Analyzing Android Adware

Most Android smartphone apps are free; in order to generate revenue, the app developers embed ad libraries so that advertisements are displayed when the app is being used. Billions of dollars are lost annually due to ad fraud. In this research, we propose a machine learning based scheme to detect Android adware based on static and dynamic features. We collect static features from the manifest file, while dynamic features are obtained from network traffic. Using these features, we initially classify Android applications into broad categories (e.g., adware and benign) and then further classify each application into a more specific family. We employ a variety of machine learning techniques including neural networks, random forests, adaboost and support vector machines

A Cybersecurity review of Healthcare Industry

Antecedentes La ciberseguridad no es un concepto nuevo de nuestros días. Desde los años 60 la ciberseguridad ha sido un ámbito de discusión e investigación. Aunque los mecanismos de defensa en materia de seguridad han evolucionado, las capacidades del atacante también se han incrementado de igual o mayor manera. Prueba de este hecho es la precaria situación en materia de ciberseguridad de muchas empresas, que ha llevado a un incremento de ataques de ransomware y el establecimiento de grandes organizaciones criminales dedicadas al cibercrimen. Esta situación, evidencia la necesidad de avances e inversión en ciberseguridad en multitud de sectores, siendo especialmente relevante en la protección de infraestructuras críticas. Se conoce como infraestructuras críticas aquellas infraestructuras estratégicas cuyo funcionamiento es indispensable y no permite soluciones alternativas, por lo que su perturbación o destrucción tendría un grave impacto sobre los servicios esenciales. Dentro de esta categorización se encuentran los servicios e infraestructuras sanitarias. Estas infraestructuras ofrecen un servicio, cuya interrupción conlleva graves consecuencias, como la pérdida de vidas humanas. Un ciberataque puede afectar a estos servicios sanitarios, llevando a su paralización total o parcial, como se ha visto en recientes incidentes, llevando incluso a la pérdida de vidas humanas. Además, este tipo de servicios contienen multitud de información personal de carácter altamente sensible. Los datos médicos son un tipo de datos con alto valor en mercados ilegales, y por tanto objetivos de ataques centrados en su robo. Por otra parte, se debe mencionar, que al igual que otros sectores, actualmente los servicios sanitarios se encuentran en un proceso de digitalización. Esta evolución, ha obviado la ciberseguridad en la mayoría de sus desarrollos, contribuyendo al crecimiento y gravedad de los ataques previamente mencionados. - Metodología e investigación El trabajo presentado en esta tesis sigue claramente un método experimental y deductivo. Está investigación se ha centrado en evaluar el estado de la ciberseguridad en infraestructuras sanitarias y proponer mejoras y mecanismos de detección de ciberataques. Las tres publicaciones científicas incluidas en esta tesis buscan dar soluciones y evaluar problemas actuales en el ámbito de las infraestructuras y sistemas sanitarios. La primera publicación, 'Mobile malware detection using machine learning techniques', se centró en desarrollar nuevas técnicas de detección de amenazas basadas en el uso de tecnologías de inteligencia artificial y ‘machine learning’. Esta investigación fue capaz de desarrollar un método de detección de aplicaciones potencialmente no deseadas y maliciosas en entornos móviles de tipo Android. Además, tanto en el diseño y creación se tuvo en cuenta las necesidades específicas de los entornos sanitarios. Buscando ofrecer una implantación sencilla y viable de acorde las necesidades de estos centros, obteniéndose resultados satisfactorios. La segunda publicación, 'Interconnection Between Darknets', buscaba identificar y detectar robos y venta de datos médicos en darknets. El desarrollo de esta investigación conllevó el descubrimiento y prueba de la interconexión entre distintas darknets. La búsqueda y el análisis de información en este tipo de redes permitió demostrar como distintas redes comparten información y referencias entre ellas. El análisis de una darknet implica la necesidad de analizar otras, para obtener una información más completa de la primera. Finalmente, la última publicación, 'Security and privacy issues of data-over-sound technologies used in IoT healthcare devices' buscó investigar y evaluar la seguridad de dispositivos médicos IoT ('Internet of Things'). Para desarrollar esta investigación se adquirió un dispositivo médico, un electrocardiógrafo portable, actualmente en uso por diversos hospitales. Las pruebas realizadas sobre este dispositivo fueron capaces de descubrir múltiples fallos de ciberseguridad. Estos descubrimientos evidenciaron la carencia de certificaciones y revisiones obligatorias en materia ciberseguridad en productos sanitarios, comercializados actualmente. Desgraciadamente la falta de presupuesto dedicado a investigación no permitió la adquisición de varios dispositivos médicos, para su posterior evaluación en ciberseguridad. - Conclusiones La realización de los trabajos e investigaciones previamente mencionadas permitió obtener las siguientes conclusiones. Partiendo de la necesidad en mecanismos de ciberseguridad de las infraestructuras sanitarias, se debe tener en cuenta su particularidad diseño y funcionamiento. Las pruebas y mecanismos de ciberseguridad diseñados han de ser aplicables en entornos reales. Desgraciadamente actualmente en las infraestructuras sanitarias hay sistemas tecnológicos imposibles de actualizar o modificar. Multitud de máquinas de tratamiento y diagnostico cuentan con software y sistemas operativos propietarios a los cuales los administradores y empleados no tienen acceso. Teniendo en cuenta esta situación, se deben desarrollar medidas que permitan su aplicación en este ecosistema y que en la medida de los posible puedan reducir y paliar el riesgo ofrecido por estos sistemas. Esta conclusión viene ligada a la falta de seguridad en dispositivos médicos. La mayoría de los dispositivos médicos no han seguido un proceso de diseño seguro y no han sido sometidos a pruebas de seguridad por parte de los fabricantes, al suponer esto un coste directo en el desarrollo del producto. La única solución en este aspecto es la aplicación de una legislación que fuerce a los fabricantes a cumplir estándares de seguridad. Y aunque actualmente se ha avanzado en este aspecto regulatorio, se tardaran años o décadas en sustituir los dispositivos inseguros. La imposibilidad de actualizar, o fallos relacionados con el hardware de los productos, hacen imposible la solución de todos los fallos de seguridad que se descubran. Abocando al reemplazo del dispositivo, cuando exista una alternativa satisfactoria en materia de ciberseguridad. Por esta razón es necesario diseñar nuevos mecanismos de ciberseguridad que puedan ser aplicados actualmente y puedan mitigar estos riesgos en este periodo de transición. Finalmente, en materia de robo de datos. Aunque las investigaciones preliminares realizadas en esta tesis no consiguieron realizar ningún descubrimiento significativo en el robo y venta de datos. Actualmente las darknets, en concreto la red Tor, se han convertido un punto clave en el modelo de Ransomware as a Business (RaaB), al ofrecer sitios webs de extorsión y contacto con estos grupos

SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

© 2013 IEEE. For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer's attention. Android allows downloading and installation of apps from other unofficial market places. This gives malware developers an opportunity to put repackaged malicious applications in third-party app-stores and attack the Android devices. A large number of malware analysis and detection systems have been developed which uses static analysis, dynamic analysis, or hybrid analysis to keep Android devices secure from malware. However, the existing research clearly lags in detecting malware efficiently and accurately. For accurate malware detection, multilayer analysis is required which consumes large amount of hardware resources of resource constrained mobile devices. This research proposes an efficient and accurate solution to this problem, named SAMADroid, which is a novel 3-level hybrid malware detection model for Android operating systems. The research contribution includes multiple folds. First, many of the existing Android malware detection techniques are thoroughly investigated and categorized on the basis of their detection methods. Also, their benefits along with limitations are deduced. A novel 3-level hybrid malware detection model for Android operating systems is developed, that can provide high detection accuracy by combining the benefits of the three different levels: 1) Static and Dynamic Analysis; 2) Local and Remote Host; and 3) Machine Learning Intelligence. Experimental results show that SAMADroid achieves high malware detection accuracy by ensuring the efficiency in terms of power and storage consumption

Rohelisema tarkvaratehnoloogia poole tarkvaraanalüüsi abil

Mobiilirakendused, mis ei tühjenda akut, saavad tavaliselt head kasutajahinnangud. Mobiilirakenduste energiatõhusaks muutmiseks on avaldatud mitmeid refaktoreerimis- suuniseid ja tööriistu, mis aitavad rakenduse koodi optimeerida. Neid suuniseid ei saa aga seoses energiatõhususega üldistada, sest kõigi kontekstide kohta ei ole piisavalt energiaga seotud andmeid. Olemasolevad energiatõhususe parandamise tööriistad/profiilid on enamasti prototüübid, mis kohalduvad ainult väikese alamhulga energiaga seotud probleemide suhtes. Lisaks käsitlevad olemasolevad suunised ja tööriistad energiaprobleeme peamiselt a posteriori ehk tagantjärele, kui need on juba lähtekoodi sees. Android rakenduse koodi saab põhijoontes jagada kaheks osaks: kohandatud kood ja korduvkasutatav kood. Kohandatud kood on igal rakendusel ainulaadne. Korduvkasutatav kood hõlmab kolmandate poolte teeke, mis on rakendustesse lisatud arendusprotessi kiirendamiseks. Alustuseks hindame mitmete lähtekoodi halbade lõhnade refaktoreerimiste energiatarbimist Androidi rakendustes. Seejärel teeme empiirilise uuringu Androidi rakendustes kasutatavate kolmandate osapoolte võrguteekide energiamõju kohta. Pakume üldisi kontekstilisi suuniseid, mida võiks rakenduste arendamisel kasutada. Lisaks teeme süstemaatilise kirjanduse ülevaate, et teha kindlaks ja uurida nüüdisaegseid tugitööriistu, mis on rohelise Androidi arendamiseks saadaval. Selle uuringu ja varem läbi viidud katsete põhjal toome esile riistvarapõhiste energiamõõtmiste jäädvustamise ja taasesitamise probleemid. Arendame tugitööriista ARENA, mis võib aidata koguda energiaandmeid ja analüüsida Androidi rakenduste energiatarbimist. Viimasena töötame välja tugitööriista REHAB, et soovitada arendajatele energiatõhusaid kolmanda osapoole võrguteekeMobile apps that do not drain the battery usually get good user ratings. To make mobile apps energy efficient many refactoring guidelines and tools are published that help optimize the app code. However, these guidelines cannot be generalized w.r.t energy efficiency, as there is not enough energy-related data for every context. Existing energy enhancement tools/profilers are mostly prototypes applicable to only a small subset of energy-related problems. In addition, the existing guidelines and tools mostly address the energy issues a posteriori, i.e., once they have already been introduced into the code. Android app code can be roughly divided into two parts: the custom code and the reusable code. Custom code is unique to each app. Reusable code includes third-party libraries that are included in apps to speed up the development process. We start by evaluating the energy consumption of various code smell refactorings in native Android apps. Then we conduct an empirical study on the energy impact of third-party network libraries used in Android apps. We provide generalized contextual guidelines that could be used during app development Further, we conduct a systematic literature review to identify and study the current state of the art support tools available to aid green Android development. Based on this study and the experiments we conducted before, we highlight the problems in capturing and reproducing hardware-based energy measurements. We develop the support tool ‘ARENA’ that could help gather energy data and analyze the energy consumption of Android apps. Last, we develop the support tool ‘REHAB’ to recommend energy efficient third-party network libraries to developers.https://www.ester.ee/record=b547174

Op2Vec: An Opcode Embedding Technique and Dataset Design for End-to-End Detection of Android Malware

Android is one of the leading operating systems for smart phones in terms of market share and usage. Unfortunately, it is also an appealing target for attackers to compromise its security through malicious applications. To tackle this issue, domain experts and researchers are trying different techniques to stop such attacks. All the attempts of securing Android platform are somewhat successful. However, existing detection techniques have severe shortcomings, including the cumbersome process of feature engineering. Designing representative features require expert domain knowledge. There is a need for minimizing human experts' intervention by circumventing handcrafted feature engineering. Deep learning could be exploited by extracting deep features automatically. Previous work has shown that operational codes (opcodes) of executables provide key information to be used with deep learning models for detection process of malicious applications. The only challenge is to feed opcodes information to deep learning models. Existing techniques use one-hot encoding to tackle the challenge. However, the one-hot encoding scheme has severe limitations. In this paper, we introduce; (1) a novel technique for opcodes embedding, which we name Op2Vec, (2) based on the learned Op2Vec we have developed a dataset for end-to-end detection of android malware. Introducing the end-to-end Android malware detection technique avoids expert-intensive handcrafted features extraction, and ensures automation. Some of the recent deep learning-based techniques showed significantly improved results when tested with the proposed approach and achieved an average detection accuracy of 97.47%, precision of 0.976 and F1 score of 0.979

An Android Security Policy Enforcement Tool

The Android operating system (OS) has become the dominant smart phone OS in recent years due to its accessibility, usability and its open-source philosophy. Consequently, this has also made it a popular target for attackers who aim to install malware on Android devices and take advantage of Android’s coarse-grained, non-revoking permission system. This project designs, implements and evaluates a security tool named COMBdroid, which addresses these security concerns in Android by enforcing fine-grained, user-defined policies. COMBdroid modifies an application before installation, allowing it to override points of security vulnerabilities at run-time. As a proof of concept we have implemented three policies in COMBdroid. This paper documents the development process of COMBdroid, deriving design decisions from the literature review, detailing the design and implementation, and proving the program’s effectiveness through evaluation.

AoT - Attack on Things: A security analysis of IoT firmware updates

IoT devices implement firmware update mechanisms to fix security issues and deploy new features. These mechanisms are often triggered and mediated by mobile companion apps running on the users' smartphones. While it is crucial to update devices, these mechanisms may cause critical security flaws if they are not implemented correctly. Given their relevance, in this paper, we perform a systematic security analysis of the firmware update mechanisms adopted by IoT devices via their companion apps. First, we define a threat model for IoT firmware updates, and we categorize the different potential security issues affecting them. Then, we analyze 23 popular IoT devices (and corresponding companion apps) to identify vulnerable devices and the SDKs that such devices use to implement the update functionality. Our analysis reveals that 6 popular SDKs present dangerous security flaws. Additionally, we fingerprint each vulnerable SDK and we leverage our fingerprints to perform a large-scale analysis of companion apps from the Google Play Store. Our results show that 61 popular devices and 1,356 apps rely on vulnerable SDKs, thus, they potentially adopt an insecure firmware update mechanism.</p

Android source code vulnerability detection: a systematic literature review

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not address the security aspects appropriately. This is often due to a lack of automated mechanisms to identify, test, and fix source code vulnerabilities at the early stages of design and development. Therefore, the need to fix such issues at the initial stages rather than providing updates and patches to the published applications is widely recognized. Researchers have proposed several methods to improve the security of applications by detecting source code vulnerabilities and malicious codes. This Systematic Literature Review (SLR) focuses on Android application analysis and source code vulnerability detection methods and tools by critically evaluating 118 carefully selected technical studies published between 2016 and 2022. It highlights the advantages, disadvantages, applicability of the proposed techniques and potential improvements of those studies. Both Machine Learning (ML) based methods and conventional methods related to vulnerability detection are discussed while focusing more on ML-based methods since many recent studies conducted experiments with ML. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in secure mobile application development while minimizing the vulnerabilities by applying ML methods. Furthermore, researchers can use the discussions and findings of this SLR to identify potential future research and development directions

Intelligent Android malware family classification using Genetic Algorithms and SVM

As of April 2019, Android was the most popular mobile operating system amongst smartphone users[1]. Its high popularity, combined with the extended use of smartphones for everyday tasks as well as storing or accessing sensitive and personal data, has made Android applications the target of numerous malware attacks over the last few years and in the present. The malware attacks have been perfected to target specific vulnerabilities in the operating system or the user; thus specializing in types of malware and families within each type. The malware is usually distributed in infected applications (or APKs), which contain malicious behaviours that can be found looking into their code (known as static analysis) or analysing the behaviour of the application while running (known as dynamic analysis). This document describes the implementation of an intelligent system that aims to classify a series of malicious APK samples obtained from the free repository ContagioDump. These samples are classified inside the type and family they belong to. To create the classifier system, a Support Vector Machine (SVM) is implemented using Python’s library Scikit Learn. A series of attributes are extracted from the samples of malicious APK by analysing the code of the APKs via static analysis, using Python’s library Androguard, which contains a parser that allows to interact with all the relevant parts of the APK file. The attributes obtained are very high in number, and for that reason a Genetic Algorithm is used to optimize the attributes that the SVM uses in the learning process. The algorithm codifies a subset of attributes from all the attributes extracted in the static analysis, and is evaluated using the accuracy score obtained when training the SVM with said subset. As a result, a subset of attributes and a trained model for the classification are obtained. This model is then tested with a new set of malware samples, belonging to all the families classified in the learning. The present document contains the explanation of the process of designing, creating and testing the system. It is developed as bachelor’s thesis for computer science and engineering degree in Universidad Carlos III de Madrid.Ingeniería en Tecnologías de Telecomunicación (Plan 2010

Not Your Average App: A Large-scale Privacy Analysis of Android Browsers

Peer reviewe