A novel non-linear transformation based multi-user identification algorithm for fixed text keystroke behavioral dynamics

• URL: http://arxiv.org/abs/2210.02505v1
• Date: Wed, 5 Oct 2022 18:42:32 GMT
• Title: A novel non-linear transformation based multi-user identification algorithm for fixed text keystroke behavioral dynamics
• Authors: Chinmay Sahu, Mahesh Banavar, Stephanie Schuckers
• Abstract summary: We propose a new technique to uniquely classify and identify multiple users accessing a single application using keystroke dynamics. Our results are validated with the help of benchmark keystroke datasets and show that our algorithm outperforms other methods.
• Score: 2.3848738964230023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a new technique to uniquely classify and identify multiple users accessing a single application using keystroke dynamics. This problem is usually encountered when multiple users have legitimate access to shared computers and accounts, where, at times, one user can inadvertently be logged in on another user's account. Since the login processes are usually bypassed at this stage, we rely on keystroke dynamics in order to tell users apart. Our algorithm uses the quantile transform and techniques from localization to classify and identify users. Specifically, we use an algorithm known as ordinal Unfolding based Localization (UNLOC), which uses only ordinal data obtained from comparing distance proxies, by "locating" users in a reduced PCA/Kernel-PCA/t-SNE space based on their typing patterns. Our results are validated with the help of benchmark keystroke datasets and show that our algorithm outperforms other methods.

Related papers

• User Strategization and Trustworthy Algorithms [81.82279667028423]
  We show that user strategization can actually help platforms in the short term. We then show that it corrupts platforms' data and ultimately hurts their ability to make counterfactual decisions.
  arXiv  Detail & Related papers  (2023-12-29T16:09:42Z)
• User-Level Differential Privacy With Few Examples Per User [73.81862394073308]
  We consider the example-scarce regime, where each user has only a few examples, and obtain the following results. For approximate-DP, we give a generic transformation of any item-level DP algorithm to a user-level DP algorithm. We present a simple technique for adapting the exponential mechanism [McSherry, Talwar FOCS 2007] to the user-level setting.
  arXiv  Detail & Related papers  (2023-09-21T21:51:55Z)
• Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs [75.40636935415601]
  Deep learning often faces the challenge of efficiently processing dynamic inputs, such as sensor data or user inputs. We take an incremental computing approach, looking to reuse calculations as the inputs change. We apply this approach to the transformers architecture, creating an efficient incremental inference algorithm with complexity proportional to the fraction of modified inputs.
  arXiv  Detail & Related papers  (2023-07-27T16:30:27Z)
• KeyDetect --Detection of anomalies and user based on Keystroke Dynamics [0.0]
  Cyber attacks can easily access sensitive data like credit card details and social security number. Currently to stop cyber attacks, various different methods are opted from using two-step verification methods. We are proposing a technique of using keystroke dynamics (typing pattern) of a user to authenticate the genuine user.
  arXiv  Detail & Related papers  (2023-04-08T09:00:07Z)
• An Empirical Evaluation of Federated Contextual Bandit Algorithms [27.275089644378376]
  Federated learning can be done using implicit signals generated as users interact with applications of interest. We develop variants of prominent contextual bandit algorithms from the centralized seting for the federated setting. Our experiments reveal the surprising effectiveness of the simple and commonly used softmax in balancing the well-know exploration-exploitation tradeoff.
  arXiv  Detail & Related papers  (2023-03-17T19:22:30Z)
• Resolving Uncertain Case Identifiers in Interaction Logs: A User Study [0.4014524824655105]
  We propose a neural network-based technique to determine a case notion for click data. We validate its efficacy through a user study based on the segmented event log resulting from interaction data of a mobility sharing company.
  arXiv  Detail & Related papers  (2022-11-21T16:13:04Z)
• The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm [154.47590401735323]
  Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems. This paper focuses on a challenging scenario where a user has multiple categories of interests. We propose a novel method called textitDiversity-Promoting Collaborative Metric Learning (DPCML)
  arXiv  Detail & Related papers  (2022-09-30T08:02:18Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)
• HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations [38.95985439093335]
  Existing work on detecting user disengagement typically requires hand-labeling many dialog samples. We propose HERALD, an efficient annotation framework that reframes the training data annotation process as a denoising problem. Our experiments show that HERALD improves annotation efficiency significantly and achieves 86% user disengagement detection accuracy in two dialog corpora.
  arXiv  Detail & Related papers  (2021-06-01T01:09:55Z)
• Quickest Intruder Detection for Multiple User Active Authentication [74.5256211285431]
  We formulate the Multiple-user Quickest Intruder Detection (MQID) algorithm. We extend the algorithm to the data-efficient scenario where intruder detection is carried out with fewer observation samples. We evaluate the effectiveness of the proposed method on two publicly available AA datasets on the face modality.
  arXiv  Detail & Related papers  (2020-06-21T21:59:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.