Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning

Mallet, Jacob; Pryor, Laura; Dave, Rushit; Seliya, Naeem; Vanamala, Mounika; Sowells-Boone, Evelyn

doi:10.1109/cacml55074.2022.00081

Cited by 15 publications

(6 citation statements)

References 23 publications

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“…Finally, with K-nearest-neighbors, he found that the speed of the algorithm is amazingly fast and easy to implement. However as other research corroborates, it is sensitive to irrelevant features and can be computationally expensive [16]. The information in the paper was particularly useful in our research due to providing information on what algorithms would be best when running these machine learning processes after we used topic modeling on our code.…”

Section: Literature Reviewmentioning

confidence: 82%

Software Engineering and Applications

2023

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Secure software engineering is crucial but can be time-consuming; therefore, methods that could expedite the identification of software weaknesses without reducing the process' efficacy would benefit the software engineering industry and thus benefit modern life. This research focuses on finding an efficient machine learning algorithm to identify software weaknesses from requirement specifications. The research uses the CWE repository and PROMISE_exp dataset for training. Keywords extracted using latent semantic analysis help map the CWE categories to PROMISE_exp. Naïve Bayes, support vector machine (SVM), decision trees, neural network, and convolutional neural network (CNN) algorithms were tested, with SVM and neural network producing reliable results. The research's unique contribution lies in the mapping technique and algorithm selection. It serves as a valuable reference for the secure software engineering community seeking to expedite the development lifecycle without compromising efficacy. Future work involves testing more algorithms, optimizing existing ones, and improving the training sets' accuracy.

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Section: Literature Reviewmentioning

confidence: 82%

Software Engineering and Applications

2023

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“…This lack of cybersecurity professionals is not limited to the government [16]. Additionally, automated systems such as authentication using machine learning are not enough to make up for this discrepancy [17], [18], [19]. Thus, considering the evident lack of cybersecurity professionals, it would be prudent for individuals to learn to protect themselves.…”

Section: Literature Reviewmentioning

confidence: 99%

Secure Arcade: A Gamified Defense Against Cyber Attacks

Loesch,

Hrastich,

Herbert

et al. 2023

IJCSIT

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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.

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“…Fortunately, many forgery detection methods have been proposed recently and most of them usually devise the forgery detection task as a true-false binary classification problem. The detection problem for DeepFake is usually modelled as an image-level classification problem, which is focused on discovering pattern differences between real and fake images [5][6][7][8]. While these methods achieve promising results in terms of detection accuracy, they are susceptible to adversarial examples.…”

Section: Introductionmentioning

confidence: 99%

DeepFake detection against adversarial examples based on D‐VAEGAN

Chen,

Xu,

2023

IET Image Processing

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Recent years, the development of DeepFake has raise a lot of security problems. Therefore, detection of DeepFake is critical. However, the existing DeepFake detection methods are often vulnerable to adversarial attacks, i.e. adding carefully crafted imperceptible perturbations into forged images is possible to evade detection. In this paper, a DeepFake detection method based on image denoising is proposed by combining variational autoencoder (VAE) and generative adversarial network (GAN), namely D‐VAEGAN. Firstly, an encoder is designed to extract the features of the image in a low‐dimensional latent space. Then, a decoder reconstructs the original clean image using the features in this low‐dimensional latent space. Secondly, an auxiliary discriminative network is introduced to further improve the performance of the model, which improves the quality of the reconstructed images. Furthermore, feature similarity loss is added as a penalty term to the reconstruction optimization function to improve the adversarial robustness. Experimental results on the FaceForensics++ dataset show that the proposed approach significantly outperforms the five adversarial training‐based defence methods. The approach achieves 96% in accuracy, which is on average about 50% higher than other comparison methods.

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Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning

Cited by 15 publications

References 23 publications

Software Engineering and Applications

Software Engineering and Applications

Secure Arcade: A Gamified Defense Against Cyber Attacks

DeepFake detection against adversarial examples based on D‐VAEGAN

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