Biblio

With the rapid growth of mobile devices and mobile apps, mobile has surpassed desktop and now has the largest worldwide market share [1]. While such growth brings in more opportunities, it also poses new challenges in security. Among the challenges, user privacy protection has drawn tremendous attention in recent years, especially after the Facebook-Cambridge Analytica data scandal in April 2018 [2].

Mobile two-factor authentication (2FA) has become commonplace along with the popularity of mobile devices. Current mobile 2FA solutions all require some form of user effort which may seriously affect the experience of mobile users, especially senior citizens or those with disability such as visually impaired users. In this paper, we propose Proximity-Proof, a secure and usable mobile 2FA system without involving user interactions. Proximity-Proof automatically transmits a user's 2FA response via inaudible OFDM-modulated acoustic signals to the login browser. We propose a novel technique to extract individual speaker and microphone fingerprints of a mobile device to defend against the powerful man-in-the-middle (MiM) attack. In addition, Proximity-Proof explores two-way acoustic ranging to thwart the co-located attack. To the best of our knowledge, Proximity-Proof is the first mobile 2FA scheme resilient to the MiM and co-located attacks. We empirically analyze that Proximity-Proof is at least as secure as existing mobile 2FA solutions while being highly usable. We also prototype Proximity-Proof and confirm its high security, usability, and efficiency through comprehensive user experiments.

A majority of today's mobile apps integrate web content of various kinds. Unfortunately, the interactions between app code and web content expose new attack vectors: a malicious app can subvert its embedded web content to steal user secrets; on the other hand, malicious web content can use the privileges of its embedding app to exfiltrate sensitive information such as the user's location and contacts. In this paper, we discuss security weaknesses of the interface between app code and web content through attacks, then introduce defenses that can be deployed without modifying the OS. Our defenses feature WIREframe, a service that securely embeds and renders external web content in Android apps, and in turn, prevents attacks between em- bedded web and host apps. WIREframe fully mediates the interface between app code and embedded web content. Un- like the existing web-embedding mechanisms, WIREframe allows both apps and embedded web content to define simple access policies to protect their own resources. These policies recognize fine-grained security principals, such as origins, and control all interactions between apps and the web. We also introduce WIRE (Web Isolation Rewriting Engine), an offline app rewriting tool that allows app users to inject WIREframe protections into existing apps. Our evaluation, based on 7166 popular apps and 20 specially selected apps, shows these techniques work on complex apps and incur acceptable end-to-end performance overhead.

In this paper, we present a security and privacy enhancement (SPE) framework for unmodified mobile operating systems. SPE introduces a new layer between the application and the operating system and does not require a device be jailbroken or utilize a custom operating system. We utilize an existing ontology designed for enforcing security and privacy policies on mobile devices to build a policy that is customizable. Based on this policy, SPE provides enhancements to native controls that currently exist on the platform for privacy and security sensitive components. SPE allows access to these components in a way that allows the framework to ensure the application is truthful in its declared intent and ensure that the user's policy is enforced. In our evaluation we verify the correctness of the framework and the computing impact on the device. Additionally, we discovered security and privacy issues in several open source applications by utilizing the SPE Framework. From our findings, if SPE is adopted by mobile operating systems producers, it would provide consumers and businesses the additional privacy and security controls they demand and allow users to be more aware of security and privacy issues with applications on their devices.

Transport Layer Security (TLS), has become the de-facto standard for secure Internet communication. When used correctly, it provides secure data transfer, but used incorrectly, it can leave users vulnerable to attacks while giving them a false sense of security. Numerous efforts have studied the adoption of TLS (and its predecessor, SSL) and its use in the desktop ecosystem, attacks, and vulnerabilities in both desktop clients and servers. However, there is a dearth of knowledge of how TLS is used in mobile platforms. In this paper we use data collected by Lumen, a mobile measurement platform, to analyze how 7,258 Android apps use TLS in the wild. We analyze and fingerprint handshake messages to characterize the TLS APIs and libraries that apps use, and also evaluate weaknesses. We see that about 84% of apps use default OS APIs for TLS. Many apps use third-party TLS libraries; in some cases they are forced to do so because of restricted Android capabilities. Our analysis shows that both approaches have limitations, and that improving TLS security in mobile is not straightforward. Apps that use their own TLS configurations may have vulnerabilities due to developer inexperience, but apps that use OS defaults are vulnerable to certain attacks if the OS is out of date, even if the apps themselves are up to date. We also study certificate verification, and see low prevalence of security measures such as certificate pinning, even among high-risk apps such as those providing financial services, though we did observe major third-party tracking and advertisement services deploying certificate pinning.

Smart mobile devices are becoming the main vessel of personal privacy information. While they carry valuable information, data erasure is somehow much more vulnerable than was predicted. The security mechanisms provided by the Android system are not flexible enough to thoroughly delete sensitive data. In addition to the weakness among several provided data-erasing and file-deleting mechanisms, we also target the Android OS design flaws in data erasure, and unveil that the design of the Android OS contradicts some secure data-erasure demands. We present the data-erasure flaws in three typical scenarios on mainstream Android devices, such as the data clearing flaw, application uninstallation flaw, and factory reset flaw. Some of these flaws are inherited data-deleting security issues from the Linux kernel, and some are new vulnerabilities in the Android system. Those scenarios reveal the data leak points in Android systems. Moreover, we reveal that the data remanence on the disk is rarely affected by the user’s daily operation, such as file deletion and app installation and uninstallation, by a real-world data deletion latency experiment. After one volunteer used the Android phone for 2 months, the data remanence amount was still considerable. Then, we proposed DataRaider for file recovering from disk fragments. It adopts a file-carving technique and is implemented as an automated sensitive information recovering framework. DataRaider is able to extract private data in a raw disk image without any file system information, and the recovery rate is considerably high in the four test Android phones. We propose some mitigation for data remanence issues, and give the users some suggestions on data protection in Android systems.

The growing popularity of Android applications makes them vulnerable to security threats. There exist several studies that focus on the analysis of the behaviour of Android applications to detect the repackaged and malicious ones. These techniques use a variety of features to model the application's behaviour, among which the calls to Android API, made by the application components, are shown to be the most reliable. To generate the APIs that an application calls is not an easy task. This is because most malicious applications are obfuscated and do not come with the source code. This makes the problem of identifying the API methods invoked by an application an interesting research issue. In this paper, we present HyDroid, a hybrid approach that combines static and dynamic analysis to generate API call traces from the execution of an application's services. We focus on services because they contain key characteristics that allure attackers to misuse them. We show that HyDroid can be used to extract API call trace signatures of several malware families.

Mobile apps are widely adopted in daily life, and contain increasing security flaws. Many regulatory agencies and organizations have announced security guidelines for app development. However, most security guidelines involving technicality and compliance with this requirement is not easily feasible. Thus, we propose Mobile Apps Assessment and Analysis System (MAS), an automatic security validation system to improve guideline compliance. MAS combines static and dynamic analysis techniques, which can be used to verify whether android apps meet the security guideline requirements. We implemented MAS in practice and verified 143 real-world apps produced by the Taiwan government. Besides, we also validated 15,000 popular apps collected from Google Play Store produced in three countries. We found that most apps contain at least three security issues. Finally, we summarize the results and list the most common security flaws for consideration in further app development.

This poster documents three approaches that we are undertaking to increase security awareness within undergraduate computer science classes. The first approach is a verbal password entry system, with surreptitious photos being taken when the mobile device is stolen. The second approach is a lab where students develop a password entry and verification system between a mobile device and a remote server. The third approach is a captcha system, where students implement a simple challenge that can be verified. Like password entry, the captcha communications must be secure and difficult to automatically manipulate. Unlike password entry, the captcha is meant to allow humans access while denying other computers.

Android is the most commonly used mobile device operation system. The core of Android, the System Server (SS), is a multi-threaded process that provides most of the system services. Based on a new understanding of the security risks introduced by the callback mechanism in system services, we have discovered a general type of design flaw. A vulnerability detection tool has been designed and implemented based on static taint analysis. We applied the tool on all the 80 system services in the SS of Android 5.1.0. With its help, we have discovered six previously unknown vulnerabilities, which are further confirmed on Android 2.3.7-6.0.1. According to our analysis, about 97.3% of the entire 1.4 billion real-world Android devices are vulnerable. Our proof-of-concept attack proves that the vulnerabilities can enable a malicious app to freeze critical system functionalities or soft-reboot the system immediately. It is a neat type of denial-of-service at-tack. We also proved that the attacks can be conducted at mission critical moments to achieve meaningful goals, such as anti anti-virus, anti process-killer, hindering app updates or system patching. After being informed, Google confirmed our findings promptly. Several suggestions on how to use callbacks safely are also proposed to Google.

Malicious Android applications pose serious threats to mobile security. They threaten the data confidentiality and system integrity on Android devices. Monitoring runtime activities serves as an important technique for analyzing dynamic app behaviors. We design a triggering relation model for dynamically analyzing network traffic on Android devices. Our model enables one to infer the dependency of outbound network requests from the device. We describe a new machine learning approach for discovering the dependency of network requests. These request-level dependence relations are used to detect stealthy malware activities. Malicious requests are identified due to the lack of dependency with legitimate triggers. Our prototype is evaluated on 14GB network traffic data and system logs collected from an Android tablet. Experimental results show that our solution achieves a high accuracy (99.1%) in detecting malicious requests sent from new malicious apps.

In mobile platforms and their app markets, controlling app permissions and preventing abuse of private information are crucial challenges. Information Flow Control (IFC) is a powerful approach for formalizing and answering user concerns such as: "Does this app send my geolocation to the Internet?" Yet despite intensive research efforts, IFC has not been widely adopted in mainstream programming practice. Abstract We observe that the typical structure of Android apps offers an opportunity for a novel and effective application of IFC. In Android, an app consists of a collection of a few dozen "components", each in charge of some high-level functionality. Most components do not require access to most resources. These components are a natural and effective granularity at which to apply IFC (as opposed to the typical process-level or language-level granularity). By assigning different permission labels to each component, and limiting information flow between components, it is possible to express and enforce IFC constraints. Yet nuances of the Android platform, such as its multitude of discretionary (and somewhat arcane) communication channels, raise challenges in defining and enforcing component boundaries. Abstract We build a system, DroidDisintegrator, which demonstrates the viability of component-level IFC for expressing and controlling app behavior. DroidDisintegrator uses dynamic analysis to generate IFC policies for Android apps, repackages apps to embed these policies, and enforces the policies at runtime. We evaluate DroidDisintegrator on dozens of apps.

In recent years, simple password-based authentication systems have increasingly proven ineffective for many classes of real-world devices. As a result, many researchers have concentrated their efforts on the design of new biometric authentication systems. This trend has been further accelerated by the advent of mobile devices, which offer numerous sensors and capabilities to implement a variety of mobile biometric authentication systems. Along with the advances in biometric authentication, however, attacks have also become much more sophisticated and many biometric techniques have ultimately proven inadequate in face of advanced attackers in practice. In this paper, we investigate the effectiveness of sensor-enhanced keystroke dynamics, a recent mobile biometric authentication mechanism that combines a particularly rich set of features. In our analysis, we consider different types of attacks, with a focus on advanced attacks that draw from general population statistics. Such attacks have already been proven effective in drastically reducing the accuracy of many state-of-the-art biometric authentication systems. We implemented a statistical attack against sensor-enhanced keystroke dynamics and evaluated its impact on detection accuracy. On one hand, our results show that sensor-enhanced keystroke dynamics are generally robust against statistical attacks with a marginal equal-error rate impact (textless0.14%). On the other hand, our results show that, surprisingly, keystroke timing features non-trivially weaken the security guarantees provided by sensor features alone. Our findings suggest that sensor dynamics may be a stronger biometric authentication mechanism against recently proposed practical attacks.

The browsing behavior of a user allows to infer personal details, such as health status, political interests, sexual orientation, etc. In order to protect this sensitive information and to cope with possible privacy threats, defense mechanisms like SSH tunnels and anonymity networks (e.g., Tor) have been established. A known shortcoming of these defenses is that website fingerprinting attacks allow to infer a user's browsing behavior based on traffic analysis techniques. However, website fingerprinting typically assumes access to the client's network or to a router near the client, which restricts the applicability of these attacks. In this work, we show that this rather strong assumption is not required for website fingerprinting attacks. Our client-side attack overcomes several limitations and assumptions of network-based fingerprinting attacks, e.g., network conditions and traffic noise, disabled browser caches, expensive training phases, etc. Thereby, we eliminate assumptions used for academic purposes and present a practical attack that can be implemented easily and deployed on a large scale. Eventually, we show that an unprivileged application can infer the browsing behavior by exploiting the unprotected access to the Android data-usage statistics. More specifically, we are able to infer 97% of 2,500 page visits out of a set of 500 monitored pages correctly. Even if the traffic is routed through Tor by using the Orbot proxy in combination with the Orweb browser, we can infer 95% of 500 page visits out of a set of 100 monitored pages correctly. Thus, the READ\_HISTORY\_BOOKMARKS permission, which is supposed to protect the browsing behavior, does not provide protection.

In this paper, we consider side-channel mechanisms, specifically using smart device ambient light sensors, to capture information about user computing activity. We distinguish keyboard keystrokes using only the ambient light sensor readings from a smart watch worn on the user's non-dominant hand. Additionally, we investigate the feasibility of capturing screen emanations for determining user browser usage patterns. The experimental results expose privacy and security risks, as well as the potential for new mobile user interfaces and applications.

We present new applications for cryptographic secret handshakes between mobile devices on top of Bluetooth Low-Energy (LE). Secret handshakes enable mutual authentication, with the property that the parties learn nothing about each other unless they have been both issued credentials by a group administrator. This property provides strong privacy guarantees that enable interesting applications. One of them is proximity-based discovery for private communities. We introduce MASHaBLE, a mobile application that enables participants to discover and interact with nearby users if and only if they belong to the same secret community. We use direct peer-to-peer communication over Bluetooth LE, rather than relying on a central server. We discuss the specifics of implementing secret handshakes over Bluetooth LE and present our prototype implementation.

Many mobile services consist of two components: a server providing an API, and an application running on smartphones and communicating with the API. An unresolved problem in this design is that it is difficult for the server to authenticate which app is accessing the API. This causes many security problems. For example, the provider of a private network API has to embed secrets in its official app to ensure that only this app can access the API; however, attackers can uncover the secret by reverse-engineering. As another example, malicious apps may send automatic requests to ad servers to commit ad fraud. In this work, we propose a system that allows network API to authenticate the mobile app that sends each request so that the API can make an informed access control decision. Our system, the Mobile Trusted-Origin Policy, consists of two parts: 1) an app provenance mechanism that annotates outgoing HTTP(S) requests with information about which app generated the network traffic, and 2) a code isolation mechanism that separates code within an app that should have different app provenance signatures into mobile origin. As motivation for our work, we present two previously-unknown families of apps that perform click fraud, and examine how the lack of mobile origin information enables the attacks. Based on our observations, we propose Trusted Cross-Origin Requests to handle point (1), which automatically includes mobile origin information in outgoing HTTP requests. Servers may then decide, based on the mobile origin data, whether to process the request or not. We implement a prototype of our system for Android and evaluate its performance, security, and deployability. We find that our system can achieve our security and utility goals with negligible overhead.

The IPv6 Routing Protocol for Low-Power and Lossy Networks was recently introduced as the new routing standard for the Internet of Things. Although RPL defines basic security modes, it remains vulnerable to topological attacks which facilitate blackholing, interception, and resource exhaustion. We are concerned with analyzing the corresponding threats and protecting future RPL deployments from such attacks. Our contributions are twofold. First, we analyze the state of the art, in particular the protective scheme VeRA and present two new rank order attacks as well as extensions to mitigate them. Second, we derive and evaluate TRAIL, a generic scheme for topology authentication in RPL. TRAIL solely relies on the basic assumptions of RPL that (1) the root node serves as a trust anchor and (2) each node interconnects to the root in a straight hierarchy. Using proper reachability tests, TRAIL scalably and reliably identifies any topological attacker without strong cryptographic efforts.

A recent report has shown that there are more than 5,000 malicious applications created for Android devices each day. This creates a need for researchers to develop effective and efficient malware classification and detection approaches. To address this need, we introduce DroidClassifier: a systematic framework for classifying network traffic generated by mobile malware. Our approach utilizes network traffic analysis to construct multiple models in an automated fashion using a supervised method over a set of labeled malware network traffic (the training dataset). Each model is built by extracting common identifiers from multiple HTTP header fields. Adaptive thresholds are designed to capture the disparate characteristics of different malware families. Clustering is then used to improve the classification efficiency. Finally, we aggregate the multiple models to construct a holistic model to conduct cluster-level malware classification. We then perform a comprehensive evaluation of DroidClassifier by using 706 malware samples as the training set and 657 malware samples and 5,215 benign apps as the testing set. Collectively , these malicious and benign apps generate 17,949 network flows. The results show that DroidClassifier successfully identifies over 90% of different families of malware with more than 90% accuracy with accessible computational cost. Thus, DroidClassifier can facilitate network management in a large network, and enable unobtrusive detection of mobile malware. By focusing on analyzing network behaviors, we expect DroidClassifier to work with reasonable accuracy for other mobile platforms such as iOS and Windows Mobile as well.

Sensitive data such as text messages, contact lists, and personal information are stored on mobile devices. This makes authentication of paramount importance. More security is needed on mobile devices since, after point-of-entry authentication, the user can perform almost all tasks without having to re-authenticate. For this reason, many authentication methods have been suggested to improve the security of mobile devices in a transparent and continuous manner, providing a basis for convenient and secure user re-authentication. This paper presents a comprehensive analysis and literature review on transparent authentication systems for mobile device security. This review indicates a need to investigate when to authenticate the mobile user by focusing on the sensitivity level of the application, and understanding whether a certain application may require a protection or not.

The QR codes have gained wide popularity in mobile marketing and advertising campaigns. However, the hidden security threat on the involved information system might endanger QR codes' success, and this issue has not been adequately addressed. In this paper we propose to examine the life cycle of a redesigned QR code ecosystem to identify the possible security risks. On top of this examination, we further propose standard changes to enhance security through a digital signature mechanism.

Smartwatches, with motion sensors, are becoming a common utility for users. With the increasing popularity of practical wearable computers, and in particular smartwatches, the security risks linked with sensors on board these devices have yet to be fully explored. Recent research literature has demonstrated the capability of using a smartphone's own accelerometer and gyroscope to infer tap locations; this paper expands on this work to demonstrate a method for inferring smartphone PINs through the analysis of smartwatch motion sensors. This study determines the feasibility and accuracy of inferring user keystrokes on a smartphone through a smartwatch worn by the user. Specifically, we show that with malware accessing only the smartwatch's motion sensors, it is possible to recognize user activity and specific numeric keypad entries. In a controlled scenario, we achieve results no less than 41% and up to 92% accurate for PIN prediction within 5 guesses.

Recently, the demand for more robust protection against unauthorized use of mobile devices has been rapidly growing. This paper presents a novel biometric modality Transient Evoked Otoacoustic Emission (TEOAE) for mobile security. Prior works have investigated TEOAE for biometrics in a setting where an individual is to be identified among a pre-enrolled identity gallery. However, this limits the applicability to mobile environment, where attacks in most cases are from imposters unknown to the system before. Therefore, we employ an unsupervised learning approach based on Autoencoder Neural Network to tackle such blind recognition problem. The learning model is trained upon a generic dataset and used to verify an individual in a random population. We also introduce the framework of mobile biometric system considering practical application. Experiments show the merits of the proposed method and system performance is further evaluated by cross-validation with an average EER 2.41% achieved.

As web-server spoofing is increasing, we investigate a novel technology termed ICmetrics, used to identify fraud for given software/hardware programs based on measurable quantities/features. ICmetrics technology is based on extracting features from digital systems' operation that may be integrated together to generate unique identifiers for each of the systems or create unique profiles that describe the systems' actual behavior. This paper looks at the properties of the several behaviors as a potential ICmetrics features to identify android apps, it presents several quality features which meet the ICmetrics requirements and can be used for encryption key generation. Finally, the paper identifies four android apps and verifies the use of ICmetrics by identifying a spoofed app as a different app altogether.

Smart mobile devices such as smartphones and tablets have become an integral part of our society. However, it also becomes a prime target for attackers with malicious intents. There have been a number of efforts on developing innovative courseware to promote cybersecurity education and to improve student learning; however, hands-on labs are not well developed for smart mobile devices and for mobile security topics. In this paper, we propose to design and develop a mobile security labware with smart mobile devices to promote the cybersecurity education. The integration of mobile computing technologies and smart devices into cybersecurity education will connect the education to leading-edge information technologies, motivate and engage students in security learning, fill in the gap with IT industry need, and help faculties build expertise on mobile computing. In addition, the hands-on experience with mobile app development will promote student learning and supply them with a better understanding of security knowledge not only in classical security domains but also in the emerging mobile security areas.