Titlebook: Artificial Intelligence for Cybersecurity; Mark Stamp,Corrado Aaron Visaggio,Fabio Di Troia Book 2022 The Editor(s) (if applicable) and Th

事情

Fair Trial and Judicial Independencemics features collected from free-text. We implement and analyze a novel a deep learning model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU). We optimize the resulting model and consider several relevant related problems. Our model is competitive with the best results obtained in previous comparable research.

擋泥板

modest

anthesis

Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamicsned in related research. We find that models based on extreme gradient boosting (XGBoost) and multi-layer perceptrons (MLP) perform well in our experiments. Our best models outperform previous comparable research.

爆米花

Machine Learning-Based Analysis of Free-Text Keystroke Dynamicsmics features collected from free-text. We implement and analyze a novel a deep learning model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU). We optimize the resulting model and consider several relevant related problems. Our model is competitive with the best results obtained in previous comparable research.

吸氣

1568-2633 e AI field is tackling within the context of cybersecurity.T.This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniqu

Cerumen

debunk

Clickbait Detection for YouTube Videostly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the challenging problem of detecting clickbait YouTube videos. We experiment with multiple state-of-the-art machine learning techniques using a variety of textual features.

Alveoli

Generation of Adversarial Malware and Benign Examples Using Reinforcement Learningieving state-of-the-art results in many areas, it also has drawbacks exploited by many with white-box attacks. Although the white-box scenario is possible in malware detection, the detailed structure of antivirus is often unknown. Consequently, we focused on a pure black-box setup where no informati

Arable

Auxiliary-Classifier GAN for Malware Analysisd simultaneously. GANs have been used, for example, to successfully generate “deep fake” images. A recent trend in malware research consists of treating executables as images and employing image-based analysis techniques. In this research, we generate fake malware images using auxiliary classifier G