.
As redes corporativas e da emergência como os sistemas de Proteção Pública
e Mitigação de Desastres (PPDR), exigem o estabelecimento de um ambiente
altamente seguro, para proteger vários bens crÃticos. Além disso, organizações
como a PolÃcia Judiciária acedem dados de dispositivos móveis de terceiras
entidades como provas para investigações criminais.
A aquisição e análise forense móvel têm um papel crucial tanto na proteção de
um ambiente PPDR contra ataques intencionais ou uso indevido dos utilizadores,
como na condução de uma investigação criminal robusta. Esta tese estuda
o papel da aquisição e análise forense para sistemas PPDR, introduzindo
uma metodologia para perfs digitais automatizados e identifcação de padrões
suspeitos a partir de dados e metadados de dispositivos móveis.
Três técnicas de computação inteligente, nomeadamente Fuzzy Systems, Redes
Neuronais (RNs) e Adaptive Neuro-Fuzzy Inference System (ANFIS) são usadas
para construir perfs criminais e identifcar padrões suspeitos em dados e
metadados provenientes de chamadas e SMS para três cenários de casos de uso
diferentes. Mais especifcamente, os Sistemas Fuzzy servÃram como prova de
conceito para detectar a deserção de agentes PPDR realizada por SMS. Um
cenário mais complexo envolveu o uso de RNs e ANFIS, que foram empregados
como meio de identifcação de padrões suspeitos de chamadas e SMS para casos
de cyberbullying e de tráfico de droga.
Os resultados que foram produzidos durante todas as fases experimentais foram
bastante satisfatórios e foram comparados para defnir a técnica mais apropriada
para a identifcação de padrões suspeitos.Mobile devices have substituted desktop and portable computers in many
aspects of people’s everyday routine. Practically, they have become digital
fngerprints that carry a critical amount of personal information, varying from
multimedia and communication logs to geolocation and electronic transaction
data. Moreover, the usage of mobile devices is not limited to an individual’s
personal interactions. The aforementioned devices may also constitute parts of
corporate or dedicated communication networks.
Enterprise and frst-responder communication networks, such as Public
Protection and Disaster Relief (PPDR) systems require the establishment of a
highly secured environment, in order to protect various critical assets. Moreover,
services such as law enforcement may need to access third-party mobile device
data as evidence for criminal investigations.
Mobile forensic acquisition and analysis play a crucial role towards both the
protection of a PPDR environment against intentional attacks or potential user
misuse and the conduction of a robust criminal investigation. The current thesis
studies the role of forensic analysis in use cases related to law enforcement
investigations by introducing a methodology for automated digital profling and
suspicious pattern identifcation from mobile device data and metadata.
Three intelligent computation techniques, namely Fuzzy Systems, Neural
Networks (NNs) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are
used for constructing criminal profles and identifying suspicious patterns in
calls and SMS evidence data and metadata for three different use case scenarios.
More specifcally, Fuzzy Systems served as proof-of-concept for detecting PPDR
ofcers’ defection performed by SMS. A more complex scenario for call and
SMS suspicious pattern identifcation of cyberbullying and low-level drug dealing
cases was documented with the use of NNs and ANFIS
Understanding the gastrointestinal mucus and its impact on drug absorption
The gastrointestinal mucus is a hydrogel lining the luminal side of the gastrointestinal epithelium. Mucus is vital for gut homeostasis because it protects the epithelium from the noxious external environment. However, from a drug delivery perspective, drugs have to permeate through the mucus to reach the epithelium; therefore, mucus might pose a barrier to drug absorption. Most of the information about mucus derives from fundamental studies performed on rodents. However, information from larger preclinical animal species is highly warranted for improving study designs and guiding better interpretation of data from preclinical assessments. Furthermore, improved understanding of the nature of the gastrointestinal mucus would enable the development of in vitro mucus models with increased biorelevance. These could then be implemented in drug absorption assays to improve the (bio)predictability. Well-informed in vitro mucus models would enable quick and less variable screening of drug candidates in the early drug development stages. Finally, these models would contribute to reduction, refinement, and replacement (the 3Rs) of animal usage in the drug development process. This thesis aims to improve our understanding of the nature of gastrointestinal mucus and its impact on drug absorption. For this purpose, mucus from the complete gastrointestinal tract of pigs and dogs was characterized and the diffusion of physicochemically diverse FITC-dextrans through colonic mucus was studied, both ex vivo and in vitro. The characterization of the gastrointestinal mucus focused on properties relevant for drug absorption and revealed the physiological characteristics, composition, and structural profiles from the various gastrointestinal regions. The findings pointed towards substantial differences between small intestinal and colonic mucus in both species and served as the basis for developing artificial colonic mucus models for drug permeation assessments. Porcine and canine artificial mucus models were developed and validated against the respective native secretions in terms of structural properties and demonstrated their potential to capture the key diffusion patterns of FITC-dextrans observed in native colonic mucus. Overall, this work provided insights into key properties of mucus from large preclinical species and validated tools for the assessment of the impact of mucus on drug absorption
Revisiting the Detection of Lateral Movement through Sysmon
This work attempts to answer in a clear way the following key questions regarding the optimal initialization of the Sysmon tool for the identification of Lateral Movement in the MS Windows ecosystem. First, from an expert’s standpoint and with reference to the relevant literature, what are the criteria for determining the possibly optimal initialization features of the Sysmon event monitoring tool, which are also applicable as custom rules within the config.xml configuration file? Second, based on the identified features, how can a functional configuration file, able to identify as many LM variants as possible, be generated? To answer these questions, we relied on the MITRE ATT and CK knowledge base of adversary tactics and techniques and focused on the execution of the nine commonest LM methods. The conducted experiments, performed on a properly configured testbed, suggested a great number of interrelated networking features that were implemented as custom rules in the Sysmon’s config.xml file. Moreover, by capitalizing on the rich corpus of the 870K Sysmon logs collected, we created and evaluated, in terms of TP and FP rates, an extensible Python .evtx file analyzer, dubbed PeX, which can be used towards automatizing the parsing and scrutiny of such voluminous files. Both the .evtx logs dataset and the developed PeX tool are provided publicly for further propelling future research in this interesting and rapidly evolving field
A Survey on Mobile Malware Detection Techniques
Modern mobile devices are equipped with a variety of tools and services, and handle increasing amounts of sensitive information. In the
same trend, the number of vulnerabilities exploiting mobile devices are also augmented on a daily basis and, undoubtedly, popular mobile platforms, such as Android and iOS, represent an alluring target for malware writers. While researchers strive to find alternative detection approaches to fight against mobile malware, recent reports exhibit an alarming increase in mobile malware exploiting victims to create revenues, climbing towards a billion-dollar industry. Current approaches to mobile malware analysis and detection cannot always keep up with future malware sophistication [2] [4]. The aim of this work is to provide a structured and comprehensive overview of the latest research on mobile malware detection techniques and pinpoint their benefits and limitations.JRC.E.3-Cyber and Digital Citizens' Securit
Mobile Forensic Data Analysis: Suspicious Pattern Detection in Mobile Evidence
Culprits' identi cation by the means of suspicious pattern detection techniques from mobile
device data is one of the most important aims of the mobile forensic data analysis. When criminal activities
are related to entirely automated procedures such as malware propagation, predicting the corresponding
behavior is a rather achievable task. However, when human behavior is involved, such as in cases of
traditional crimes, prediction and detection become more compelling. This paper introduces a combined
criminal pro ling and suspicious pattern detection methodology for two criminal activities with moderate to
the heavy involvement of mobile devices, cyberbullying and low-level drug dealing. Neural and Neurofuzzy
techniques are applied on a hybrid original and simulated dataset. The respective performance results are
measured and presented, the optimal technique is selected, and the scenarios are re-run on an actual dataset
for additional testing and veri cation
Physiological properties, composition and structural profiling of porcine gastrointestinal mucus
The gastrointestinal mucus is a hydrogel that lines the luminal side of the gastrointestinal epithelium, offering barrier protection from pathogens and lubrication of the intraluminal contents. These barrier properties likewise affect nutrients and drugs that need to penetrate the mucus to reach the epithelium prior to absorption. In order to assess the potential impact of the mucus on drug absorption, we need information about the nature of the gastrointestinal mucus. Today, most of the relevant available literature is mainly derived from rodent studies. In this work, we used a larger animal species, the pig model, to characterize the mucus throughout the length of the gastrointestinal tract. This is the first report of the physiological properties (physical appearance, pH and water content), composition (protein, lipid and metabolite content) and structural profiling (rheology and gel network) of the porcine gastrointestinal mucus. These findings allow for direct comparisons between the characteristics of mucus from various segments and can be further utilized to improve our understanding of the role of the mucus on region dependent drug absorption. Additionally, the present work is expected to contribute to the assessment of the porcine model as a preclinical species in the drug development process
The use of BioGIT system to assess the impact of dose and formulation on early exposure to low solubility drugs after oral administration
Purpose: To evaluate the usefulness of the Biorelevant Gastrointestinal Transfer (BioGIT) system in assessing the impact of dose and formulation on early exposure of five lipophilic active pharmaceutical ingredients (APIs) by comparing in vitro data with previously collected plasma data in healthy adults.
Methods: Lu 35-138 (HCl salt of weak base) was studied at three dose levels in a-hydroxypropyl-beta-cyclodextrin solution. Fenofibrate was studied at two dose levels in the form of a solid dispersion formulation. AZD2207 (hemi-1,5-naphthalenedisulfonate salt of weak base) was studied at three dose levels in the form of immediate release capsules. SB705498 (weak base) was studied in the form of immediate release tablets and capsules. Cyclosporine A was studied in the form of self-emulsifying drug delivery systems, Sandimmun® and Sandimmun® Neoral. Duodenal concentrations were estimated using the BioGIT system. AUC0-0.75h values were calculated from the apparent concentration versus time data in the duodenal compartment of the BioGIT system and were used for estimating early exposure. Differences in AUC0-0.75h values were evaluated versus differences in AUC0-1h and in AUC0-2h values calculated from previously collected individual plasma data in healthy adults.
Results: The BioGIT system qualitatively identified the impact of dose and of formulation on early exposure in all cases. Quantitative prediction of the impact of dose was possible for the fenofibrate solid dispersion tablets. Although trends matched, slight underestimation of differences in early exposure was observed for the three weak bases. Overestimation of the impact of formulation in early exposure to cyclosporine was observed.
Conclusions: The BioGIT system was useful for detecting the impact of dose and of formulation on early exposure to five model lipophilic APIs