Biometric systems have become increasingly popular for user authentication andidentification across various domains, including smartphones, laptops, financial services,healthcare, and security. These systems offer enhanced security and quickaccess to data, aiming to address the challenges associated with passwords and pins.However, achieving a method that provides a 100% guarantee in all fields and for alldevices remains a challenge. To explore user perceptions on the acceptance of biometricsystems, an online survey was conducted with 99 participants from diversebackgrounds, education levels, ages, and countries. The survey focused on understandingusers’ acceptance of biometric systems based on their experiences and perspectives.Additionally, it aimed to investigate whether demographic factors, suchas age, education, and background, influence user acceptance. The study also comparedthe preferred authentication technique among users with findings from previousstudies. The survey results supported previous research, showing that fingerprinttechnology is the most recommended and preferred method for user authentication,followed by facial recognition. This study sheds light on the growing adoption ofbiometric systems to overcome password-related issues and provides valuable insightsinto user preferences for authentication and identification methods.

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The aim of the project was to examine the feasibilityof evading continuous authentication systems with a generativeadversarial network. To this end, a group of supervised andunsupervised state-of-the-art classifiers were trained on a publiclyavailable dataset of stroke patterns on mobile devices. To find thebest configurations for each classifier, hyper-parameter searcheswere performed. To attack the classifiers, a generative adversarialnetwork was trained on the dataset to reproduce samples followingthe same distribution. The generative adversarial networkwas optimized to maximize the Equal Error Rate metric of theclassifiers on the reproduced data. Our results show that theEqual Error Rate and the Threshold False Acceptance Rateincreased on generated samples compared to random evasionattacks. Across the classifiers, the greatest increase in Equal ErrorRate was 26 percent (for the artificial neural network), and thegreatest increase in Threshold False Acceptance Rate was 60percent for the same classifier. Moreover, it was found that, ingeneral, the unsupervised classifiers were more robust towardsthis type of attack. The results indicate that evasion attacksagainst continuous authentication systems using a generativeadversarial network are feasible and thus constitute a real threat. / Målet med detta projekt var att undersökamöjligheten att undgå ett aktivt verifieringssystem med hjälpav ett generativt nätverk. För att göra detta valde vi ut ettantal moderna klassifieringsalgoritmer och tränade dem på enoffentlig datasamling av svepmönster på mobiltelefoner. För atterhålla de bästa konfigurationerna för varje klassifieringsalgoritmutfördes hyper-parameter sökningar. För att attackera klassifieringsalgorithmernaimplementerades ett generative adversarialnetwork som tränades på datasamlingen för att reproduceraliknande svepmönster. Det generativa nätverket optimerades föratt maximera klassifieringsalgoritmernas likvärdiga felkvot medden reproducerade datan. Resultaten visar att den likvärdigafelkvoten och tröskeln av den felaktiga verifieringskvoten ökademed den reproducerade datan jämfört med slumpmässiga tester.Den högsta ökningen av den likvärdiga felkvoten var 26 procent(för det artificiella neurala nätverket) och den högsta ökningenav tröskeln av den felaktiga verifieringskvoten var 60 procent forsamma algoritm. Därutöver fann vi att de oövervakade klassifieringsalgoritmernavar mer motståndskraftiga mot denna typenav attack jämfört med de övervakade klassifieringsalgoritmerna.Resultaten tyder på att det är möjligt att till viss del undgå ettaktivt verifieringssystem med hjälp av ett generative adversarialnetwork och att denna typen av attacker utgör ett konkret hot. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

The research conducted under this study offers an understanding of the reasons why information technology (IT) and/or information assurance (IA) managers choose to recommend or not to recommend particular technologies, specifically biometric security, to their organizations. A review of the relevant literature provided the foundation to develop a set of research questions and factors for this research effort. The research questions became the basis of the study’s stated hypotheses for examining managers’ perceptions of the security effectiveness, need, reliability, and cost-effectiveness of biometrics. The research indicates that positive perceptions of security effectiveness, need, reliability, and cost-effectiveness correlate with IT/IA managers’ willingness to recommend biometric security technologies. The implications of this study are that executives and managers can make informed decisions about the recommendation and adoption process relevant to biometric security technologies through an understanding of how perceptions of biometric technology affect the decision to recommend this type of technology. The study’s results may also help biometric product developers, vendors, and marketers understand the important perceptions of biometric security technologies within their customer base of IT/IA managers.

In the internet age, privacy and security have become major concerns since an increasing number of transactions are made over an unsecured network. Thus there is a greater chance for private data to be misused. Further, insider attacks can result in loss of valuable data. Hence there arises a strong need for continual, non-intrusive, quick user re-authentication. Previously, a number of studies have been conducted on authentication using behavioral attributes. Currently, few successful re-authentication mechanisms are available since they use either the mouse or the keyboard for re-authentication and target particular applications. However, successful re-authentication is still dependent on a large number of factors such as user excitation level, fatigue and using just the keyboard or the mouse does not mitigate these factors successfully. Both keyboard and mouse contain valuable, hard-to-duplicate information about the user's behavior. This can be used for analysis and identification of the current user. We propose an application independent system that uses this information for user re-authentication. This system will authenticate the user continually based on his/her behavioral attributes obtained from both the keyboard and mouse operations. This re-authentication system is simple, continual, non-intrusive and easily deployable. To utilize the mouse and keyboard information for re-authentication, we propose a novel heuristic that uses the percentage of mouse-to-keyboard interaction ratio. This heuristic allows us to extract suitable user-behavioral attributes. The extracted data is compared with an already trained database for user re-authentication. The accuracy of the system is calculated by the number of correct identifications to total number of identifications. At present, the accuracy of the system is around 96% for application based user re-authentication and around 82% for application independent user re-authentication. We perform black box, white box testing and Spec# verification procedures that prove the robustness of the proposed system. On testing POCKET, a privacy protection software for children, it was found that the security of POCKET was inadequate at the user level. Our system enhances POCKET security at the user level and ensures that the child's privacy is protected. / Master of Science

Identity verification is ubiquitous in our daily life. By verifying the user's identity, the authorization process grants the privilege to access resources or facilities or perform certain tasks. The traditional and most prevalent authentication method is the personal identification number (PIN) or password. While these knowledge-based credentials could be lost or stolen, human biometric-based verification technologies have become popular alternatives in recent years. Nowadays, more people are used to unlocking their smartphones using their fingerprint or face instead of the conventional passcode. However, these biometric approaches have their weaknesses. For example, fingerprints could be easily fabricated, and a photo or image could spoof the face recognition system. In addition, these existing biometric-based identity verification methods could continue if the user is unaware, sleeping, or even unconscious. Therefore, an additional level of security is needed. In this dissertation, we demonstrate a novel identity verification approach, which makes the biometric authentication process more secure. Our approach requires only one regular camera to acquire a short video for computing the face and facial motion representations. It takes advantage of the advancements in computer vision and deep learning techniques. Our new deep neural network model, or facial motion encoder, can generate a representation vector for the facial motion in the video. Then the decision algorithm compares the vector to the enrolled facial motion vector to determine their similarity for identity verification. We first proved its feasibility through a keypoint-based method. After that, we built a curated dataset and proposed a novel representation learning framework for facial motions. The experimental results show that this facial motion verification approach reaches an average precision of 98.8\%, which is more than adequate for customary use. We also tested this algorithm on complex facial motions and proposed a new self-supervised pretraining approach to boost the encoder's performance. At last, we evaluated two other potential upstream tasks that could help improve the efficiency of facial motion encoding. Through these efforts, we have built a solid benchmark for facial motion representation learning, and the elaborate techniques can inspire other face analysis and video understanding research.