Персона: Когос, Константин Григорьевич
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Touch and Move: Incoming Call User Authentication
© 2019, Springer Nature Switzerland AG.This paper presents two methods of implicit authentication during answering an incoming call based on user behavior biometrics. Such methods allow to increase usability of authentication against common PIN or graphical password. Also, a concept of authentication system based on presented methods is proposed. The paper shows that user’s touch dynamics and movement of the hand towards the ear when accepting the call provide all necessary information for authentication and there is no need for user to enter a PIN or graphical password.
Timing covert channels detection cases via machine learning
© 2019 IEEE.Currently, packet data networks are widespread. Their architectural features allow constructing covert channels that are able to transmit covert data under the conditions of using standard protection measures. However, encryption or packets length normalization, leave the possibility for an intruder to transfer covert data via timing covert channels (TCCs). In turn, inter-packet delay (IPD) normalization leads to reducing communication channel capacity. Detection is an alternative countermeasure. At the present time, detection methods based on machine learning are widely studied. The complexity of TCCs detection based on machine learning depends on the availability of traffic samples, and on the possibility of an intruder to change covert channels parameters. In the current work, we explore the cases of TCCs detection via.
Continuous authentication of smartphone users via swipes and taps analysis
© 2019 IEEE.Nowadays, smartphones are used for getting access to sensitive and private data. As a result, we need an authentication system that will provide smartphones with additional security and at the same time will not cause annoyance to users. Existing authentication mechanisms provide just a one-time user verification and do not perform re-authentication in the process of further interaction. In this paper, we present a continuous user authentication system based on user's interaction with the touchscreen in conjunction with micromovements, performed by smartphones at the same time. We consider two of the most common types of gestures performed by users (vertical swipes up and down, and taps). The novelty of our approach is that swipes and taps are both analyzed to provide continuous authentication. Swipes are informative gestures, while taps are the most common gestures. This way, we aim to reduce the time of impostors' detection. The proposed scheme collects data from the touchscreen and multiple 3-dimensional sensors integrated in all modern smartphones. We use One-Class Support Vector Machine (OSVM) algorithm to get a model of a legitimate user. The obtained results show that the proposed scheme of continuous authentication can improve smartphone security.
Artificial Intelligence to Detect Timing Covert Channels
© 2020, Springer Nature Switzerland AG.The peculiarities of the batch data transmission networks make it possible to use covert channels, which survive under standard protective measures, to perform data leaks. However, storage covert channels can be annihilated by means of limiting the flow capacity, or by use of encryption. The measures against storage covert channels cannot be implemented against timing covert channels (TCCs), otherwise their usage has to be conditioned by certain factors. For instance, while packet encryption an intruder still possesses the ability to covertly transfer the data. At the same time, normalization of inter-packet delays (IPDs) influences the flow capacity in a greater degree than sending fixed-length packets does. Detection can be called an alternative countermeasure. At the present time, detection methods based on artificial intelligence have been widespreadly used, however the possibility to implement these methods under conditions of a covert channel parametrization has not been investigated. In the current work, we study the possibility to implement artificial intelligence for detecting TCCs under conditions of varying covert channel characteristics: flow capacity and encoding scheme. The detection method is based on machine learning algorithms that solve the problem of binary classification.
Mobile user authentication using keystroke dynamics
© 2019 IEEE.Behavioral biometrics identifies individuals according to their unique way of interacting with computer devices. Keystroke dynamics can be used to identify people, and it can replace the second factor in two-factor authentication. This paper presents a keystroke dynamics biometric system for user authentication in mobile devices. We propose to use data from sensors of motion and position as features for the biometric system to improve the quality of user recognition. The proposed novel model combines different anomaly detection methods (distance-based and density-based) in an ensemble. We achieved the average EER of 8.0%. Our model has a retraining module that updates the keystroke dynamics template of a user each time after a successful authentication in the system. All the process of training and retraining a model and making a decision is made directly on a mobile device using our mobile application, as well as keystroke data is stored on a device.