Skip to content
2000

Multi-Factor Authentication Protocol Based on Electrocardiography Signals for a Mobile Cloud Computing Environment

image of Multi-Factor Authentication Protocol Based on Electrocardiography Signals for a Mobile Cloud Computing Environment

Mobile Cloud Computing (MCC) is a highly complex topic that encompasses several information security issues. The authentication area of the various entities involved has been extensively discussed in recent years and shown a wide range of possibilities. The use of inadequate authentication processes leads to several problems, which range from financial damage to users or providers of Mobile Commerce (M-Commerce) services to the death of patients who depend on Mobile Healthcare (M-Health) services.<br><br>The design of reliable authentication processes that minimize such issues involves the use of non-intrusive authentication techniques and continuous authentication of users by MCC service providers. In this sense, biometrics may satisfy such needs in various scenarios.<br><br>This research has explored some conceptual bases and presents a continuous authentication protocol for MCC environments. Such a protocol is part of a cyberphysical system (CPS) and is based on the monitoring of physiological information interpreted from users’ electrocardiograms (ECG). Machine learning techniques based on the Adaptative Boost (Adaboost) and Random Undersampling Boost (RUSBoost) were used for the classification of the cardiac cycles recognized in such ECGs.<br><br>The two ML techniques applied to electrocardiography were compared by a random subsampling technique that considers four analysis metrics, namely accuracy, precision, sensitivity, and F1-score. The experimental results showed better performance of RUSBoost regarding accuracy (97.4%), precision (98.7%), sensitivity (96.1%), and F1- score (97.4%).<br>

/content/books/9789815050592.chapter-5
dcterms_subject,pub_keyword
-contentType:Journal
10
5
Chapter
content/books/9789815050592
Book
false
en
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test