Frictionless Authentication Systems: Emerging Trends, Research Challenges and Opportunities

Authentication and authorization are critical security layers to protect a wide range of online systems, services and content. However, the increased prevalence of wearable and mobile devices, the expectations of a frictionless experience and the diverse user environments will challenge the way users are authenticated. Consumers demand secure and privacy-aware access from any device, whenever and wherever they are, without any obstacles. This paper reviews emerging trends and challenges with frictionless authentication systems and identifies opportunities for further research related to the enrollment of users, the usability of authentication schemes, as well as security and privacy trade-offs of mobile and wearable continuous authentication systems.Comment: published at the 11th International Conference on Emerging Security Information, Systems and Technologies (SECURWARE 2017

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction

A survey of app store analysis for software engineering

App Store Analysis studies information about applications obtained from app stores. App stores provide a wealth of information derived from users that would not exist had the applications been distributed via previous software deployment methods. App Store Analysis combines this non-technical information with technical information to learn trends and behaviours within these forms of software repositories. Findings from App Store Analysis have a direct and actionable impact on the software teams that develop software for app stores, and have led to techniques for requirements engineering, release planning, software design, security and testing. This survey describes and compares the areas of research that have been explored thus far, drawing out common aspects, trends and directions future research should take to address open problems and challenges

DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees

This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements

Defense in Depth of Resource-Constrained Devices

The emergent next generation of computing, the so-called Internet of Things (IoT), presents significant challenges to security, privacy, and trust. The devices commonly used in IoT scenarios are often resource-constrained with reduced computational strength, limited power consumption, and stringent availability requirements. Additionally, at least in the consumer arena, time-to-market is often prioritized at the expense of quality assurance and security. An initial lack of standards has compounded the problems arising from this rapid development. However, the explosive growth in the number and types of IoT devices has now created a multitude of competing standards and technology silos resulting in a highly fragmented threat model. Tens of billions of these devices have been deployed in consumers\u27 homes and industrial settings. From smart toasters and personal health monitors to industrial controls in energy delivery networks, these devices wield significant influence on our daily lives. They are privy to highly sensitive, often personal data and responsible for real-world, security-critical, physical processes. As such, these internet-connected things are highly valuable and vulnerable targets for exploitation. Current security measures, such as reactionary policies and ad hoc patching, are not adequate at this scale. This thesis presents a multi-layered, defense in depth, approach to preventing and mitigating a myriad of vulnerabilities associated with the above challenges. To secure the pre-boot environment, we demonstrate a hardware-based secure boot process for devices lacking secure memory. We introduce a novel implementation of remote attestation backed by blockchain technologies to address hardware and software integrity concerns for the long-running, unsupervised, and rarely patched systems found in industrial IoT settings. Moving into the software layer, we present a unique method of intraprocess memory isolation as a barrier to several prevalent classes of software vulnerabilities. Finally, we exhibit work on network analysis and intrusion detection for the low-power, low-latency, and low-bandwidth wireless networks common to IoT applications. By targeting these areas of the hardware-software stack, we seek to establish a trustworthy system that extends from power-on through application runtime

Measuring Alexa skill privacy practices across three years

Smart Voice Assistants are transforming the way users interact with technology. This transformation is mostly fostered by the proliferation of voice-driven applications (called skills) offered by third-party developers through an online market. We see how the number of skills has rocked in recent years, with the Amazon Alexa skill ecosystem growing from just 135 skills in early 2016 to about 125k skills in early 2021. Along with the growth in skills, there is increasing concern over the risks that third-party skills pose to users' privacy. In this paper, we perform a systematic and longitudinal measurement study of the Alexa marketplace. We shed light on how this ecosystem evolves using data collected across three years between 2019 and 2021. We demystify developers' data disclosure practices and present an overview of the third-party ecosystem. We see how the research community continuously contribute to the market's sanitation, but the Amazon vetting process still requires significant improvement. We perform a responsible disclosure process reporting 675 skills with privacy issues to both Amazon and all affected developers, out of which 246 skills suffer from important issues (i.e., broken traceability). We see that 107 out of the 246 (43.5%) skills continue to display broken traceability almost one year after being reported. As a result, the overall state of affairs has improved in the ecosystem over the years. Yet, newly submitted skills and unresolved known issues pose an endemic risk