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.
Research in biometric gait recognition has increased. Earlier gait recognition works reported promising results, usually with a small sample size. Recent studies with a larger sample size confirm gait potential as a biometric from which individuals can be identified. Despite much research being carried out in gait recognition, the topic of vulnerability of gait to attacks has not received enough attention. In this paper, an analysis of minimal-effort impersonation attack and the closest person attack on gait biometrics are presented. Unlike most previous gait recognition approaches, where gait is captured using a (video) camera from a distance, in our approach, gait is collected by an accelerometer sensor attached to the hip of subjects. Hip acceleration in three orthogonal directions (up-down, forward-backward, and sideways) is utilized for recognition. We have collected 760 gait sequences from 100 subjects. The experiments consisted of two parts. In the first part, subjects walked in their normal walking style, and using the averaged cycle method, an EER of about 13 was obtained. In the second part, subjects were trying to walk as someone else. Analysis based on FAR errors indicates that a minimal-effort impersonation attack on gait biometric does not necessarily improve the chances of an impostor being accepted. However, attackers with knowledge of their closest person in the database can be a serious threat to the authentication system. The widespread usage of smartphones gives rise to new security and privacy concerns. Smartphones are becoming a personal entrance to networks, and may store private information. Due to its small size, a smartphone could be easily taken away and used by an attacker. Using a victim’s smartphone, the attacker can launch an impersonation attack, which threatens the security of current networks, especially online social networks. Therefore, it is necessary to design a mechanism for smartphones to re-authenticate the current user’s identity and alert the owner when necessary. Such a mechanism can help to inhibit smartphone theft and safeguard the information stored in smartphones. In this paper, we propose a novel biometric-based system to achieve continuous and unobservable re-authentication for smartphones. The system uses a classifier to learn the owner’s finger movement patterns and checks the current user’s finger movement patterns against the owner’s. The system continuously re-authenticates the current user without interrupting user-smartphone interactions. Experiments show that our system is efficient on smartphones and achieves high accuracy. Password patterns, as used on current Android phones, and other shape-based authentication schemes are highly usable and memorable. In terms of security, they are rather weak since the shapes are easy to steal and reproduce. In this work, we introduce an implicit authentication approach that enhances password patterns with an additional security layer, transparent to the user. In short, users are not only authenticated by the shape they input but also by the way they perform the input. We conducted two consecutive studies, a lab and a long-term study, using Android applications to collect and log data from user input on a touch screen of standard commercial smartphones. Analyses using dynamic time warping (DTW) provided first proof that it is actually possible to distinguish different users and use this information to increase security of the input while keeping the convenience for the user high. As we are surrounded by an ever-larger variety of post-PC devices, the traditional methods for identifying and authenticating users have become cumbersome and time-consuming. In this paper, we present a capacitive communication method through which a device can recognize who is interacting with it. This method exploits the capacitive touchscreens, which are now used in laptops, phones, and tablets, as a signal receiver. The signal that identifies the user can be generated by a small transmitter embedded into a ring, watch, or other artifact carried on the human body. We explore two example system designs with a low-power continuous transmitter that communicates through the skin and a signet ring that needs to be touched to the screen. Experiments with our prototype transmitter and tablet receiver show that capacitive communication through a touchscreen is possible, even without hardware or firmware modifications on a receiver. This latter approach imposes severe limits on the data rate, but the rate is sufficient for differentiating users in multiplayer tablet games or parental control applications. Controlled experiments with a signal generator also indicate that future designs may be able to achieve datarates that are useful for providing less obtrusive authentication with similar assurance as PIN codes or swipe patterns commonly used on smartphones today. This paper presents an overview of the weakness of biometric security systems and possible solutions to improve it. Different levels of attack are described, and the strengths and weaknesses of the main biometric system are emphasized. Solutions are provided with special emphasis on cryptography and watermarking techniques.  In this paper we discuss the feasibility of employing keystroke dynamics to perform user verification on mobile phones. Specifically, after having introduced a new statistical classifier, we analyze the discriminative capabilities of the features extracted from the acquired patterns, in order to determine which ones guarantee the best authentication performances. The effectiveness of using template selection techniques for keystroke verification is also investigated. The obtained experimental results indicate that the proposed method can be effectively employed to authenticate mobile phones users, even in operational contexts where the number of enrollment acquisition is kept low. Identifying users of portable devices from gait signals acquired with three-dimensional accelerometers was studied. Three approaches, correlation, frequency domain and data distribution statistics, were used. Test subjects (N=36) walked with fast, normal and slow walking speeds in enrolment and test sessions on separate days wearing the accelerometer device on their belt, at back. It was shown to be possible to identify users with this novel gait recognition method. Best equal error rate (EER=7 ) was achieved with the signal correlation method, while the frequency domain method and two variations of the data distribution statistics method produced EER of 10 , 18 and 19 , respectively.