Advances in the Keystroke Dynamics: The Practical Impact of Database Quality

Rybnik, Mariusz; Panasiuk, Piotr; Saeed, Khalid; Rogowski, Marcin

doi:10.1007/978-3-642-33260-9_17

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…There are also some disadvantages of keystroke biometrics: (i) efficient interpretation of features can be problematic; (ii) limitations of devices, operating systems and communication protocols can affect the data quality. This paper is the continuation of an earlier conference paper [4], with addition of touchscreen devices issues.…”

Section: Introductionmentioning

confidence: 92%

The impact of database quality on keystroke dynamics authentication

Panasiuk

Rybnik

Saeed

et al. 2016

AIP Conference Proceedings

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CitationPanasiuk P, Rybnik M, Saeed K, Rogowski M (2016) The impact of database quality on keystroke dynamics authentication. Abstract. This paper concerns keystroke dynamics, also partially in the context of touchscreen devices. The authors concentrate on the impact of database quality and propose their algorithm to test database quality issues. The algorithm is used on their own as well as the well-known . Following specific problems were researched: classification accuracy, development of user typing proficiency, time precision during sample acquisition, representativeness of training set, sample length.

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Section: Introductionmentioning

confidence: 92%

The impact of database quality on keystroke dynamics authentication

Panasiuk

Rybnik

Saeed

et al. 2016

AIP Conference Proceedings

Self Cite

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“…They also developed an instant classifier in the direction of construe folder access patterns to classify zero-day assaults appropriately. [18] Used API calls to create an autoencoder-based classifier and test multiple categorization layers. RNNs were integrated with MLP as well as logistic regression for classification in [19], which established a technique for detecting malware that employs RNNs mixed with MLP and LR.…”

Section: Related Workmentioning

confidence: 99%

ShChain_3D-ResNet: Sharding Blockchain with 3D-Residual Network (3D-ResNet) Deep Learning Model for Classifying DDoS Attack in Software Defined Network

Fenil

Kumar²

2022

Symmetry

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The distributed denial of service (DDoS) vulnerabilities have rapidly extended and have been given different possibilities for even more advanced assaults on specific targets in recent times, thanks to the growth of innovative technology such as the Internet of Things (IoT) and Software-Defined Networking (SDN). The attack patterns route comprises unprotected and susceptible IoT systems that are internet-connected, as well as denial of service weaknesses in the SDN controllers, such as southbound connection exhaustion. (1) Background: The review does not go into detail about the symmetry blockchain approaches used to mitigate DDoS attacks, nor does it classify them in IoT; (2) To overcome the privacy issues, a novel deep learning-based privacy preservation method was proposed named ShChain_3D-ResNet. This novel method combines Sharding, blockchain and Residual Network for securing the SDN. Under this network, the proposed efficient attention module jointly learns attention to enforce the symmetry on weights for various channels in spatial dimension as well as attention weights of multiple frames in temporal dimension assistance of pre-training, updating, and dense convolution process; (3) Results: the proposed ShChain_3D-ResNet achieves 95.6% of accuracy, 97.3% of precision, 95.2% of recall, 94.4% of F1-score, 32.5 ms of encryption time and 35.2 ms of decryption time for dataset-1. Further, it achieves 97.3% accuracy, 95.3% precision, 96.1% recall, 98.2% F1-score, 32.1 ms of encryption time, and 36.2 ms of decryption time for dataset-2; (4) Conclusions: The Sharding strategy can increase ShChain performance while simultaneously utilizing Multi User (MU) resources for SDN.

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“…KDA is one of the Biometric Authentication techniques. Biometric Authentication utilizes something unique from users such as the face, fingerprints, and habits (in this case KDA) [3,6,13]. And every person face, fingerprints, and habits can not be imitated by others (one of the habits is typing characters using the keyboard or KDA).…”

Section: Introductionmentioning

confidence: 99%

Combined scaled manhattan distance and mean of horner’s rules for keystroke dynamic authentication

2020

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Account security was determined by how well the security techniques applied by the system were used. There had been many security methods that guaranteed the security of their accounts, one of which was Keystroke Dynamic Authentication. Keystroke Dynamic Authentication was an authentication technique that utilized the typing habits of a person as a security measurement tool for the user account. From several research, the average use in the Keystroke Dynamic Authentication classification is not suitable, because a user's typing speed will change over time, maybe faster or slower depending on certain conditions. So, in this research, we proposed a combination of the Scaled Manhattan Distance method and the Mean of Horner's Rules as a classification method between the user and attacker against the Keystroke Dynamic Authentication. The reason for using Mean of Horner's Rules can adapt to changes in values over time and based on the results can improve the accuracy of the previous method.

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Advances in the Keystroke Dynamics: The Practical Impact of Database Quality

Cited by 11 publications

References 17 publications

The impact of database quality on keystroke dynamics authentication

The impact of database quality on keystroke dynamics authentication

ShChain_3D-ResNet: Sharding Blockchain with 3D-Residual Network (3D-ResNet) Deep Learning Model for Classifying DDoS Attack in Software Defined Network

Combined scaled manhattan distance and mean of horner’s rules for keystroke dynamic authentication

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