Maintaining The Security In Internet Marketing: Moving From Biometrics To Behaviometrics

While it is a rather common business practice, Internet marketing is still an area that continues to evolve and adapt. One of the everlasting challenges associated with this field is being able to insure that the online transactions take place in a secure setting. This construct of security appears to be multidimensional since it can include issues associated with secure ordering, hacker protection, firewalls, identity theft, etc. While the privacy of the online consumers has to be protected, it is important for the marketers to identify the users on the Internet to collect a profile of their interests so that they can adjust their site contents accordingly and deliver advertisements that appeal to their specific preferences. Whether the ultimate purpose is to custom-tailor the online messages or offer appropriate product/service options, it is imperative that the identity of the online consumers needs to be authenticated to make sure that there is no security breach in completing the online marketing transactions. This paper reviews some of the ongoing efforts in preventing the potential intrusions in online practices

Security challenges and solutions for e-business

The advantages of economic growth and increasing ease of operation afforded by e-business and e-commerce developments are unfortunately matched by growth in cyber attacks. This paper outlines the common attacks faced by e-business and describes the defenses that can be used against them. It also reviews the development of newer security defense methods. These are: (1) biometrics for authentication; parallel processing to increase power and speed of defenses; (2) data mining and machine learning to identify attacks; (3) peer-to-peer security using blockchains; 4) enterprise security modelling and security as a service; and (5) user education and engagement. The review finds overall that one of the most prevalent dangers is social engineering in the form of phishing attacks. Recommended counteractions include education and training, and the development of new machine learning and data sharing approaches so that attacks can be quickly discovered and mitigated

Fulltime biometric mouse design for continuous authentication

As we embrace the information and communication technology in our everyday activities and day-to-day transactions, security concerns have increasingly come to light, especially in some of the critical areas of our society today such as education, health and commerce, where such security concerns are even higher. The need for complete and clear authentication and authorisation is of paramount importance. This paper explores andpresents the optimal use of full-time biometric mouse (FBM) for continuous authentication, which would not only enable authentication during log in and start of an application, but will enable continuous authentication throughout a transaction. We formulate a full-time biometric mouse (FBM) design that would ensure thumb positioning and its  ergonomics while ensuring comfort and maximum contact with the scanner to enable continuous authentication of the user in a speedy, easy and non-strenuous way. The mouse employs a simple algorithm that ensures quick operation to cut on possible delays and yet maintain the accuracy of the system

Machine Learning Models for Network Intrusion Detection and Authentication of Smart Phone Users

A thesis presented to the faculty of the Elmer R. Smith College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree of Master of Science by S. Sareh Ahmadi on November 18, 2019

ML-Based User Authentication Through Mouse Dynamics

Increasing reliance on digital services and the limitations of traditional authentication methods have necessitated the development of more advanced and secure user authentication methods. For user authentication and intrusion detection, mouse dynamics, a form of behavioral biometrics, offers a promising and non-invasive method. This paper presents a comprehensive study on ML-Based User Authentication Through Mouse Dynamics. This project proposes a novel framework integrating sophisticated techniques such as embeddings extraction using Transformer models with cutting-edge machine learning algorithms such as Recurrent Neural Networks (RNN). The project aims to accurately identify users based on their distinct mouse behavior and detect unauthorized access by utilizing the hybrid models. Using a mouse dynamics dataset, the proposed framework’s performance is evaluated, demonstrating its efficacy in accurately identifying users and detecting intrusions. In addition, a comparative analysis with existing methodologies is provided, highlighting the enhancements made by the proposed framework. This paper contributes to the development of more secure, reliable, and user-friendly authentication systems that leverage the power of machine learning and behavioral biometrics, ultimately augmenting the privacy and security of digital services and resources