Non-conventional keystroke dynamics for user authentication

This paper introduces an approach for user authentication using free-text keystroke dynamics which incorporates the use of non-conventional keystroke features. Semi-timing features along with editing features are extracted from the user’s typing stream. Decision trees were exploited to classify each of the user’s data. In parallel for comparison, support vector machines (SVMs) were also used for classification in association with an ant colony optimization (ACO) feature selection technique. The results obtained from this study are encouraging as low false accept rates (FAR) and false reject rates (FRR) were achieved in the experimentation phase. This signifies that satisfactory overall system performance was achieved by using the typing attributes in the proposed approach. Thus, the use of non-conventional typing features improves the understanding of human typing behavior and therefore, provides significant contribution to the authentication system

Literature Survey on Keystroke Dynamics for User Authentication

Behavioural biometrics is the field of study related to the measure of uniquely identifying and measuring the patterns in human activities. Computer security plays a vital role as most of the sensitive data is stored on computers. Keystrokes Dynamics is a technique based on human behaviour for typing the password. Whenever any user logins into the system, username and password combinations are used for authenticating the users. The username is not secret, and the imposter acts as user to guess the password also because of simplicity of password, the system is prone to more attacks. In this case biometrics provide secure and convenient authentication. Our system uses a Support Vector Machine (SVM) which is one of the best known classifications and regression algorithm. Support Vectors (SV) that fall under different regions is separated using hyper planes linear as well as non-linear. Researchers have proved that SVM will converge to the best possible solution in very less time

Behavioural Based Biometrics Using Keystroke Dynamics for User Authentication

Security of data in recent times has become paramount, which has led to the development of many security systems. Among such systems is keystroke dynamics. Keystroke dynamics has become an active area of research in recent times. This is due, in part, to the increased importance of cyber-security, computer or network access control. Also known as typing dynamics, keystroke refers to a method that identifies users/individuals based on the manner of their typing pattern or rhythm on the keyboard, which could either mean a user is verified (identified) or authenticated. User identification is a critical factor before authentication. Now with a person already identified, the next step is to authenticate. Even if the user types in a correct password, that does not mean that the user is whom they say they are. The focus of this thesis is on the dynamic approach of keystrokes. In this work, we propose a method with improves our classification algorithm. We introduce a method that uses the minimum redundancy maximum relevance feature selection method which selects the best features based on the relevance and redundancy. We have also used several classifiers that include vector machine, k-nearest neighbour, Naive Bayes and grid search for optimizing the support vector machine. The results not only show the efficiency of our method but also show that the proposed method can be applied to other datasets to produce optimal results

Analysis of Cloud Based Keystroke Dynamics for Behavioral Biometrics Using Multiclass Machine Learning

With the rapid proliferation of interconnected devices and the exponential growth of data stored in the cloud, the potential attack surface for cybercriminals expands significantly. Behavioral biometrics provide an additional layer of security by enabling continuous authentication and real-time monitoring. Its continuous and dynamic nature offers enhanced security, as it analyzes an individual's unique behavioral patterns in real-time. In this study, we utilized a dataset consisting of 90 users' attempts to type the 11-character string 'Exponential' eight times. Each attempt was recorded in the cloud with timestamps for key press and release events, aligned with the initial key press. The objective was to explore the potential of keystroke dynamics for user authentication. Various features were extracted from the dataset, categorized into tiers. Tier-0 features included key-press time and key-release time, while Tier-1 derived features encompassed durations, latencies, and digraphs. Additionally, Tier-2 statistical measures such as maximum, minimum, and mean values were calculated. The performance of three popular multiclass machine learning models, namely Decision Tree, Multi-layer Perceptron, and LightGBM, was evaluated using these features. The results indicated that incorporating Tier-1 and Tier-2 features significantly improved the models' performance compared to relying solely on Tier-0 features. The inclusion of Tier-1 and Tier-2 features allows the models to capture more nuanced patterns and relationships in the keystroke data. While Decision Trees provide a baseline, Multi-layer Perceptron and LightGBM outperform them by effectively capturing complex relationships. Particularly, LightGBM excels in leveraging information from all features, resulting in the highest level of explanatory power and prediction accuracy. This highlights the importance of capturing both local and higher-level patterns in keystroke data to accurately authenticate users

Keystroke dynamics based user authentication using deep multilayer perceptron

User authentication is an essential factor to protect digital service and prevent malicious users from gaining access to the system. As Single Factor Authentication (SFA) is less secure, organizations started to utilize Multi-Factor Authentication (MFA) to provide reliable protection by using two or more identification measures. Keystroke dynamics is a behavioral biometric, which analyses users typing rhythm to identify the legitimacy of the subject accessing the system. Keystroke dynamics that have a low implementation cost and does not require additional hardware in the authentication process since the collection of typing data is relatively simple as it does not require extra effort from the user. This study aims to propose deep learning model using Multilayer Perceptron (MLP) in keystroke dynamics for user authentication on CMU benchmark dataset. The user typing rhythm from 51 subjects collected based on the static password (.tie5Roanl) typed 400 times over 8 sessions and 50 repetitions per session. The MLP achieved optimum EER of 4.45% compared to original benchmark classifiers such as 9.6% (scaled Manhattan), 9.96% (Mahalanobis Nearest Neighbor), 10.22% (Outlier Count), 10.25% and 16.14% (Neural Network Auto-Assoc). © 2020 by the authors

Score Normalization for Keystroke Dynamics Biometrics

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A. Morales, E. Luna-Garcia, J. Fierrez and J. Ortega-Garcia, "Score normalization for keystroke dynamics biometrics," Security Technology (ICCST), 2015 International Carnahan Conference on, Taipei, 2015, pp. 223-228. doi: 10.1109/CCST.2015.7389686This paper analyzes score normalization for keystroke dynamics authentication systems. Previous studies have shown that the performance of behavioral biometric recognition systems (e.g. voice and signature) can be largely improved with score normalization and target-dependent techniques. The main objective of this work is twofold: i) to analyze the effects of different thresholding techniques in 4 different keystroke dynamics recognition systems for real operational scenarios; and ii) to improve the performance of keystroke dynamics on the basis of target-dependent score normalization techniques. The experiments included in this work are worked out over the keystroke pattern of 114 users from two different publicly available databases. The experiments show that there is large room for improvements in keystroke dynamic systems. The results suggest that score normalization techniques can be used to improve the performance of keystroke dynamics systems in more than 20%. These results encourage researchers to explore this research line to further improve the performance of these systems in real operational environments.A.M. is supported by a post-doctoral Juan de la Cierva contract by the Spanish MECD (JCI-2012-12357). This work has been partially supported by projects: Bio-Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK and Cátedra UAM Telefónica

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

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

Study of Adverse Health Effects Due to Mobile Tower Radiation situated in Densely Populated Residential Areas

Use of mobiles is increased tremendously in recent years; therefore to cover these mobile subscribers it is essential to increase the number of base stations. Nowadays these base stations are located in intensely populated areas. People around these base stations will get exposed to electromagnetic field (EMF) exposure which is radiated from base stations. This EMF exposure is called as non-ionizing radiation. Non-ionizing radiation has some adverse health effects. People are not aware of it in India. Power density is measured in nearby areas of mobile tower base stations which are located in densely populated areas. The places selected are Pachgaon, Kandalgaon, Kasba bawada, Tarabai Park, Mahalaxmi Nagar, RK Nagar, and Jarag Nagar in Kolhapur. The EMF exposure is measured in terms of power densities and electric field which were well below the international standard ICNIRP and national standard DoT. A questionnaire was prepared to find the different adverse health effects faced by the people living around the base stations. Different health symptoms of electromagnetic field exposure faced by the people within 20m, 50m and 150m are analyzed. Symptoms such as a headache and sleep disorder, daytime sleepiness, depression and memory changes were found in the study