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

2020 ◽  
Vol 18 (2) ◽  
pp. 770
Author(s):  
Didih Rizki Chandranegara ◽  
Hardianto Wibowo ◽  
Agus Eko Minarno
Keyword(s):  
Manhattan Distance ◽  
Keystroke Dynamic ◽  
Dynamic Authentication

scholarly journals Keystroke Dynamic Authentication Using Combined MHR (Mean of Horner’s Rules) and Standard Deviation

2018 ◽  
pp. 13-18 ◽  
Author(s):  
Didih Rizki Chandranegara ◽  
Fauzi Dwi Setiawan Sumadi
Keyword(s):  
Standard Deviation ◽  
High Accuracy ◽  
Previous Method ◽  
Acceptance Rate ◽  
Biometric Authentication ◽  
False Acceptance Rate ◽  
False Acceptance ◽  
Keystroke Dynamic ◽  
Dynamic Authentication

Keystroke Dynamic Authentication used a behavior to authenticate the user and one of biometric authentication. The behavior used a typing speed a character on the keyboard and every user had a unique behavior in typing. To improve classification between user and attacker of Keystroke Dynamic Authentication in this research, we proposed a combination of MHR (Mean of Horner’s Rules) and standard deviation. The results of this research showed that our proposed method gave a high accuracy (93.872%) than the previous method (75.388% and 75.156%). This research gave an opportunity to implemented in real login system because our method gave the best results with False Acceptance Rate (FAR) is 0.113. The user can be used as a simple password and ignore a worrying about an account hacking in the system.

scholarly journals Analyzing the Effectiveness of Touch Keystroke Dynamic Authentication for the Arabic Language

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Suliman A. Alsuhibany ◽  
Afnan S. Almuqbil
Keyword(s):  
Arabic Language ◽  
Error Rates ◽  
Free Text ◽  
Classification Methods ◽  
Traditional Methods ◽  
Digital Devices ◽  
Android Platform ◽  
Authentication Technique ◽  
Keystroke Dynamic ◽  
Dynamic Authentication

The keystroke dynamic authentication (KDA) technique was proposed in the literature to develop a more effective authentication technique than traditional methods. KDA analyzes the rhythmic typing of the owner on a keypad or keyboard as a source of verification. In this study, we extend the findings of the system by analyzing the existing literature and validating its effectiveness in Arabic. In particular, we examined the effectiveness of the KDA system in Arabic for touchscreen-based digital devices using two KDA classes: fixed and free text. To this end, a KDA system was developed and applied to a selected device operating on the Android platform, and various classification methods were used to assess the similarity between log-in and enrolment sessions. The developed system was experimentally evaluated. The results showed that using Arabic KDA on touchscreen devices is possible and can enhance security. It attains a higher accuracy with average equal error rates of 0.0% and 0.08% by using the free text and fixed text classes, respectively, implying that free text is more secure than fixed text.

scholarly journals Graphical-Based Password Keystroke Dynamic Authentication System for Android Phone

IJARCCE ◽  
2016 ◽  
Vol 5 (12) ◽  
pp. 377-381
Author(s):  
Miss. Aarti Raman Sonawane ◽  
Prof. Kumbhar H. V.
Keyword(s):  
Authentication System ◽  
Keystroke Dynamic ◽  
Dynamic Authentication

scholarly journals Keystroke Dynamic Authentication in Mobile Cloud Computing

2014 ◽  
Vol 90 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Mahnoush Babaeizadeh ◽  
Majid Bakhtiari ◽  
Mohd Aizaini Maarof
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Keystroke Dynamic ◽  
Dynamic Authentication

Cloud-integrated wireless-optical broadband access network with survivability

2020 ◽  
Vol 10 ◽  
Author(s):  
Sangita Solanki ◽  
Raksha Upadhyay ◽  
Uma Rathore Bhatt
Keyword(s):  
Access Network ◽  
Access Networks ◽  
Manhattan Distance ◽  
Broadband Access ◽  
Optical Access Networks ◽  
Component Level ◽  
Backup Path ◽  
Wireless Router ◽  
Path Computation ◽  
Broadband Access Network

Cloud-integrated wireless optical broadband (CIW) access networks inheriting advantages of cloud computing, wireless and optical access networks have a broad prospect in the future. Due to failure of components like OLT level, ONU level, link or path failure and cloud component level in CIW, survivability is becoming one of the important issues. In this paper, we have presented cloud-integrated wireless-optical broadband access network with survivability using integer linear programming (ILP) model, to minimize the number of cloud components while providing maximum backup paths. Hence, we have proposed protection through cloud-integrated wireless router to available ONUs (PCIWRAO). So, evaluated the backup path computation. We have considered ONU level failure in which the affected traffic is transferred through wireless routers and cloud component to the available ONUs using Manhattan distance algorithm. Simulation results show different configurations for different number of routers and cloud components illustrating available backup path when ONU fails.

Hybrid Deep Neural Model for Duplicate Question Detection in Transliterated Bi-lingual Data

2019 ◽  
Vol 12 ◽  
Author(s):  
Seema Rani ◽  
Avadhesh Kumar ◽  
Naresh Kumar
Keyword(s):  
Question Answering ◽  
Neural Model ◽  
Manhattan Distance ◽  
Semantic Matching ◽  
English Only ◽  
Detection Model ◽  
Proposed Model ◽  
Reported Study ◽  
Social Media Platforms ◽  
Efficient Information

Background: Duplicate content often corrupts the filtering mechanism in online question answering. Moreover, as users are usually more comfortable conversing in their native language questions, transliteration adds to the challenges in detecting duplicate questions. This compromises with the response time and increases the answer overload. Thus, it has now become crucial to build clever, intelligent and semantic filters which semantically match linguistically disparate questions. Objective: Most of the research on duplicate question detection has been done on mono-lingual, majorly English Q&A platforms. The aim is to build a model which extends the cognitive capabilities of machines to interpret, comprehend and learn features for semantic matching in transliterated bi-lingual Hinglish (Hindi + English) data acquired from different Q&A platforms. Method: In the proposed DQDHinglish (Duplicate Question Detection) Model, firstly language transformation (transliteration & translation) is done to convert the bi-lingual transliterated question into a mono-lingual English only text. Next a hybrid of Siamese neural network containing two identical Long-term-Short-memory (LSTM) models and Multi-layer perceptron network is proposed to detect semantically similar question pairs. Manhattan distance function is used as the similarity measure. Result: A dataset was prepared by scrapping 100 question pairs from various social media platforms, such as Quora and TripAdvisor. The performance of the proposed model on the basis of accuracy and F-score. The proposed DQDHinglish achieves a validation accuracy of 82.40%. Conclusion: A deep neural model was introduced to find semantic match between English question and a Hinglish (Hindi + English) question such that similar intent questions can be combined to enable fast and efficient information processing and delivery. A dataset was created and the proposed model was evaluated on the basis of performance accuracy. To the best of our knowledge, this work is the first reported study on transliterated Hinglish semantic question matching.

Dynamic Authentication for Privacy Preserving Data

2016 ◽  
Vol 3 (1) ◽  
pp. 11-18
Author(s):  
Ravi Kumar T ◽  
◽  
Y Praveen Kumar ◽  
Keyword(s):  
Privacy Preserving ◽  
Dynamic Authentication
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