Hands-on labs demonstrating HTML5 security Concerns

The research is focused on the new features added in HTML5 standard that have strong implications towards the overall information security of a system that uses this implementation.A Hands-on Lab is developed to demonstrate how Web Storage and the Geo-location API of HTML5 can affect the privacy of the user

Secure Arcade: A Gamified Defense Against Cyber Attacks

In modernity, we continually receive increasingly intricate technologies that allow us to increase our lives convenience and efficiency. Our technology, particularly technology available over the internet, is advancing at unprecedented speed. However, this speed of advancement allows those behind malicious attacks to have an increasingly easier time taking advantage of those who know little about computer security. Unfortunately, education in the computer security field is generally limited only to tertiary education. This research addresses this problem through a gamified web-based application that drives users to reach learning goals to help them become more vigilant internet users: 1. Learn and memorize general computer security terminology, 2. Become familiar with basic cryptography concepts, 3. Learn to recognize potential phishing scams via email quickly, and 4. Learn common attacks on servers and how to deal with them

Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics

The aim of this research paper is to look into the use of continuous authentication with mobile touch dynamics, using three different algorithms: Neural Network, Extreme Gradient Boosting, and Support Vector Machine. Mobile devices are constantly increasing in popularity in the world, today smartphone subscriptions have surpassed 6 billion. Mobile touch dynamics refer to the distinct patterns of how a user interacts with their mobile device, this includes factors such as touch pressure, swipe speed, and touch duration. Continuous authentication refers to the process of continuously verifying a user's identity while they are using a device, rather than just at the initial login. This research used a dataset of touch dynamics collected from 40 subjects using the LG V30+. The participants played four mobile games, PUBG, Diep.io, Slither, and Minecraft, for 10 minutes each game. The three algorithms were trained and tested on the extracted dataset, and their performance was evaluated based on metrics such as accuracy, precision, false negative rate, and false positive rate. The results of the research showed that all three algorithms were able to effectively classify users based on their individual touch dynamics, with accuracy ranging from 80% to 95%. The Neural Network algorithm performed the best, achieving the highest accuracy and precision scores, followed closely by XGBoost and SVC. The data shows that continuous authentication using mobile touch dynamics has the potential to be a useful method for enhancing security and reducing the risk of unauthorized access to personal devices. This research also notes the importance of choosing the correct algorithm for a given dataset and use case, as different algorithms may have varying levels of performance depending on the specific task

Deep Learning and Machine Learning, Better Together Than Apart: A Review on Biometrics Mobile Authentication

Throughout the past several decades, mobile devices have evolved in capability and popularity at growing rates while improvement in security has fallen behind. As smartphones now hold mass quantities of sensitive information from millions of people around the world, addressing this gap in security is crucial. Recently, researchers have experimented with behavioral and physiological biometrics-based authentication to improve mobile device security. Continuing the previous work in this field, this study identifies popular dynamics in behavioral and physiological smartphone authentication and aims to provide a comprehensive review of their performance with various deep learning and machine learning algorithms. We found that utilizing hybrid schemes with deep learning features and deep learning/machine learning classification can improve authentication performance. Throughout this paper, the benefits, limitations, and recommendations for future work will be discussed

Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication

Static authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still monitored in order to continuously verify that the user is not an imposter who had access to the user credentials. Mouse dynamics is the behavior of a users mouse movements and is a biometric that has shown great promise for continuous authentication schemes. This article builds upon our previous published work by evaluating our dataset of 40 users using three machine learning and deep learning algorithms. Two evaluation scenarios are considered: binary classifiers are used for user authentication, with the top performer being a 1-dimensional convolutional neural network with a peak average test accuracy of 85.73% across the top 10 users. Multi class classification is also examined using an artificial neural network which reaches an astounding peak accuracy of 92.48% the highest accuracy we have seen for any classifier on this dataset

Antibodies and antibody fragments are therapeutic tools in the treatment of type-II diabetes mellitus

Antibody fragments (FABs) are proteins that form part of the antigen recognition site. FABs are produced in genetically modified bacteriophages, bacteria, fungi, or plants and, consequently, can be produced in large quantities at a fraction of the cost of traditional antibodies. Antibody fragments are small and simple structure that today is highly regarded because of the many advantages they have over the use of whole antibodies. Single-domain antibodies are the smallest antigen-binding units of antibodies, consisting either only of one variable domain or one engineered constant domain that solely facilitates target binding. Fibroblast growth factor 21 (FGF21) is a promising drug candidate for the treatment of type 2 diabetes. Clinical use of recombinant fibroblast growth factor 21 (FGF21) for the treatment of type 2 diabetes and other disorders linked to obesity has been proposed; however, its clinical development has been challenging owing to its poor pharmacokinetics

Antibodies and Antibody Fragments Are Therapeutic Tools in the Treatment of Type-II Diabetes Mellitus

Antibody fragments (FABs) are proteins that form part of the antigen recognition site. FABs are produced in genetically modified bacteriophages, bacteria, fungi, or plants and, consequently, can be produced in large quantities at a fraction of the cost of traditional antibodies. Antibody fragments are small and simple structure that today is highly regarded because of the many advantages they have over the use of whole antibodies. Single-domain antibodies are the smallest antigen-binding units of antibodies, consisting either only of one variable domain or one engineered constant domain that solely facilitates target binding. Fibroblast growth factor 21 (FGF21) is a promising drug candidate for the treatment of type 2 diabetes. Clinical use of recombinant fibroblast growth factor 21 (FGF21) for the treatment of type 2 diabetes and other disorders linked to obesity has been proposed; however, its clinical development has been challenging owing to its poor pharmacokinetics