Observation Resilient Authentication Schemes (ORAS) are a class of shared secret challenge-response identification schemes where a user mentally computes the response via a cognitive function to authenticate herself such that eavesdroppers cannot readily extract the secret. Security evaluation of ORAS generally involves quantifying information leaked via observed challenge-response pairs. However, little work has evaluated information leaked via human behavior while interacting with these schemes. A common way to achieve observation resilience is by including a modulus operation in the cognitive function. This minimizes the information leaked about the secret due to the many-to-one map from the set of possible secrets to a given response. In this work, we show that user behavior can be used as a side-channel to obtain the secret in such ORAS. Specifically, the user's eye-movement patterns and associated timing information can deduce whether a modulus operation was performed (a fundamental design element), to leak information about the secret. We further show that the secret can still be retrieved if the deduction is erroneous, a more likely case in practice. We treat the vulnerability analytically, and propose a generic attack algorithm that iteratively obtains the secret despite the "faulty" modulus information. We demonstrate the attack on five ORAS, and show that the secret can be retrieved with considerably less challenge-response pairs than non-side-channel attacks (e.g., algebraic/statistical attacks). In particular, our attack is applicable on Mod10, a one-time-pad based scheme, for which no non-side-channel attack exists. We field test our attack with a small-scale eye-tracking user study.
Recent research has shown the possibility of using smartphones' sensors and accessories to extract some behavioral attributes such as touch dynamics, keystroke dynamics and gait recognition. These attributes are known as behavioral biometrics and could be used to verify or identify users implicitly and continuously on smartphones. The authentication systems that have been built based on these behavioral biometric traits are known as active or continuous authentication systems. This paper provides a review of the active authentication systems. We present the components and the operating process…
University of Central Florida（中佛罗里达大学）；Ewha Womans University（梨花女子大学）
Mobile devices and technologies have become increasingly popular, offering comparable storage and computational capabilities to desktop computers allowing users to store and interact with sensitive and private information. The security and protection of such personal information are becoming more and more important since mobile devices are vulnerable to unauthorized access or theft. User authentication is a task of paramount importance that grants access to legitimate users at the point-of-entry and continuously through the usage session. This task is made possible with today's smartphones' em…
In recent cyber attacks, credential theft has emerged as one of the primary vectors of gaining entry into the system. Once attacker(s) have a foothold in the system, they use various techniques including token manipulation to elevate the privileges and access protected resources. This makes authentication and token based authorization a critical component for a secure and resilient cyber system. In this paper we discuss the design considerations for such a secure and resilient authentication and authorization framework capable of self-adapting based on the risk scores and trust profiles. We co…
In this paper we evaluate mobile active authentication based on an ensemble of biometrics and behavior-based profiling signals. We consider seven different data channels and their combination. Touch dynamics (touch gestures and keystroking), accelerometer, gyroscope, WiFi, GPS location and app usage are all collected during human-mobile interaction to authenticate the users. We evaluate two approaches: one-time authentication and active authentication. In one-time authentication, we employ the information of all channels available during one session. For active authentication we take advantage…
In most PUF-based authentication schemes, a central server is usually engaged to verify the response of the device's PUF to challenge bit-streams. However, the server availability may be intermittent in practice. To tackle such an issue, this paper proposes a new protocol for supporting distributed authentication while avoiding vulnerability to information leakage where CRPs could be retrieved from hacked devices and collectively used to model the PUF. The main idea is to provision for scrambling the challenge bit-stream in a way that is dependent on the verifier. The scrambling pattern varies…
Physical unclonable functions (PUFs), as hardware security primitives, exploit manufacturing randomness to extract hardware instance-specific secrets. One of most popular structures is time-delay based Arbiter PUF attributing to large number of challenge response pairs (CRPs) yielded and its compact realization. However, modeling building attacks threaten most variants of APUFs that are usually employed for strong PUF-oriented application---lightweight authentication---without reliance on the securely stored digital secrets based standard cryptographic protocols. In this paper, we investigate …
Mobile devices have been manufactured and enhanced at growing rates in the past decades. While this growth has significantly evolved the capability of these devices, their security has been falling behind. This contrast in development between capability and security of mobile devices is a significant problem with the sensitive information of the public at risk. Continuing the previous work in this field, this study identifies key Machine Learning algorithms currently being used for behavioral biometric mobile authentication schemes and aims to provide a comprehensive review of these algorithms…
University of Wisconsin – Eau Claire（威斯康星大学欧克莱尔分校）；North Carolina Agricultural & Technical State University（北卡罗来纳农业与技术州立大学）
The amount of secure data being stored on mobile devices has grown immensely in recent years. However, the security measures protecting this data have stayed static, with few improvements being done to the vulnerabilities of current authentication methods such as physiological biometrics or passwords. Instead of these methods, behavioral biometrics has recently been researched as a solution to these vulnerable authentication methods. In this study, we aim to contribute to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral b…
UC Berkeley（加州大学伯克利分校）；German Research Center for Artificial Intelligence (DFKI) GmbH（德国人工智能研究中心（DFKI）有限公司）；University of Oxford（牛津大学）
We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification f…
Most of the current user authentication systems are based on PIN code, password, or biometrics traits which can have some limitations in usage and security. Lifestyle authentication has become a new research approach. A promising idea for it is to use the location history since it is relatively unique. Even when people are living in the same area or have occasional travel, it does not vary from day to day. For Global Positioning System (GPS) data, the previous work used the longitude, the latitude, and the timestamp as the features for the classification. In this paper, we investigate a new ap…
Emerging technology demands reliable authentication mechanisms, particularly in interconnected systems. Current systems rely on a single moment of authentication, however continuous authentication systems assess a users identity utilizing a constant biometric analysis. Spy Hunter, a continuous authentication mechanism uses keystroke dynamics to validate users over blocks of data. This easily-incorporated periodic biometric authentication system validates genuine users and detects intruders quickly. Because it verifies users in the background, Spy Hunter is not constrained to a password box. In…
We introduce Hand Movement, Orientation, and Grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data was collected under two conditions: sitting and walking. We achieved authentication EERs as low as 7.16% (walking) and 10.05% (sitting) when we combin…
Smartphones and tablets have become ubiquitous in our daily lives. Smartphones, in particular, have become more than personal assistants. These devices have provided new avenues for consumers to play, work, and socialize whenever and wherever they want. Smartphones are small in size, so they are easy to handle and to stow and carry in users' pockets or purses. However, mobile devices are also susceptible to various problems. One of the greatest concerns is the possibility of breach in security and privacy if the device is seized by an outside party. It is possible that threats can come from fr…
Previous studies have shown that commonly studied (vanilla) implementations of touch-based continuous authentication systems (V-TCAS) are susceptible to active adversarial attempts. This study presents a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework and compares it to the V-TCAS under three active adversarial environments viz. Zero-effort, Population, and Random-vector. The Zero-effort environment was implemented in two variations viz. Zero-effort (same-dataset) and Zero-effort (cross-dataset). The first involved a Zero-effort attack from the same dataset, while the sec…
Mobile applications are widely used for online services sharing a large amount of personal data online. One-time authentication techniques such as passwords and physiological biometrics (e.g., fingerprint, face, and iris) have their own advantages but also disadvantages since they can be stolen or emulated, and do not prevent access to the underlying device, once it is unlocked. To address these challenges, complementary authentication systems based on behavioural biometrics have emerged. The goal is to continuously profile users based on their interaction with the mobile device. However, exis…
Continuous Behavioral Authentication via Multi-Expert BERT Log Analysis for Secure Data Sharing
Dept. of Electrical and Computer Engineering, University of Thessaly, Greece（希腊塞萨洛尼基大学电气与计算机工程系）；Centre for Research and Technology Hellas, CERTH, Greece（希腊研究中心与技术 Hellas 研究中心）
The Internet of Things (IoT) is increasingly empowering people with an interconnected world of physical objects ranging from smart buildings to portable smart devices such as wearables. With recent advances in mobile sensing, wearables have become a rich collection of portable sensors and are able to provide various types of services including tracking of health and fitness, making financial transactions, and unlocking smart locks and vehicles. Most of these services are delivered based on users' confidential and personal data, which are stored on these wearables. Existing explicit authenticat…
Continuous authentication utilizes automatic recognition of certain user features for seamless and passive authentication without requiring user attention. Such features can be divided into categories of physiological biometrics and behavioral biometrics. Keystroke dynamics is proposed for behavioral biometrics-oriented authentication by recognizing users by means of their typing patterns. However, it has been pointed out that continuous authentication using physiological biometrics and behavior biometrics incur privacy risks, revealing personal characteristics and activities. In this paper, w…
As the demand for highly secure and dependable lightweight systems increases in the modern world, Physically Unclonable Functions (PUFs) continue to promise a lightweight alternative to high-cost encryption techniques and secure key storage. While the security features promised by PUFs are highly attractive for secure system designers, they have been shown to be vulnerable to various sophisticated attacks - most notably Machine Learning (ML) based modelling attacks (ML-MA) which attempt to digitally clone the PUF behaviour and thus undermine their security. More recent ML-MA have even exploite…
It is a challenging task to deploy lightweight security protocols in resource-constrained IoT applications. A hardware-oriented lightweight authentication protocol based on device signature generated during voltage over-scaling (VOS) was recently proposed to address this issue. VOS-based authentication employs the computation unit such as adders to generate the process variation dependent error which is combined with secret keys to create a two-factor authentication protocol. In this paper, machine learning (ML)-based modeling attacks to break such authentication is presented. We also propose …
Authenticated safety beacons in Vehicular Communication (VC) systems ensure awareness among neighboring vehicles. However, the verification of beacon signatures introduces significant processing overhead for resource-constrained vehicular On-Board Units (OBUs). Even worse in dense neighborhood or when a clogging Denial of Service (DoS) attack is mounted. The OBU would fail to verify for all received (authentic or fictitious) beacons. This could significantly delay the verifications of authentic beacons or even affect the awareness of neighboring vehicle status. In this paper, we propose an eff…
Home-based Internet of Things (IoT) devices have gained in popularity and many households have become 'smart' by using devices such as smart sensors, locks, and voice-based assistants. Traditional authentication methods such as passwords, biometrics or multi-factor (using SMS or email) are either not applicable in the smart home setting, or they are inconvenient as they break the natural flow of interaction with these devices. Voice-based biometrics are limited due to safety and privacy concerns. Given the limitations of existing authentication techniques, we explore new opportunities for user…
We propose Secure Pick Up (SPU), a convenient, lightweight, in-device, non-intrusive and automatic-learning system for smartphone user authentication. Operating in the background, our system implicitly observes users' phone pick-up movements, the way they bend their arms when they pick up a smartphone to interact with the device, to authenticate the users. Our SPU outperforms the state-of-the-art implicit authentication mechanisms in three main aspects: 1) SPU automatically learns the user's behavioral pattern without requiring a large amount of training data (especially those of other users) …
The Internet-of-Things (IoT) has brought in new challenges in, device identification --what the device is, and, authentication --is the device the one it claims to be. Traditionally, the authentication problem is solved by means of a cryptographic protocol. However, the computational complexity of cryptographic protocols and/or scalability problems related to key management, render almost all cryptography based authentication protocols impractical for IoT. The problem of device identification is, on the other hand, sadly neglected. We believe that device fingerprinting can be used to solve bot…
The Internet of Things (IoT) is increasingly empowering people with an interconnected world of physical objects ranging from smart buildings to portable smart devices such as wearables. With the recent advances in mobile sensing, wearables have become a rich collection of portable sensors and are able to provide various types of services including health and fitness tracking, financial transactions, and unlocking smart locks and vehicles. Existing explicit authentication approaches (i.e., PINs or pattern locks) suffer from several limitations including limited display size, shoulder surfing, a…
This paper presents TapMeIn, an eyes-free, two-factor authentication method for smartwatches. It allows users to tap a memorable melody (tap-password) of their choice anywhere on the touchscreen to unlock their watch. A user is verified based on the tap-password as well as her physiological and behavioral characteristics when tapping. Results from preliminary experiments with 41 participants show that TapMeIn could achieve an accuracy of 98.7% with a False Positive Rate of only 0.98%. In addition, TapMeIn retains its performance in different conditions such as sitting and walking. In terms of …
Leveraging users' behavioral data sampled by various sensors during the identification process, implicit authentication (IA) relieves users from explicit actions such as remembering and entering passwords. Various IA schemes have been proposed based on different behavioral and contextual features such as gait, touch, and GPS. However, existing IA schemes suffer from false positives, i.e., falsely accepting an adversary, and false negatives, i.e., falsely rejecting the legitimate user due to users' behavior change and noise. To deal with this problem, we propose BubbleMap (BMap), a framework th…
University of Wisconsin – Eau Claire（威斯康星大学欧克莱尔分校）；North Carolina A&T State University（北卡罗来纳农工州立大学）
In recent years the amount of secure information being stored on mobile devices has grown exponentially. However, current security schemas for mobile devices such as physiological biometrics and passwords are not secure enough to protect this information. Behavioral biometrics have been heavily researched as a possible solution to this security deficiency for mobile devices. This study aims to contribute to this innovative research by evaluating the performance of a multimodal behavioral biometric based user authentication scheme using touch dynamics and phone movement. This study uses a fusio…
Message authentication guarantees the integrity of messages exchanged over untrusted channels. However, to achieve this goal, message authentication considerably expands packet sizes, which is especially problematic in constrained wireless environments. To address this issue, progressive message authentication provides initially reduced integrity protection that is often sufficient to process messages upon reception. This reduced security is then successively improved with subsequent messages to uphold the strong guarantees of traditional integrity protection. However, contrary to previous cla…
With the latest developments in technology, extra and extra human beings depend on their private gadgets to keep their touchy information. Concurrently, the surroundings in which these gadgets are linked have grown to grow to be greater dynamic and complex. This opens the dialogue of if the modern day authentication strategies being used in these gadgets are dependable ample to preserve these user's records safe. This paper examines the distinct consumer authentication schemes proposed to make bigger the protection of exceptional devices. This article is break up into two one of a kind avenues…
BEACON: A Multimodal Dataset for Learning Behavioral Fingerprints from Gameplay Data
Computer Science and Engineering Department Thapar Institute of Engineering and Technology（计算机科学与工程系 泰帕尔工程与技术学院）；David R Cheriton School of Computer Science University of Waterloo, Canada（大卫·R·切里顿计算机科学学院 温哥华大学）
Bio-features are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summaries the factors that hinder biometrics models' development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authenticati…
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 …
Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this study, we collect and analyze behavioral biometrics data from 200subjects, each using their personal Android mobile device for a period of at least 30 days. This dataset is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment su…
The Internet of Things (IoT) is giving a boost to a plethora of new opportunities for the robust and sustainable deployment of cyber physical systems. The cornerstone of any IoT system is the sensing devices. These sensing devices have considerable resource constraints, including insufficient battery capacity, CPU capability, and physical security. Because of such resource constraints, designing lightweight cryptographic protocols is an opportunity. Remote User Authentication ensures that two parties establish a secure and durable session key. This study presents a lightweight and safe authent…
Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously bu…
Smartphones are now frequently used by end-users as the portals to cloud-based services, and smartphones are easily stolen or co-opted by an attacker. Beyond the initial log-in mechanism, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data, whether in the cloud or in the smartphone. But attackers who have gained access to a logged-in smartphone have no incentive to re-authenticate, so this must be done in an automatic, non-bypassable way. Hence, this paper proposes a novel authentication system, iAuth, for implicit, continuous au…
Implicit authentication (IA) transparently authenticates users by utilizing their behavioral data sampled from various sensors. Identifying the illegitimate user through constantly analyzing current users' behavior, IA adds another layer of protection to the smart device. Due to the diversity of human behavior, the existing research works tend to simultaneously utilize many different features to identify users, which is less efficient. Irrelevant features may increase system delay and reduce the authentication accuracy. However, dynamically choosing the best suitable features for each user (pe…
The lack of standard input interfaces in the Internet of Things (IoT) ecosystems presents a challenge in securing such infrastructures. To tackle this challenge, we introduce a novel behavioral biometric system based on naturally occurring interactions with objects in smart environments. This biometric leverages existing sensors to authenticate users without requiring any hardware modifications of existing smart home devices. The system is designed to reduce the need for phone-based authentication mechanisms, on which smart home systems currently rely. It requires the user to approve transacti…
Biometrics deal with automated methods of identifying a person or verifying the identity of a person based on physiological or behavioral characteristics. Visual cryptography is a secret sharing scheme where a secret image is encrypted into the shares which independently disclose no information about the original secret image. As biometric template are stored in the centralized database, due to security threats biometric template may be modified by attacker. If biometric template is altered authorized user will not be allowed to access the resource. To deal this issue visual cryptography schem…
Continuous authentication has been proposed as a complementary security mechanism to password-based authentication for computer devices that are handled directly by humans, such as smart phones. Continuous authentication has some privacy issues as certain user features and actions are revealed to the authentication server, which is not assumed to be trusted. Wei et al. proposed in 2021 a privacy-preserving protocol for behavioral authentication that utilizes homomorphic encryption. The encryption prevents the server from obtaining sampled user features. In this paper, we show that the Wei et a…
In this paper, a new behavioral biometric password based on haptic interaction is described. This "haptic password" system is built on a device that has a force-sensitive screen. The user authentication process leverages maximal overlap discrete wavelet transform (MODWT) based feature extraction along with a customized classifier as well as an adaptive template scheme to accommodate users' variations over time. An experimental study was conducted with 29 subjects and we investigated the authentication performance with multiple user postures under simulated forgery attacks. The performance of t…
Transparent authentication (TA) schemes are those in which a user is authenticated by a verifier without requiring explicit user interaction. By doing so, those schemes promise high usability and security simultaneously. The majority of TA implementations rely on the received signal strength as an indicator for the proximity of a user device (prover). However, such implicit proximity verification is not secure against an adversary who can relay messages over a larger distance. In this paper, we propose a novel approach for thwarting relay attacks in TA schemes: the prover permits access to aut…
In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we find that there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. To deeply exploit such a complementary effect, we propo…
