Precognition: Automated Digital Forensic Readiness System for Mobile Computing Devices in Enterprises

Enterprises are facing an unprecedented risk of security incidents due to the influx of emerging technologies, like smartphones and wearables. Most of the current Mobile security systems are not maturing in pace with technological advances. They lack the ability to learn and adapt from the past knowledge base. In the case of a security incident, enterprises find themselves underprepared for the lack of evidence and data. The systems are not designed to be forensic ready. There is a need for automated security analysis and forensically ready solution, which can learn and continuously adapt to new challenges, improve efficiency and productivity of the system. In this research, the authors have designed a security analysis and digital forensic readiness system targeted at smartphones and wearables in an enterprise environment. The proposed system detects applications violating security policies, analyzes Android and iOS applications to identify possible vulnerabilities on the server, apply machine learning algorithms to improve the efficiency and accuracy of vulnerability prediction. The System continuously learns from past incidents, proactively collect required information from the devices which can help in digital forensics. Machine learning techniques are applied to the set of features extracted from the decompiled Mobile applications and applications classified based on consisting of one or more vulnerabilities. The system was evaluated in a real-world enterprise environment with 14151 mobile applications and vulnerabilities was predicted with an accuracy of 94.2%. The system can also work on virtual instances of the mobile devices

Comparison of Forensic Acquisition and Analysis on an iPhone over an Android Mobile Through multiple forensic methods

Mobile phones are most widely used as mini laptops as well as personal digital devices one could have. The dependency on mobiles for every single person on every single aspect has increased day by day. Depending on the operating systems, storage capacity, user interface developed by various manufacturers, there are numerous mobile phones designed with diverse computing capabilities. Among all the distinct kinds of smart mobile devices that are available in the mobile market, iPhone became one of the most popularly used smart mobiles across the world due to its complex logical computing capabilities, striking touch interface, optimum screen resolutions. People started relying on iPhone by utilizing its functionalities including storing sensitive information, capturing pictures, making online payments by providing credentials. These factors made iPhone to be one of the best resources for the forensic department to retrieve and analyze sensitive information and provide supporting evidence. Thus, the rise of iPhone forensics took place where the data is retrieved and analyzed with the help of various iPhone forensic tool kits. The agenda of this paper is to give overview of iPhone forensics and mainly focuses on analysis done, and challenges faced while retrieving the sensitive information on iPhone by means of distinct forensic tools when compare to Android mobile device forensic

Smartphone data evaluation model : identifying authentic smartphone data

Ever improving smartphone technology, along with the widespread use of the devices to accomplish daily tasks, leads to the collection of rich sources of smartphone data. Smartphone data are, however, susceptible to change and can be altered intentionally or accidentally by end-users or installed applications. It becomes, therefore, important to establish the authenticity of smartphone data, confirming the data refer to actual events, before submitting the data as potential evidence. This paper focuses on data created by smartphone applications and the techniques that can be used to establish the authenticity of the data. To identify authentic smartphone data, a better understanding of the smartphone, related smartphone applications and the environment in which the smartphone operates are required. From the gathered knowledge and insight, requirements are identified that authentic smartphone data must adhere to. These requirements are captured in a new model to assist digital forensic professionals with the evaluation of smartphone data. Experiments, involving different smartphones, are conducted to determine the practicality of the new evaluation model with the identification of authentic smartphone data. The presented results provide preliminary evidence that the suggested model offers the necessary guidance to identify authentic smartphone data.http://www.elsevier.com/locate/diinhj2019Computer Scienc

Evaluation and Identification of Authentic Smartphone Data

Mobile technology continues to evolve in the 21st century, providing end-users with mobile devices that support improved capabilities and advance functionality. This ever-improving technology allows smartphone platforms, such as Google Android and Apple iOS, to become prominent and popular among end-users. The reliance on and ubiquitous use of smartphones render these devices rich sources of digital data. This data becomes increasingly important when smartphones form part of regulatory matters, security incidents, criminal or civil cases. Digital data is, however, susceptible to change and can be altered intentionally or accidentally by end-users or installed applications. It becomes, therefore, essential to evaluate the authenticity of data residing on smartphones before submitting the data as potential digital evidence. This thesis focuses on digital data found on smartphones that have been created by smartphone applications and the techniques that can be used to evaluate and identify authentic data. Identification of authentic smartphone data necessitates a better understanding of the smartphone, the related smartphone applications and the environment in which the smartphone operates. Derived from the conducted research and gathered knowledge are the requirements for authentic smartphone data. These requirements are captured in the smartphone data evaluation model to assist digital forensic professionals with the assessment of smartphone data. The smartphone data evaluation model, however, only stipulates how to evaluate the smartphone data and not what the outcome of the evaluation is. Therefore, a classification model is constructed using the identified requirements and the smartphone data evaluation model. The classification model presents a formal classification of the evaluated smartphone data, which is an ordered pair of values. The first value represents the grade of the authenticity of the data and the second value describes the completeness of the evaluation. Collectively, these models form the basis for the developed SADAC tool, a proof of concept digital forensic tool that assists with the evaluation and classification of smartphone data. To conclude, the evaluation and classification models are assessed to determine the effectiveness and efficiency of the models to evaluate and identify authentic smartphone data. The assessment involved two attack scenarios to manipulate smartphone data and the subsequent evaluation of the effects of these attack scenarios using the SADAC tool. The results produced by evaluating the smartphone data associated with each attack scenario confirmed the classification of the authenticity of smartphone data is feasible. Digital forensic professionals can use the provided models and developed SADAC tool to evaluate and identify authentic smartphone data. The outcome of this thesis provides a scientific and strategic approach for evaluating and identifying authentic smartphone data, offering needed assistance to digital forensic professionals. This research also adds to the field of digital forensics by providing insights into smartphone forensics, architectural components of smartphone applications and the nature of authentic smartphone data.Thesis (PhD)--University of Pretoria, 2019.Computer SciencePhDUnrestricte

BYOD: Risk considerations in a South African organisation

In recent times, while numerous organisations have difficulty keeping abreast with the frequent year-on-year technology changes, their employees on the other hand, continue to bring their personal devices to work to more readily access organisational data. This concept is known as Bring Your Own Device (BYOD). Studies have demonstrated that the introduction of BYOD commonly has a positive effect on both organisation and employees: increased optimism, job satisfaction and productivity are some of the perceived positive effects. Furthermore, BYOD can improve employees’ opportunities for mobile working and assist with the work flexibility they seek. This phenomenon, however, is still not well understood. In the South African context, this refers particularly to an inadequate understanding of risks associated with the introduction of BYOD into organisations. Some of the risks associated with this phenomenon are, for instance, related to information security, legislation and privacy issues. Hence, the intention of this research was to investigate, determine and assess BYOD risk considerations in a South African organisation. Using the available literature on this subject and an interpretative exploratory case study approach, this research explored various facets of BYOD-related risks (e.g. implementational, technological, legislation, regulation and privacy risks, human aspects and organisational concerns) as well as the impact these risks may have on both employees and an organisation. The organisation under investigation – from this point onward referred to as “Organisation A” – is a South African based information technology (IT) security consulting and service management organisation, which has seen increased expansion in its business and thus an increase in the number of its employees utilising their personal devices at the workplace. Even so, Organisation A was uncertain regarding possible risks that might hinder benefits of BYOD. Hence, this researcher defined the main research question as “What are the risks of introducing the BYOD in the South African organisation and what is an effective approach to address identified risks?”. The main objective was to identify and describe BYOD-related risks and to propose an appropriate model for addressing these risks. To answer the main research question, this researcher reviewed the applicable literature on the BYOD, including the limited South African literature pertaining to the subject. The review elicited the most common BYOD-related risks but also some models, frameworks and standards that may be applied for addressing these risks. Based on these revelations, an applicable BYOD risk management model was created and proposed. The literature review findings were subsequently tested in the empirical setting (in Organisation A) by conducting comprehensive interviews with research participants. This research adopted a qualitative approach in general and a case study methodology in particular. The collected data were analysed using the interpretative phenomenological analysis (IPA), which aided in providing a comprehensive understanding of the interviewees’ responses regarding the BYOD risks. The interviewees were selected based on a purposeful (pre-defined) sampling. The results of this interpretative research suggest that the interviewees’ responses are closely aligned with the information on BYOD risks collected from the pertinent literature. The results show that successful introduction and usage of BYOD in the studied organisation requires the implementation of mixed risk management measures: technological (e.g. mobile device management and its additional components), non-technological (e.g. IT or BYOD security policies), the usage of general risk management frameworks (e.g. ISO 27001), the development of an organisational security culture and skilling of the human factor (e.g. employee awareness, training and education, for example). Additionally, it was found that participation of employees in the development of BYOD policies is an essential and effective tactic for transforming a fragile BYOD risk link (i.e. employees) into a strong risk prevention mechanism. Furthermore, this research also revealed that in the South African context, it is important that an organisation’s BYOD security policies are sound, preferably meeting the POPI Act requirements and thereby avoiding legislation risks. The contribution of this research is twofold: first academic, and second, practical. The academic contribution is realised by adding to the body of knowledge on the BYOD risks – most particularly in terms of understanding potential risks when introducing BYOD in the South African context. The practical contribution manifests through the provision of detailed risk considerations and mitigation guidelines for organisations wishing to introduce BYOD practices or considering ways to improve their current BYOD risk management strategy. It is acknowledged that this research has some limitations, particularly in regard to the limited generalisation of the findings due to the limited sample provided by only one organisation. Although the results are not necessarily applicable to other South African organisations, these limitations did not impact the relevance and validity of this research

Data-centric security : towards a utopian model for protecting corporate data on mobile devices

Data-centric security is significant in understanding, assessing and mitigating the various risks and impacts of sharing information outside corporate boundaries. Information generally leaves corporate boundaries through mobile devices. Mobile devices continue to evolve as multi-functional tools for everyday life, surpassing their initial intended use. This added capability and increasingly extensive use of mobile devices does not come without a degree of risk - hence the need to guard and protect information as it exists beyond the corporate boundaries and throughout its lifecycle. Literature on existing models crafted to protect data, rather than infrastructure in which the data resides, is reviewed. Technologies that organisations have implemented to adopt the data-centric model are studied. A utopian model that takes into account the shortcomings of existing technologies and deficiencies of common theories is proposed. Two sets of qualitative studies are reported; the first is a preliminary online survey to assess the ubiquity of mobile devices and extent of technology adoption towards implementation of data-centric model; and the second comprises of a focus survey and expert interviews pertaining on technologies that organisations have implemented to adopt the data-centric model. The latter study revealed insufficient data at the time of writing for the results to be statistically significant; however; indicative trends supported the assertions documented in the literature review. The question that this research answers is whether or not current technology implementations designed to mitigate risks from mobile devices, actually address business requirements. This research question, answered through these two sets qualitative studies, discovered inconsistencies between the technology implementations and business requirements. The thesis concludes by proposing a realistic model, based on the outcome of the qualitative study, which bridges the gap between the technology implementations and business requirements. Future work which could perhaps be conducted in light of the findings and the comments from this research is also considered

Mining structural and behavioral patterns in smart malware

Mención Internacional en el título de doctorFuncas. Premio Enrique Fuentes Quintana 2016.Smart devices equipped with powerful sensing, computing and networking capabilities have proliferated lately, ranging from popular smartphones and tablets to Internet appliances, smart TVs, and others that will soon appear (e.g., watches, glasses, and clothes). One key feature of such devices is their ability to incorporate third-party apps from a variety of markets. This poses strong security and privacy issues to users and infrastructure operators, particularly through software of malicious (or dubious) nature that can easily get access to the services provided by the device and collect sensory data and personal information. Malware in current smart devices—mostly smartphones and tablets—has rocketed in the last few years, supported by sophisticated techniques (e.g., advanced obfuscation and targeted infection and activation engines) purposely designed to overcome security architectures currently in use by such devices. This phenomenon is known as the proliferation of smart malware. Even though important advances have been made on malware analysis and detection in traditional personal computers during the last decades, adopting and adapting those techniques to smart devices is a challenging problem. For example, power consumption is one major constraint that makes unaffordable to run traditional detection engines on the device, while externalized (i.e., cloud-based) techniques raise many privacy concerns. This Thesis examines the problem of smart malware in such devices, aiming at designing and developing new approaches to assist security analysts and end users in the analysis of the security nature of apps. We first present a comprehensive analysis on how malware has evolved over the last years, as well as recent progress made to analyze and detect malware. Additionally, we compile a suit of the most cutting-edge open source tools, and we design a versatile and multipurpose research laboratory for smart malware analysis and detection. Second, we propose a number of methods and techniques aiming at better analyzing smart malware in scenarios with a constant and large stream of apps that require security inspection. More precisely, we introduce Dendroid, an effective system based on text mining and information retrieval techniques. Dendroid uses static analysis to measures the similarity between malware samples, which is then used to automatically classify them into families with remarkably accuracy. Then, we present Alterdroid, a novel dynamic analysis technique for automatically detecting hidden or obfuscated malware functionality. Alterdroid introduces the notion of differential fault analysis for effectively mining obfuscated malware components distributed as parts of an app package. Next, we present an evaluation of the power-consumption trade-offs among different strategies for off-loading, or not, certain security tasks to the cloud. We develop a system for testing several functional tasks and metering their power consumption called Meterdroid. Based on the results obtained in this analysis, we then propose a cloud-based system, called Targetdroid, that addresses the problem of detecting targeted malware by relying on stochastic models of usage and context events derived from real user traces. Based on these models, we build an efficient automatic testing system capable of triggering targeted malware. Finally, based on the conclusions extracted from this Thesis, we propose a number of open research problems and future directions where there is room for researchLos dispositivos inteligentes se han posicionado en pocos años como aparatos altamente populares con grandes capacidades de cómputo, comunicación y sensorización. Entre ellos se encuentran dispositivos como los teléfonos móviles inteligentes (o smartphones), las televisiones inteligentes, o más recientemente, los relojes, las gafas y la ropa inteligente. Una característica clave de este tipo de dispositivos es su capacidad para incorporar aplicaciones de terceros desde una gran variedad de mercados. Esto plantea fuertes problemas de seguridad y privacidad para sus usuarios y para los operadores de infraestructuras, sobre todo a través de software de naturaleza maliciosa (o malware), el cual es capaz de acceder fácilmente a los servicios proporcionados por el dispositivo y recoger datos sensibles de los sensores e información personal. En los últimos años se ha observado un incremento radical del malware atacando a estos dispositivos inteligentes—principalmente a smartphones—y apoyado por sofisticadas técnicas diseñadas para vencer los sistemas de seguridad implantados por los dispositivos. Este fenómeno ha dado pie a la proliferación de malware inteligente. Algunos ejemplos de estas técnicas inteligentes son el uso de métodos de ofuscación, de estrategias de infección dirigidas y de motores de activación basados en el contexto. A pesar de que en las últimos décadas se han realizado avances importantes en el análisis y la detección de malware en los ordenadores personales, adaptar y portar estas técnicas a los dispositivos inteligentes es un problema difícil de resolver. En concreto, el consumo de energía es una de las principales limitaciones a las que están expuestos estos dispositivos. Dicha limitación hace inasequible el uso de motores tradicionales de detección. Por el contrario, el uso de estrategias de detección externalizadas (es decir, basadas en la nube) suponen una gran amenaza para la privacidad de sus usuarios. Esta tesis analiza el problema del malware inteligente que adolece a estos dispositivos, con el objetivo de diseñar y desarrollar nuevos enfoques que permitan ayudar a los analistas de seguridad y los usuarios finales en la tarea de analizar aplicaciones. En primer lugar, se presenta un análisis exhaustivo sobre la evolución que el malware ha seguido en los últimos años, así como los avances más recientes enfocados a analizar apps y detectar malware. Además, integramos y extendemos las herramientas de código abierto más avanzadas utilizadas por la comunidad, y diseñamos un laboratorio que permite analizar malware inteligente de forma versátil y polivalente. En segundo lugar, se proponen una serie de técnicas dirigida a mejorar el análisis de malware inteligente en escenarios dónde se requiere analizar importantes cantidad de muestras. En concreto, se propone Dendroid, un sistema basado en minería de textos que permite analizar conjuntos de apps de forma eficaz. Dendroid hace uso de análisis estático de código para extraer una medida de la similitud entre distintas las muestras de malware. Dicha distancia permitirá posteriormente clasificar cada muestra en su correspondiente familia de malware de forma automática y con gran precisión. Por otro lado, se propone una técnica de análisis dinámico de código, llamada Alterdroid, que permite detectar automáticamente funcionalidad oculta y/o ofuscada. Alterdroid introduce la un nuevo método de análisis basado en la inyección de fallos y el análisis diferencial del comportamiento asociado. Por último, presentamos una evaluación del consumo energético asociado a diferentes estrategias de externalización usadas para trasladar a la nube determinadas tareas de seguridad. Para ello, desarrollamos un sistema llamado Meterdroid que permite probar distintas funcionalidades y medir su consumo. Basados en los resultados de este análisis, proponemos un sistema llamado Targetdroid que hace uso de la nube para abordar el problema de la detección de malware dirigido o especializado. Dicho sistema hace uso de modelos estocásticos para modelar el comportamiento del usuario así como el contexto que les rodea. De esta forma, Targetdroid permite, además, detectar de forma automática malware dirigido por medio de estos modelos. Para finalizar, a partir de las conclusiones extraídas en esta Tesis, identificamos una serie de líneas de investigación abiertas y trabajos futuros basados.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Francisco Javier López Muñoz.- Secretario: Jesús García Herrero.- Vocal: Nadarajah Asoka

FORENSIC-READY SECURE iOS APPS FOR JAILBROKEN iPHONES

Part 4: MOBILE DEVICE FORENSICSInternational audienceApple iOS is one of the most popular smartphone operating systems, but it restricts the installation of apps that are not from the Apple App Store. As a result, users often jailbreak their iPhones to defeat this restriction. Jailbroken iPhones are making their way into enterprises that have a Bring Your Own Device (BYOD) policy, but these devices are often barred or restricted by mobile device management software because they pose security risks. This chapter describes the iSecureRing solution that secures mobile apps and preserves the dates and timestamps of events in order to support forensic examinations of jailbroken iPhones. An analysis of the literature reveals that iSecureRing is the first forensicready mobile app security solution for iOS applications that execute in unsecured enterprise environments

Mobile Forensics – The File Format Handbook

This open access book summarizes knowledge about several file systems and file formats commonly used in mobile devices. In addition to the fundamental description of the formats, there are hints about the forensic value of possible artefacts, along with an outline of tools that can decode the relevant data. The book is organized into two distinct parts: Part I describes several different file systems that are commonly used in mobile devices. · APFS is the file system that is used in all modern Apple devices including iPhones, iPads, and even Apple Computers, like the MacBook series. · Ext4 is very common in Android devices and is the successor of the Ext2 and Ext3 file systems that were commonly used on Linux-based computers. · The Flash-Friendly File System (F2FS) is a Linux system designed explicitly for NAND Flash memory, common in removable storage devices and mobile devices, which Samsung Electronics developed in 2012. · The QNX6 file system is present in Smartphones delivered by Blackberry (e.g. devices that are using Blackberry 10) and modern vehicle infotainment systems that use QNX as their operating system. Part II describes five different file formats that are commonly used on mobile devices. · SQLite is nearly omnipresent in mobile devices with an overwhelming majority of all mobile applications storing their data in such databases. · The second leading file format in the mobile world are Property Lists, which are predominantly found on Apple devices. · Java Serialization is a popular technique for storing object states in the Java programming language. Mobile application (app) developers very often resort to this technique to make their application state persistent. · The Realm database format has emerged over recent years as a possible successor to the now ageing SQLite format and has begun to appear as part of some modern applications on mobile devices. · Protocol Buffers provide a format for taking compiled data and serializing it by turning it into bytes represented in decimal values, which is a technique commonly used in mobile devices. The aim of this book is to act as a knowledge base and reference guide for digital forensic practitioners who need knowledge about a specific file system or file format. It is also hoped to provide useful insight and knowledge for students or other aspiring professionals who want to work within the field of digital forensics. The book is written with the assumption that the reader will have some existing knowledge and understanding about computers, mobile devices, file systems and file formats