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Found 11 papers, 1 papers with code
Software Repositories and Machine Learning Research in Cyber Security
In today's rapidly evolving technological landscape and advanced software development, the rise in cyber security attacks has become a pressing concern.
Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics
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%.
Leveraging Deep Learning Approaches for Deepfake Detection: A Review
Conspicuous progression in the field of machine learning and deep learning have led the jump of highly realistic fake media, these media oftentimes referred as deepfakes.
Machine Learning Based Approach to Recommend MITRE ATT&CK Framework for Software Requirements and Design Specifications
In this paper, we use machine learning algorithms to map requirements to the MITRE ATT&CK database and determine the accuracy of each mapping depending on the data split.
Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM
Social media is currently being used by many individuals online as a major source of information.
Using Deep Learning to Detecting Deepfakes
This study looks at various deepfake detection models that use deep learning algorithms to combat this looming threat.
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.
Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning
In this study, we aim to contribute to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral biometrics.
A Close Look into Human Activity Recognition Models using Deep Learning
Human activity recognition using deep learning techniques has become increasing popular because of its high effectivity with recognizing complex tasks, as well as being relatively low in costs compared to more traditional machine learning techniques.
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning
Results show promise for models trained strictly using limited sensor data collected from only smartphones and smartwatches coupled with traditional machine learning concepts and algorithms.
Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning
In recent years the amount of secure information being stored on mobile devices has grown exponentially.
