A Survey of Performance Optimization for Mobile Applications

Nowadays there is a mobile application for almost everything a user may think of, ranging from paying bills and gathering information to playing games and watching movies. In order to ensure user satisfaction and success of applications, it is important to provide high performant applications. This is particularly important for resource constraint systems such as mobile devices. Thereby, non-functional performance characteristics, such as energy and memory consumption, play an important role for user satisfaction. This paper provides a comprehensive survey of non-functional performance optimization for Android applications. We collected 155 unique publications, published between 2008 and 2020, that focus on the optimization of non-functional performance of mobile applications. We target our search at four performance characteristics, in particular: responsiveness, launch time, memory and energy consumption. For each performance characteristic, we categorize optimization approaches based on the method used in the corresponding publications. Furthermore, we identify research gaps in the literature for future work

Security and Privacy for Ubiquitous Mobile Devices

We live in a world where mobile devices are already ubiquitous. It is estimated that in the United States approximately two thirds of adults own a smartphone, and that for many, these devices are their primary method of accessing the Internet. World wide, it is estimated that in May of 2014 there were 6.9 billion mobile cellular subscriptions, almost as much as the world population. of these 6.9 billion, approximately 1 billion are smart devices, which are concentrated in the developed world. In the developing world, users are moving from feature phones to smart devices as a result of lower prices and marketing efforts. Because smart mobile devices are ubiquitous, security and privacy are primary concerns. Threats such as mobile malware are already substantial, with over 2500 different types identified in 2010 alone. It is likely that, as the smart device market continues to grow, so to will concerns about privacy, security, and malicious software. This is especially true, because these mobile devices are relatively new. Our research focuses on increasing the security and privacy of user data on smart mobile devices. We propose three applications in this domain: (1) a service that provides private, mobile location sharing; (2) a secure, intuitive proximity networking solution; and (3) a potential attack vector in mobile devices, which utilizes novel covert channels. We also propose a first step defense mechanism against these covert channels. Our first project is the design and implementation of a service, which provides users with private and secure location sharing. This is useful for a variety of applications such as online dating, taxi cab services, and social networking. Our service allows users to share their location with one another with trust and location based access controls. We allow users to identify if they are within a certain distance of one another, without either party revealing their location to one another, or any third party. We design this service to be practical and efficient, requiring no changes to the cellular infrastructure and no explicit encryption key management for the users. For our second application, we build a modem, which enables users to share relatively small pieces of information with those that are near by, also known as proximity based networking. Currently there are several mediums which can be used to achieve proximity networking such as NFC, bluetooth, and WiFi direct. Unfortunately, these currently available schemes suffer from a variety of drawbacks including slow adoption by mobile device hardware manufactures, relatively poor usability, and wide range, omni-directional propagation. We propose a new scheme, which utilizes ultrasonic (high frequency) audio on typical smart mobile devices, as a method of communication between proximal devices. Because mobile devices already carry the necessary hardware for ultrasound, adoption is much easier. Additionally, ultrasound has a limited and highly intuitive propagation pattern because it is highly directional, and can be easily controlled using the volume controls on the devices. Our ultrasound modem is fast, achieving several thousand bits per second throughput, non-intrusive because it is inaudible, and secure, requiring attackers with normal hardware to be less than or equal to the distance between the sender and receiver (a few centimeters in our tests). Our third work exposes a novel attack vector utilizing physical media covert channels on smart devices, in conjunction with privilege escalation and confused deputy attacks. This ultimately results in information leakage attacks, which allow the attacker to gain access to sensitive information stored on a user\u27s smart mobile device such as their location, passwords, emails, SMS messages and more. Our attack uses our novel physical media covert channels to launder sensitive information, thereby circumventing state of the art, taint-tracking analysis based defenses and, at the same time, the current, widely deployed permission systems employed by mobile operating systems. We propose and implement a variety of physical media covert channels, which demonstrate different strengths such as high speed, low error rate, and stealth. By proposing several different channels, we make defense of such an attack much more difficult. Despite the challenging situation, in this work we also propose a novel defense technique as a first step towards research on more robust approaches. as a contribution to the field, we present these three systems, which together enrich the smart mobile experience, while providing mobile security and keeping privacy in mind. Our third approach specifically, presents a unique attack, which has not been seen in the wild , in an effort to keep ahead of malicious efforts

Code-Reuse Attacks and Defenses

Exploitation of memory corruption vulnerabilities in widely used software has been a threat for almost three decades and no end seems to be in sight. In particular, code-reuse techniques such as return-oriented programming offer a robust attack technique that is extensively used to exploit memory corruption vulnerabilities in modern software programs (e.g. web browsers or document viewers). Whereas conventional control-flow attacks (runtime exploits) require the injection of malicious code, code-reuse attacks leverage code that is already present in the address space of an application to undermine the security model of data execution prevention (DEP). In addition, code-reuse attacks in conjunction with memory disclosure attack techniques circumvent the widely applied memory protection model of address space layout randomization (ASLR). To counter this ingenious attack strategy, several proposals for enforcement of control-flow integrity (CFI) and fine-grained code randomization have emerged. In this dissertation, we explore the limitations of existing defenses against code-reuse attacks. In particular, we demonstrate that various coarse-grained CFI solutions can be effectively undermined, even under weak adversarial assumptions. Moreover, we explore a new return-oriented programming attack technique that is solely based on indirect jump and call instructions to evade detection from defenses that perform integrity checks for return addresses. To tackle the limitations of existing defenses, this dissertation introduces the design and implementation of several new countermeasures. First, we present a generic and fine-grained CFI framework for mobile devices targeting ARM-based platforms. This framework preserves static code signatures by instrumenting mobile applications on-the-fly in memory. Second, we tackle the performance and security limitations of existing CFI defenses by introducing hardware-assisted CFI for embedded devices. To this end, we present a CFI-based hardware implementation for Intel Siskiyou Peak using dedicated CFI machine instructions. Lastly, we explore fine-grained code randomization techniques

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

Analyzing & designing the security of shared resources on smartphone operating systems

Smartphone penetration surpassed 80% in the US and nears 70% in Western Europe. In fact, smartphones became the de facto devices users leverage to manage personal information and access external data and other connected devices on a daily basis. To support such multi-faceted functionality, smartphones are designed with a multi-process architecture, which enables third-party developers to build smartphone applications which can utilize smartphone internal and external resources to offer creative utility to users. Unfortunately, such third-party programs can exploit security inefficiencies in smartphone operating systems to gain unauthorized access to available resources, compromising the confidentiality of rich, highly sensitive user data. The smartphone ecosystem, is designed such that users can readily install and replace applications on their smartphones. This facilitates users’ efforts in customizing the capabilities of their smartphones tailored to their needs. Statistics report an increasing number of available smartphone applications— in 2017 there were approximately 3.5 million third-party apps on the official application store of the most popular smartphone platform. In addition we expect users to have approximately 95 such applications installed on their smartphones at any given point. However, mobile apps are developed by untrusted sources. On Android—which enjoys 80% of the smartphone OS market share—application developers are identified based on self-sign certificates. Thus there is no good way of holding a developer accountable for a malicious behavior. This creates an issue of multi-tenancy on smartphones where principals from diverse untrusted sources share internal and external smartphone resources. Smartphone OSs rely on traditional operating system process isolation strategies to confine untrusted third-party applications. However this approach is insufficient because incidental seemingly harmless resources can be utilized by untrusted tenants as side-channels to bypass the process boundaries. Smartphones also introduced a permission model to allow their users to govern third-party application access to system resources (such as camera, microphone and location functionality). However, this permission model is both coarse-grained and does not distinguish whether a permission has been declared by a trusted or an untrusted principal. This allows malicious applications to perform privilege escalation attacks on the mobile platform. To make things worse, applications might include third- party libraries, for advertising or common recognition tasks. Such libraries share the process address space with their host apps and as such can inherit all the privileges the host app does. Identifying and mitigating these problems on smartphones is not a trivial process. Manual analysis on its own of all mobile apps is cumbersome and impractical, code analysis techniques suffer from scalability and coverage issues, ad-hoc approaches are impractical and susceptible to mistakes, while sometimes vulnerabilities are well hidden at the interplays between smartphone tenants and resources. In this work I follow an analytical approach to discover major security and privacy issues on smartphone platforms. I utilize the Android OS as a use case, because of its open-source nature but also its popularity. In particular I focus on the multi-tenancy characteristic of smartphones and identify the re- sources each tenant within a process, across processes and across devices can access. I design analytical tools to automate the discovery process, attacks to better understand the adversary models, and introduce design changes to the participating systems to enable robust fine-grained access control of resources. My approach revealed a new understanding of the threats introduced from third-party libraries within an application process; it revealed new capabilities of the mobile application adversary exploiting shared filesystem and permission resources; and shows how a mobile app adversary can exploit shared communication mediums to compromise the confidentiality of the data collected by external devices (e.g. fitness and medical accessories, NFC tags etc.). Moreover, I show how we can eradicate these problems following an architectural design approach to introduce backward-compatible, effective and efficient modifications in operating systems to achieve fine-grained application access to shared resources. My work has let to security changes in the official release of Android by Google

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available