Your Device May Know You Better Than You Know Yourself- Continuous Authentication on Novel Dataset Using Machine Learning

Pedro Gomes do Nascimento, Pidge Witiak, Tucker MacCallum, Zachary Winterfeldt, Rushit Dave, Department of Computer Information Science, Minnesota State University, Mankato, Mankato, USA

ABSTRACT
This research aims to further understanding in the field of continuous authentication using behavioural biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch dynamics can effectively distinguish users. However, further studies are needed to make it viable option for authentication systems. You can access our dataset at the following link:https://github.com/AuthenTech2023/authentech-repo

KEYWORDS
Continuous Authentication, Machine Learning, Minecraft, Novel Dataset, Touch Gestures

From Clicks to Security: Investigating Continuous Authentication via Mouse Dynamics

Rushit Dave, Marcho Handoko, Ali Rashid, Cole Schoenbauer, Minnesota State University, USA

ABSTRACT
In the realm of computer security, the importance of efficient and reliable user authentication methods has become increasingly critical. This paper examines the potential of mouse movement dynamics as a consistent metric for continuous authentication. By analysing user mouse movement patterns in two contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond conventional methodologies by employing a range of machine learning models. These models are carefully selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as reflected in their mouse movements. This multifaceted approach allows for a more nuanced and comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine learning models employed in this study demonstrate competent performance in user verification, marking an improvement over previous methods used in this field. This research contributes to the ongoing efforts to enhance computer security and highlights the potential of leveraging user behavior, specifically mouse dynamics, in developing robust authentication systems.

KEYWORDS
Continuous Authentication, Machine Learning, Mouse Dynamics

From Image Segmentation and Classification Using Neural Networks

Fatema Tuj Zohra, Rifa Tasfia Ratri, Shaheena Sultana, and Humayara Binte Rashid, Notre Dame University Bangladesh

ABSTRACT
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable of learning complex features directly from images and achieving outstanding performance across several datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection methods for pre-processing, and K-means clustering have been applied to segment the images. Image augmentation improves the size and diversity of datasets for training the models for image classification. This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that provides insights into the selection of pre-trained models and hyper parameters for optimal performance. We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such as CNN and VGG 16 for classification.

KEYWORDS
Convolutional Neural Network, VGG 16, Image Segmentation, K-means, Image Classification.

Exploring the Ev Charging Ecosystem and Performing an Experimental Assessment of Its Cloud and Mobile Application Infrastructure Security

Pooja Patil, Sara Acikkol Dogan, Samir Tout, and Ranu Parmar, Michigan University, USA

ABSTRACT
The security of Electric Vehicle (EV) charging has gained momentum after the increase in the EV adoption in the past few years. Mobile applications have been integrated into EV charging systems that mainly use a cloud-based platform to host their services and data. Like many complex systems, cloud systems are susceptible to cyberattacks if proper measures are not taken by the organization to secure them. In this paper, we explore the security of key components in the EV charging infrastructure, including the mobile application and its cloud service. We conducted an experiment that initiated a Man in the Middle attack between an EV app and its cloud services. Our results showed that it is possible to launch attacks against the connected infrastructure by taking advantage of vulnerabilities that may have substantial economic and operational ramifications on the EV charging ecosystem. We conclude by providing mitigation suggestions and future research directions.

KEYWORDS
Cloud infrastructure, Electric vehicle charging, Security analysis, Mitigation techniques, mobile application./p>