Advanced Security Analysis for Emergent Software Platforms

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems. This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms. Advisers: Hamid Bagheri and Qiben Ya

Dynamic monitoring of Android malware behavior: a DNS-based approach

The increasing technological revolution of the mobile smart devices fosters their wide use. Since mobile users rely on unofficial or thirdparty repositories in order to freely install paid applications, lots of security and privacy issues are generated. Thus, at the same time that Android phones become very popular and growing rapidly their market share, so it is the number of malicious applications targeting them. Yet, current mobile malware detection and analysis technologies are very limited and ineffective. Due to the particular traits of mobile devices such as the power consumption constraints that make unaffordable to run traditional PC detection engines on the device; therefore mobile security faces new challenges, especially on dynamic runtime malware detection. This approach is import because many instructions or infections could happen after an application is installed or executed. On the one hand, recent studies have shown that the network-based analysis, where applications could be also analyzed by observing the network traffic they generate, enabling us to detect malicious activities occurring on the smart device. On the other hand, the aggressors rely on DNS to provide adjustable and resilient communication between compromised client machines and malicious infrastructure. So, having rich DNS traffic information is very important to identify malevolent behavior, then using DNS for malware detection is a logical step in the dynamic analysis because malicious URLs are common and the present danger for cybersecurity. Therefore, the main goal of this thesis is to combine and correlate two approaches: top-down detection by identifying malware domains using DNS traces at the network level, and bottom-up detection at the device level using the dynamic analysis in order to capture the URLs requested on a number of applications to pinpoint the malware. For malware detection and visualization, we propose a system which is based on dynamic analysis of API calls. Thiscan help Android malware analysts in visually inspecting what the application under study does, easily identifying such malicious functions. Moreover, we have also developed a framework that automates the dynamic DNS analysis of Android malware where the captured URLs at the smartphone under scrutiny are sent to a remote server where they are: collected, identified within the DNS server records, mapped the extracted DNS records into this server in order to classify them either as benign or malicious domain. The classification is done through the usage of machine learning. Besides, the malicious URLs found are used in order to track and pinpoint other infected smart devices, not currently under monitoring

Security Management Framework for the Internet of Things

The increase in the design and development of wireless communication technologies offers multiple opportunities for the management and control of cyber-physical systems with connections between smart and autonomous devices, which provide the delivery of simplified data through the use of cloud computing. Given this relationship with the Internet of Things (IoT), it established the concept of pervasive computing that allows any object to communicate with services, sensors, people, and objects without human intervention. However, the rapid growth of connectivity with smart applications through autonomous systems connected to the internet has allowed the exposure of numerous vulnerabilities in IoT systems by malicious users. This dissertation developed a novel ontology-based cybersecurity framework to improve security in IoT systems using an ontological analysis to adapt appropriate security services addressed to threats. The composition of this proposal explores two approaches: (1) design time, which offers a dynamic method to build security services through the application of a methodology directed to models considering existing business processes; and (2) execution time, which involves monitoring the IoT environment, classifying vulnerabilities and threats, and acting in the environment, ensuring the correct adaptation of existing services. The validation approach was used to demonstrate the feasibility of implementing the proposed cybersecurity framework. It implies the evaluation of the ontology to offer a qualitative evaluation based on the analysis of several criteria and also a proof of concept implemented and tested using specific industrial scenarios. This dissertation has been verified by adopting a methodology that follows the acceptance in the research community through technical validation in the application of the concept in an industrial setting.O aumento no projeto e desenvolvimento de tecnologias de comunicação sem fio oferece múltiplas oportunidades para a gestão e controle de sistemas ciber-físicos com conexões entre dispositivos inteligentes e autônomos, os quais proporcionam a entrega de dados simplificados através do uso da computação em nuvem. Diante dessa relação com a Internet das Coisas (IoT) estabeleceu-se o conceito de computação pervasiva que permite que qualquer objeto possa comunicar com os serviços, sensores, pessoas e objetos sem intervenção humana. Entretanto, o rápido crescimento da conectividade com as aplicações inteligentes através de sistemas autônomos conectados com a internet permitiu a exposição de inúmeras vulnerabilidades dos sistemas IoT para usuários maliciosos. Esta dissertação desenvolveu um novo framework de cibersegurança baseada em ontologia para melhorar a segurança em sistemas IoT usando uma análise ontológica para a adaptação de serviços de segurança apropriados endereçados para as ameaças. A composição dessa proposta explora duas abordagens: (1) tempo de projeto, o qual oferece um método dinâmico para construir serviços de segurança através da aplicação de uma metodologia dirigida a modelos, considerando processos empresariais existentes; e (2) tempo de execução, o qual envolve o monitoramento do ambiente IoT, a classificação de vulnerabilidades e ameaças, e a atuação no ambiente garantindo a correta adaptação dos serviços existentes. Duas abordagens de validação foram utilizadas para demonstrar a viabilidade da implementação do framework de cibersegurança proposto. Isto implica na avaliação da ontologia para oferecer uma avaliação qualitativa baseada na análise de diversos critérios e também uma prova de conceito implementada e testada usando cenários específicos. Esta dissertação foi validada adotando uma metodologia que segue a validação na comunidade científica através da validação técnica na aplicação do nosso conceito em um cenário industrial

Malware Analysis and Detection on Android: The Big Challenge

The popularization of the use of mobile devices, such as smartphones and tablets, has accelerated in recent years, as these devices have experienced a reduction in cost together with an increase in functionality and services availability. In this context, due to its openness and free availability, Android operating system (OS) has become not only a major stakeholder in the market of mobile devices but has also become an attractive target for cybercriminals. In this chapter, we advocate to present some current trends and results in the Android malware analysis and detection research area. We start by briefly describing the Android’s security model, followed by a discussion of the static and dynamic malware analysis techniques in order to provide a general view of the analysis and detection process to the reader. After that, a description of a particular set of software developments, which exemplify some of the discussed techniques, is presented accompanied by a set of practical results. Finally, we draw some conclusions about the future development of the Android malware analysis area. The main contribution of this chapter is a description of the realization of static and dynamic malware analysis techniques and principles that can be automated and mapped to software system tools in order to simplify analyses. Moreover, some details about the use of machine learning algorithms for malware classifications and the use of the hooking software techniques for dynamic analysis execution are provided

A Paradigm for Safe Adaptation of Collaborating Robots

The dynamic forces that transit back and forth traditional boundaries of system development have led to the emergence of digital ecosystems. Within these, business gains are achieved through the development of intelligent control that requires a continuous design and runtime co-engineering process endangered by malicious attacks. The possibility of inserting specially crafted faults capable to exploit the nature of unknown evolving intelligent behavior raises the necessity of malicious behavior detection at runtime.Adjusting to the needs and opportunities of fast AI development within digital ecosystems, in this paper, we envision a novel method and framework for runtime predictive evaluation of intelligent robots' behavior for assuring a cooperative safe adjustment

On the Security and Privacy Challenges in Android-based Environments

In the last decade, we have faced the rise of mobile devices as a fundamental tool in our everyday life. Currently, there are above 6 billion smartphones, and 72% of them are Android devices. The functionalities of smartphones are enriched by mobile apps through which users can perform operations that in the past have been made possible only on desktop/laptop computing. Besides, users heavily rely on them for storing even the most sensitive information from a privacy point of view. However, apps often do not satisfy all minimum security requirements and can be targeted to indirectly attack other devices managed or connected to them (e.g., IoT nodes) that may perform sensitive operations such as health checks, control a smart car or open a smart lock. This thesis discusses some research activities carried out to enhance the security and privacy of mobile apps by i) proposing novel techniques to detect and mitigate security vulnerabilities and privacy issues, and ii) defining techniques devoted to the security evaluation of apps interacting with complex environments (e.g., mobile-IoT-Cloud). In the first part of this thesis, I focused on the security and privacy of Mobile Apps. Due to the widespread adoption of mobile apps, it is relatively straightforward for researchers or users to quickly retrieve the app that matches their tastes, as Google provides a reliable search engine. However, it is likewise almost impossible to select apps according to a security footprint (e.g., all apps that enforce SSL pinning). To overcome this limitation, I present APPregator, a platform that allows users to select apps according to a specific security footprint. This tool aims to implement state-of-the-art static and dynamic analysis techniques for mobile apps and provide security researchers and analysts with a tool that makes it possible to search for mobile applications under specific functional or security requirements. Regarding the security status of apps, I studied a particular context of mobile apps: hybrid apps composed of web technologies and native technologies (i.e., Java or Kotlin). In this context, I studied a vulnerability that affected only hybrid apps: the Frame Confusion. This vulnerability, despite being discovered several years ago, it is still very widespread. I proposed a methodology implemented in FCDroid that exploits static and dynamic analysis techniques to detect and trigger the vulnerability automatically. The results of an extensive analysis carried out through FCDroid on a set of the most downloaded apps from the Google Play Store prove that 6.63% (i.e., 1637/24675) of hybrid apps are potentially vulnerable to Frame Confusion. A side effect of the analysis I carried out through APPregator was suggesting that very few apps may have a privacy policy, despite Google Play Store imposes some strict rules about it and contained in the Google Play Privacy Guidelines. To empirically verify if that was the case, I proposed a methodology based on the combination of static analysis, dynamic analysis, and machine learning techniques. The proposed methodology verifies whether each app contains a privacy policy compliant with the Google Play Privacy Guidelines, and if the app accesses privacy-sensitive information only upon the acceptance of the policy by the user. I then implemented the methodology in a tool, 3PDroid, and evaluated a number of recent and most downloaded Android apps in the Google Play Store. Experimental results suggest that over 95% of apps access sensitive user privacy information, but only a negligible subset of it (~ 1%) fully complies with the Google Play Privacy Guidelines. Furthermore, the obtained results have also suggested that the user privacy could be put at risk by mobile apps that keep collecting a plethora of information regarding the user's and the device behavior by relying on third-party analytics libraries. However, collecting and using such data raised several privacy concerns, mainly because the end-user - i.e., the actual data owner - is out of the loop in this collection process. The existing privacy-enhanced solutions that emerged in the last years follow an ``all or nothing" approach, leaving to the user the sole option to accept or completely deny access to privacy-related data. To overcome the current state-of-the-art limitations, I proposed a data anonymization methodology, called MobHide, that provides a compromise between the usefulness and privacy of the data collected and gives the user complete control over the sharing process. For evaluating the methodology, I implemented it in a prototype called HideDroid and tested it on 4500 most-used Android apps of the Google Play Store between November 2020 and January 2021. In the second part of this thesis, I extended privacy and security considerations outside the boundary of the single mobile device. In particular, I focused on two scenarios. The first is composed of an IoT device and a mobile app that have a fruitful integration to resolve and perform specific actions. From a security standpoint, this leads to a novel and unprecedented attack surface. To deal with such threats, applying state-of-the-art security analysis techniques on each paradigm can be insufficient. I claimed that novel analysis methodologies able to systematically analyze the ecosystem as a whole must be put forward. To this aim, I introduced the idea of APPIoTTe, a novel approach to the security testing of Mobile-IoT hybrid ecosystems, as well as some notes on its implementation working on Android (Mobile) and Android Things (IoT) applications. The second scenario is composed of an IoT device widespread in the Smart Home environment: the Smart Speaker. Smart speakers are used to retrieving information, interacting with other devices, and commanding various IoT nodes. To this aim, smart speakers typically take advantage of cloud architectures: vocal commands of the user are sampled, sent through the Internet to be processed, and transmitted back for local execution, e.g., to activate an IoT device. Unfortunately, even if privacy and security are enforced through state-of-the-art encryption mechanisms, the features of the encrypted traffic, such as the throughput, the size of protocol data units, or the IP addresses, can leak critical information about the users' habits. In this perspective, I showcase this kind of risk by exploiting machine learning techniques to develop black-box models to classify traffic and implement privacy leaking attacks automatically

ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks

IoT application domains, device diversity and connectivity are rapidly growing. IoT devices control various functions in smart homes and buildings, smart cities, and smart factories, making these devices an attractive target for attackers. On the other hand, the large variability of different application scenarios and inherent heterogeneity of devices make it very challenging to reliably detect abnormal IoT device behaviors and distinguish these from benign behaviors. Existing approaches for detecting attacks are mostly limited to attacks directly compromising individual IoT devices, or, require predefined detection policies. They cannot detect attacks that utilize the control plane of the IoT system to trigger actions in an unintended/malicious context, e.g., opening a smart lock while the smart home residents are absent. In this paper, we tackle this problem and propose ARGUS, the first self-learning intrusion detection system for detecting contextual attacks on IoT environments, in which the attacker maliciously invokes IoT device actions to reach its goals. ARGUS monitors the contextual setting based on the state and actions of IoT devices in the environment. An unsupervised Deep Neural Network (DNN) is used for modeling the typical contextual device behavior and detecting actions taking place in abnormal contextual settings. This unsupervised approach ensures that ARGUS is not restricted to detecting previously known attacks but is also able to detect new attacks. We evaluated ARGUS on heterogeneous real-world smart-home settings and achieve at least an F1-Score of 99.64% for each setup, with a false positive rate (FPR) of at most 0.03%.Comment: To appear in the 32nd USENIX Security Symposium, August 2022, Anaheim CA, US

Future Vision of Dynamic Certification Schemes for Autonomous Systems

As software becomes increasingly pervasive in critical domains like autonomous driving, new challenges arise, necessitating rethinking of system engineering approaches. The gradual takeover of all critical driving functions by autonomous driving adds to the complexity of certifying these systems. Namely, certification procedures do not fully keep pace with the dynamism and unpredictability of future autonomous systems, and they may not fully guarantee compliance with the requirements imposed on these systems. In this paper, we have identified several issues with the current certification strategies that could pose serious safety risks. As an example, we highlight the inadequate reflection of software changes in constantly evolving systems and the lack of support for systems' cooperation necessary for managing coordinated movements. Other shortcomings include the narrow focus of awarded certification, neglecting aspects such as the ethical behavior of autonomous software systems. The contribution of this paper is threefold. First, we analyze the existing international standards used in certification processes in relation to the requirements derived from dynamic software ecosystems and autonomous systems themselves, and identify their shortcomings. Second, we outline six suggestions for rethinking certification to foster comprehensive solutions to the identified problems. Third, a conceptual Multi-Layer Trust Governance Framework is introduced to establish a robust governance structure for autonomous ecosystems and associated processes, including envisioned future certification schemes. The framework comprises three layers, which together support safe and ethical operation of autonomous systems

Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs