We introduce Pixie, a novel, camera based two factor authentication solution for mobile and wearable devices. A quick and familiar user action of snapping a photo is sufficient for Pixie to simultaneously perform a graphical password authentication and a physical token based authentication, yet it does not require any expensive, uncommon hardware. Pixie establishes trust based on both the knowledge and possession of an arbitrary physical object readily accessible to the user, called trinket. Users choose their trinkets similar to setting a password, and authenticate by presenting the same trinket to the camera. The fact that the object is the trinket, is secret to the user. Pixie extracts robust, novel features from trinket images, and leverages a supervised learning classifier to effectively address inconsistencies between images of the same trinket captured in different circumstances. Pixie achieved a false accept rate below 0.09 in a brute force attack with 14.3 million authentication attempts, generated with 40,000 trinket images that we captured and collected from public datasets. We identify master images, that match multiple trinkets, and study techniques to reduce their impact. In a user study with 42 participants over 8 days in 3 sessions we found that Pixie outperforms text based passwords on memorability, speed, and user preference. Furthermore, Pixie was easily discoverable by new users and accurate under field use. Users were able to remember their trinkets 2 and 7 days after registering them, without any practice between the 3 test dates.
Solutions such as @cite_36 @cite_53 @cite_66 treat the mobile device as a second factor and eliminate user interaction to retrieve a token from the mobile device to the authentication device (e.g. a desktop) by leveraging proximity based connectivity (e.g., Bluetooth, Wi-Fi). In contrast, Pixie assigns the duty of storing the token for the second factor to a physical object outside the mobile device. The mobile device is the sole device that is used to access the services on remote servers. As an added benefit, the physical factor of the trinket renders Pixie immune to the 2FA synchronization vulnerabilities'' introduced by @cite_4 , that exploit the ongoing integration of apps among multiple platforms. Since Pixie authentication requires a simple interaction with the user, it is also possible to combine Pixie with a token stored on the mobile device. The combined Pixie and mobile device token authentication would require the user to possess both the particular mobile device that stores the token and the trinket.
@cite_36 @cite_53 @cite_4 @cite_66
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Securing the sensitive data stored and accessed from mobile devices makes user authentication a problem of paramount importance. The tension between security and usability renders however the task of user authentication on mobile devices a challenging task. This paper introduces FAST (Fingergestures Authentication System using Touchscreen), a novel touchscreen based authentication approach on mobile devices. Besides extracting touch data from touchscreen equipped smartphones, FAST complements and validates this data using a digital sensor glove that we have built using off-the-shelf components. FAST leverages state-of-the-art classification algorithms to provide transparent and continuous mobile system protection. A notable feature is FAST 's continuous, user transparent post-login authentication. We use touch data collected from 40 users to show that FAST achieves a False Accept Rate (FAR) of 4.66 and False Reject Rate of 0.13 for the continuous post-login user authentication. The low FAR and FRR values indicate that FAST provides excellent post-login access security, without disturbing the honest mobile users. A trusted execution environment (TEE) is a secure, integrity-protected processing environment, consisting of processing, memory and storage capabilities. It is isolated from the “normal” processing environment, sometimes called the rich execution environment (REE), where the device operating system and applications run. TEEs can make it possible to build REE applications and services with better security and usability by partitioning them so that sensitive operations are restricted to the TEE and sensitive data, like cryptographic keys, never leave the TEE. In our daily lives, we encounter more and more services that use dedicated hardware tokens to improve their security: one-time code tokens for two-factor authentication, wireless tokens for opening doors in buildings or cars, tickets for public transport, and so on. Mobile devices equipped with TEEs have the potential for replacing these many tokens thereby improving the usability for users while also reducing the cost for the service providers without hampering security. Mobile authentication is indispensable for preventing unauthorized access to multi-touch mobile devices. Existing mobile authentication techniques are often cumbersome to use and also vulnerable to shoulder-surfing and smudge attacks. This paper focuses on designing, implementing, and evaluating TouchIn, a two-factor authentication system on multi-touch mobile devices. TouchIn works by letting a user draw on the touchscreen with one or multiple fingers to unlock his mobile device, and the user is authenticated based on the geometric properties of his drawn curves as well as his behavioral and physiological characteristics. TouchIn allows the user to draw on arbitrary regions on the touchscreen without looking at it. This nice sightless feature makes TouchIn very easy to use and also robust to shoulder-surfing and smudge attacks. Comprehensive experiments on Android devices confirm the high security and usability of TouchIn. We introduce SemanticLock, a single factor graphical authentication solution for mobile devices. SemanticLock uses a set of graphical images as password tokens that construct a semantically memorable story representing the user s password. A familiar and quick action of dragging or dropping the images into their respective positions either in a or in movements on the the touchscreen is what is required to use our solution. The authentication strength of the SemanticLock is based on the large number of possible semantic constructs derived from the positioning of the image tokens and the type of images selected. Semantic Lock has a high resistance to smudge attacks and it equally exhibits a higher level of memorability due to its graphical paradigm. In a three weeks user study with 21 participants comparing SemanticLock against other authentication systems, we discovered that SemanticLock outperformed the PIN and matched the PATTERN both on speed, memorability, user acceptance and usability. Furthermore, qualitative test also show that SemanticLock was rated more superior in like-ability. SemanticLock was also evaluated while participants walked unencumbered and walked encumbered carrying "everyday" items to analyze the effects of such activities on its usage. Location is rapidly becoming the next "killer application" as location-enabled mobile handheld devices proliferate. One class of applications that has yet-to-emerge are those in which users have an incentive to lie about their location. These applications cannot rely solely on the users' devices to discover and transmit location information because users have an incentive to cheat. Instead, such applications require their users to prove their locations. Unfortunately, today's mobile users lack a mechanism to prove their current or past locations. Consequently, these applications have yet to take off despite their potential. This paper presents location proofs - a simple mechanism that enables the emergence of mobile applications that require "proof" of a user's location. A location proof is a piece of data that certifies a receiver to a geographical location. Location proofs are handed out by the wireless infrastructure (e.g., a Wi-Fi access point or a cell tower) to mobile devices. The relatively short range of the wireless radios ensures that these devices are in physical proximity to the wireless transmitter. As a result, these devices are capable of proving their current or past locations to mobile applications. In this paper, we start by describing a mechanism to implement location proofs. We then present a set of six future applications that require location proofs to enable their core functionality. Small, mobile devices without user interfaces, such as Bluetooth headsets, often need to communicate securely over wireless networks. Active attacks can only be prevented by authenticating wireless communication, which is problematic when devices do not have any a priori information about each other. We introduce a new method for device-to-device authentication by shaking devices together. This paper describes two protocols for combining cryptographic authentication techniques with known methods of accelerometer data analysis to the effect of generating authenticated, secret keys. The protocols differ in their design, one being more conservative from a security point of view, while the other allows more dynamic interactions. Three experiments are used to optimize and validate our proposed authentication method. Android forensics is one of the most studied topics in the mobile forensics literature, partly due to the popularity of Android devices and apps. However, there does not appear to have a formal model that captures the activities undertaken during a forensic investigation. In this paper, we adapt a widely used adversary model from the cryptographic literature to formally capture a forensic investigator's capabilities during the collection and analysis of evidentiary materials from mobile devices. We demonstrate the utility of the model using five popular Android social apps (Twitter, POF Dating, Snapchat, Fling and Pinterest). We recover various information of forensic interest, such as databases, user account information, sent-received images, profile pictures, contact lists, unviewed text messages. We are also able to determine when a notification was sent, a tweet was posted, as well as identifying the Facebook authentication token string used in the apps. Recent developments in sensing and communication technologies have led to an explosion in the use of mobile devices such as smartphones and tablets. With the increase in the use of mobile devices, users must constantly worry about security and privacy, as the loss of a mobile device could compromise personal information. To deal with this problem, continuous authentication systems (also known as active authentication systems) have been proposed, in which users are continuously monitored after initial access to the mobile device. In this article, we provide an overview of different continuous authentication methods on mobile devices. We discuss the merits and drawbacks of the available approaches and identify promising avenues of research in this rapidly evolving field. Personal mobile devices are increasingly equipped with the capability to sense the physical world (through cameras, microphones, and accelerometers, for example) and the, network world (with Wi-Fi and Bluetooth interfaces). Such devices offer many new opportunities for cooperative sensing applications. For example, users' mobile phones may contribute data to community-oriented information services, from city-wide pollution monitoring to enterprise-wide detection of unauthorized Wi-Fi access points. This people-centric mobile-sensing model introduces a new security challenge in the design of mobile systems: protecting the privacy of participants while allowing their devices to reliably contribute high-quality data to these large-scale applications. We describe AnonySense, a privacy-aware architecture for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our trust model, and the security properties that drove the design of the AnonySense system. We evaluate our prototype implementation through experiments that indicate the feasibility of this approach, and through two applications: a Wi-Fi rogue access point detector and a lost-object finder. Exponential growth in smartphone usage combined with recent advances in mobile technology is causing a shift in (mobile) app behavior: application vendors no longer restrict their apps to a single platform, but rather add synchronization options that allow users to conveniently switch from mobile to PC or vice versa in order to access their services. This process of integrating apps among multiple platforms essentially removes the gap between them. Current, state of the art, mobile phone-based two-factor authentication (2FA) mechanisms, however, heavily rely on the existence of such separation. They are used in a variety of segments (such as consumer online banking services or enterprise secure remote access) to protect against malware. For example, with 2FA in place, attackers should no longer be able to use their PC-based malware to instantiate fraudulent banking transactions. Many mobile devices incorporate low-power wireless connectivity protocols, such as Bluetooth, that can be used to identify an individual to other people nearby. We have developed an architecture that leverages this functionality in mobile phones - originally designed for communication at a distance - to connect people across the room. Serendipity is an application of the architecture. It combines the existing communications infrastructure with online introduction systems' functionality to facilitate interactions between physically proximate people through a centralized server. A new mobile-phone-based system uses Bluetooth hardware addresses and a database of user profiles to cue informal, face-to-face interactions between nearby users who don't know each other, but probably should. Many systems rely on passwords for authentication. Due to numerous accounts for different services, users have to choose and remember a significant number of passwords. Password-Manager applications address this issue by storing the user's passwords. They are especially useful on mobile devices, because of the ubiquitous access to the account passwords. A device that performs private key operations (signatures or decryptions), and whose private key operations are protected by a password, can be immunized against offline dictionary attacks in case of capture by forcing the device to confirm a password guess with a designated remote server in order to perform a private key operation. Recent proposals for achieving this allow untrusted servers and require no server initialization per device. In this paper we extend these proposals to enable dynamic delegation from one server to another; i.e., the device can subsequently use the second server to secure its private key operations. One application is to allow a user who is traveling to a foreign country to temporarily delegate to a server local to that country the ability to confirm password guesses and aid the user's device in performing private key operations, or in the limit, to temporarily delegate this ability to a token in the user's possession. Another application is proactive security for the device's private key, i.e., proactive updates to the device and servers to eliminate any threat of offline password guessing attacks due to previously compromised servers. Recently, remote user authentication schemes are implemented on elliptic curve cryptosystem (ECC) to reduce the computation loads for mobile devices. However, most remote user authentication schemes on ECC are based on public-key cryptosystem, in which the public key in the system requires the associated certificate to prove its validity. Thus, the user needs to perform additional computations to verify the certificate in these schemes. In addition, we find these schemes do not provide mutual authentication or a session key agreement between the user and the remote server. Therefore, we propose an ID-based remote mutual authentication with key agreement scheme on ECC in this paper. Based upon the ID-based concept, the proposed scheme does not require public keys for users such that the additional computations for certificates can be reduced. Moreover, the proposed scheme not only provides mutual authentication but also supports a session key agreement between the user and the server. Compared with the related works, the proposed scheme is more efficient and practical for mobile devices. Implicit authentication schemes are a secondary authentication mechanism that provides authentication by employing unique patterns of device use that are gathered from smartphone users without requiring deliberate actions. Contemporary implicit authentication schemes operate at the device level such that they neither discriminate between data from different applications nor make any assumption about the nature of the application that the user is currently using. In this paper, we challenge the device-centric approach to implicit authentication on smartphones. We argue that the conventional approach of misuse detection at the device level has inherent limitations for mobile platforms. To this end, we analyze and empirically evaluate the device-centric nature of implicit authentication schemes to show their limitations in terms of detection accuracy, authentication overhead, and fine grained authentication control. To mitigate these limitations and for effective and pragmatic implicit authentication on the mobile platform, we propose a novel application-centric implicit authentication approach. We observe that for implicit authentication, an application knows best on when to authenticate and how to authenticate. Therefore, we delegate the implicit authentication task to the application and let the application provider decide when and how to authenticate a user in order to protect the owner's personal information. Our proposed application-centric implicit authentication approach improves accuracy and provides fine grained authentication control with low authentication overhead. Future research in this domain will benefit from our findings to provide pragmatic implicit authentication solutions. Mobile computing continuously evolve through the sustained effort of many researchers. It seamlessly augments users' cognitive abilities via compute-intensive capabilities such as speech recognition, natural language processing, etc. By thus empowering mobile users, we could transform many areas of human activity. This article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them. In this architecture, a mobile user exploits virtual machine (VM) technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN; the mobile device typically functions as a thin client with respect to the service. A cloudlet is a trusted, resource-rich computer or cluster of computers that's well-connected to the Internet and available for use by nearby mobile devices. Our strategy of leveraging transiently customized proximate infrastructure as a mobile device moves with its user through the physical world is called cloudlet-based, resource-rich, mobile computing. Crisp interactive response, which is essential for seamless augmentation of human cognition, is easily achieved in this architecture because of the cloudlet's physical proximity and one-hop network latency. Using a cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users interactively generating and receiving media such as high-definition video and high-resolution images. Rapid customization of infrastructure for diverse applications emerges as a critical requirement, and our results from a proof-of-concept prototype suggest that VM technology can indeed help meet this requirement. A challenge in facilitating spontaneous mobile interactions is to provide pairing methods that are both intuitive and secure. Simultaneous shaking is proposed as a novel and easy-to-use mechanism for pairing of small mobile devices. The underlying principle is to use common movement as a secret that the involved devices share for mutual authentication. We present two concrete methods, ShaVe and ShaCK, in which sensing and analysis of shaking movement is combined with cryptographic protocols for secure authentication. ShaVe is based on initial key exchange followed by exchange and comparison of sensor data for verification of key authenticity. ShaCK, in contrast, is based on matching features extracted from the sensor data to construct a cryptographic key. The classification algorithms used in our approach are shown to robustly separate simultaneous shaking of two devices from other concurrent movement of a pair of devices, with a false negative rate of under 12 percent. A user study confirms that the method is intuitive and easy to use, as users can shake devices in an arbitrary pattern. Android being the most popular open source mobile operating system, attracts a plethora of app developers. Millions of applications are developed for Android platform with a great extent of behavioral diversities and are available on Play Store as well as on many third party app stores. Due to its open nature, in the past Android Platform has been targeted by many malware writers. The conventional way of signature-based detection methods for detecting malware on a device are no longer promising due to an exponential increase in the number of variants of the same application with different signatures. Moreover, they lack in dynamic analysis too. In this paper, we propose DRACO, which employs a two-phase detection technique that blends the synergy of both static and dynamic analysis. It has two modules, client module that is in the form an Android app and gets installed on mobile devices and a server module that runs on a server. DRACO also explains user about the features contributing to the maliciousness of analyzed app and generates scoring for that maliciousness. It does not require any root or super-user privileges. In an evaluation of 18,000 benign applications and 10,000 malware samples, DRACO performs better than several related existing approaches and detects 98.4 of the malware with few false alerts. On ten popular smartphones, the method requires an average of 6 seconds for on device analysis and 90 seconds on server analysis. With the increasing of mobile devices, Device-to-Device(D2D) communication is considered as a promising technology for achieving direct communication of devices. But the security issues still remain to be solved. In this paper, We utilize D2D communication between UEs in proximity to share epidemic media contents. We propose a Secure Content Sharing Protocol(SCSP) that combine with the credibility between users. The credibility involves user's interest, interaction and geographical location. By means of encryption, signature and authentication in SCSP, the data confidentiality, non-repudiation and mutual authentication can be guaranteed. Furthermore, the user's privacy can be protected based on Pallier Homomorphic Encryption. At last, it is shown that the proposed SCSP can meet the security requirements and have lower computation cost in comparison with other related works through security analysis and quantitative evaluation. Understanding user mobility is critical for simula- tions of mobile devices in a wireless network, but current mobility models often do not reflect real user movements. In this paper, we provide a foundation for such work by exploring mobility characteristics in traces of mobile users. We present a method to estimate the physical location of users from a large trace of mobile devices associating with access points in a wireless network. Using this method, we extracted tracks of always-on Wi-Fi devices from a 13-month trace. We discovered that the speed and pause time each follow a log-normal distribution and that the direction of movements closely reflects the direction of roads and walkways. Based on the extracted mobility characteristics, we developed a mobility model, focusing on movements among popular regions. Our validation shows that synthetic tracks match real tracks with a median relative error of 17 .