Biometric Identification Based on Keystroke Dynamics

Kasprowski, Paweł; Borowska, Zaneta; Harężlak, Katarzyna

doi:10.3390/s22093158

Cited by 10 publications

(16 citation statements)

References 17 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In conclusion, the study proposes effective feature engineering strategies and compares two feature structures for authentication based on free-text keystroke dynamics. Pawel Kasprowski, Zaneta Borowska, and Katarzyna Harezlak [23] investigated the impact of altering neural network architecture and hyperparameters on biometric identification using keystroke dynamics. A publicly available dataset of keystrokes was utilized to train models with diverse parameters.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

Bansal,

Ouda

2024

Computers

View full text Add to dashboard Cite

This research article delves into the development of a reinforcement learning (RL)-based continuous authentication system utilizing behavioral biometrics for user identification on computing devices. Keystroke dynamics are employed to capture unique behavioral biometric signatures, while a reward-driven RL model is deployed to authenticate users throughout their sessions. The proposed system augments conventional authentication mechanisms, fortifying them with an additional layer of security to create a robust continuous authentication framework compatible with static authentication systems. The methodology entails training an RL model to discern atypical user typing patterns and identify potentially suspicious activities. Each user’s historical data are utilized to train an agent, which undergoes preprocessing to generate episodes for learning purposes. The environment involves the retrieval of observations, which are intentionally perturbed to facilitate learning of nonlinear behaviors. The observation vector encompasses both ongoing and summarized features. A binary and minimalist reward function is employed, with principal component analysis (PCA) utilized for encoding ongoing features, and the double deep Q-network (DDQN) algorithm implemented through a fully connected neural network serving as the policy net. Evaluation results showcase training accuracy and equal error rate (EER) ranging from 94.7% to 100% and 0 to 0.0126, respectively, while test accuracy and EER fall within the range of approximately 81.06% to 93.5% and 0.0323 to 0.11, respectively, for all users as encoder features increase in number. These outcomes are achieved through RL’s iterative refinement of rewards via trial and error, leading to enhanced accuracy over time as more data are processed and incorporated into the system.

show abstract

Section: Background and Literature Reviewmentioning

confidence: 99%

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

Bansal,

Ouda

2024

Computers

View full text Add to dashboard Cite

show abstract

“…Convolutional and recurrent networks are often used for the purpose of authenticating users by the way in which they interact with a computer. Manny research indicates that the best error-rate values achieved for the preceding experiments regarding CNN and LSTM (a type of RNN) are as follows: CNN, 2.3% and 6.5% (with and without data augmentation); LSTM, 13.6%; and CNN + LSTM, 2.36% or 5.97%, depending on the dataset utilized [65]. Furthermore, the benefits of combining various neural networks can be seen in other fields as well.…”

Section: Keystroke-injection Detectionmentioning

confidence: 99%

A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation

Gurčinas,

Dautartas,

Janulevičius

et al. 2023

Electronics

View full text Add to dashboard Cite

Investigation and detection of cybercrimes has been in the spotlight of cybersecurity research for as long as the topic has existed. Modern methods are required to keep up with the pace of the technology and toolset used to facilitate these crimes. Keystroke-injection attacks have been an issue due to the limitations of hardware and software up until recently. This paper presents comprehensive research on keystroke-injection payload generation that proposes the use of deep learning to bypass the security of keystroke-based authentication systems focusing on both fixed-text and free-text scenarios. In addition, it specifies the potential risks associated with keystroke-injection attacks. To ensure the legitimacy of the investigation, a model is proposed and implemented within this context. The results of the implemented implant model inside the keyboard indicate that deep learning can significantly improve the accuracy of keystroke dynamics recognition as well as help to generate effective payload from a locally collected dataset. The results demonstrate favorable accuracy rates, with reported performance of 93–96% for fixed-text scenarios and 75–92% for free-text. Accuracy across different text scenarios was achieved using a small dataset collected with the proposed implant model. This dataset enabled the generation of synthetic keystrokes directly within a low-computation-power device. This approach offers efficient and almost real-time keystroke replication. The results obtained show that the proposed model is sufficient not only to bypass the fixed-text keystroke dynamics system, but also to remotely control the victim’s device at the appropriate time. However, such a method poses high security risks when deploying adaptive keystroke injection with impersonated payload in real-world scenarios.

show abstract

“…Keystroke dynamics is a soft biometric, which is widely used in many applications such as user authentication [1][2][3], fraud detection [4], biometric identification [5,6] and human-computer interaction [7]. Keystroke dynamics refers to the habitual patterns or rhythms an individual exhibits while typing on a keyboard input device.…”

Section: Introductionmentioning

confidence: 99%

KD-Net: Continuous-Keystroke-Dynamics-Based Human Identification from RGB-D Image Sequences

Dai,

Zhao,

et al. 2023

Sensors

View full text Add to dashboard Cite

Keystroke dynamics is a soft biometric based on the assumption that humans always type in uniquely characteristic manners. Previous works mainly focused on analyzing the key press or release events. Unlike these methods, we explored a novel visual modality of keystroke dynamics for human identification using a single RGB-D sensor. In order to verify this idea, we created a dataset dubbed KD-MultiModal, which contains 243.2 K frames of RGB images and depth images, obtained by recording a video of hand typing with a single RGB-D sensor. The dataset comprises RGB-D image sequences of 20 subjects (10 males and 10 females) typing sentences, and each subject typed around 20 sentences. In the task, only the hand and keyboard region contributed to the person identification, so we also propose methods of extracting Regions of Interest (RoIs) for each type of data. Unlike the data of the key press or release, our dataset not only captures the velocity of pressing and releasing different keys and the typing style of specific keys or combinations of keys, but also contains rich information on the hand shape and posture. To verify the validity of our proposed data, we adopted deep neural networks to learn distinguishing features from different data representations, including RGB-KD-Net, D-KD-Net, and RGBD-KD-Net. Simultaneously, the sequence of point clouds also can be obtained from depth images given the intrinsic parameters of the RGB-D sensor, so we also studied the performance of human identification based on the point clouds. Extensive experimental results showed that our idea works and the performance of the proposed method based on RGB-D images is the best, which achieved 99.44% accuracy based on the unseen real-world data. To inspire more researchers and facilitate relevant studies, the proposed dataset will be publicly accessible together with the publication of this paper.

show abstract

Biometric Identification Based on Keystroke Dynamics

Cited by 10 publications

References 17 publications

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

Continuous Authentication in the Digital Age: An Analysis of Reinforcement Learning and Behavioral Biometrics

A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation

KD-Net: Continuous-Keystroke-Dynamics-Based Human Identification from RGB-D Image Sequences

Contact Info

Product

Resources

About