Ordinal UNLOC: Target Localization With Noisy and Incomplete Distance Measures

Banavar, Mahesh K.; Wickramasinghe, Shandeepa; Achalla, Monalisa; Sun, Jie

doi:10.1109/jiot.2021.3078331

Cited by 6 publications

(2 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ordinal UNLOC [46] is then used to perform two tasks: (1) the keystroke data is mapped from the keystroke space to the reduced dimension space using function mapping; and (2) the "location" of the unknown user in the reduced feature space is estimated. Using the nearest neighbor rule, this estimate is mapped to one of the known users, and a classification decision is made.…”

Section: Testingmentioning

confidence: 99%

“…Ordinal Unfolding based localization (in short, Ordinal UNLOC) [46] is a target localization technique where we do not have reliable distance measurements between components in the system (nodes, transmit-receive pairs, etc.). The estimation technique utilizes rank aggregation, function learning, and proximity-based unfolding optimization, and as a result, it yields accurate target localization for common transmission models with unknown parameters and noisy observations that are reminiscent of practical settings [29].…”

Section: Ordinal Unfolding Based Localizationmentioning

confidence: 99%

See 1 more Smart Citation

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

Sahu¹,

Banavar²,

Schuckers³

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Testingmentioning

confidence: 99%

Section: Ordinal Unfolding Based Localizationmentioning

confidence: 99%