Titlebook: Malware Analysis Using Artificial Intelligence and Deep Learning; Mark Stamp,Mamoun Alazab,Andrii Shalaginov Book 2021 The Editor(s) (if a

JOT

Book 2021ctical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases..

Abrade

A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classificationhat we can obtain better classification accuracy based on these feature embeddings, as compared to HMM experiments that directly use the opcode sequences, and serve to establish a baseline. These results show that word embeddings can be a useful feature engineering step in the field of malware analysis.

Density

tools, frameworks and techniques to enable readers to implem.?This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI a

Priapism

Ige326

An Empirical Analysis of Image-Based Learning Techniques for Malware Classification work, the results presented in this chapter are based on a larger and more diverse malware dataset, we consider a wider array of features, and we experiment with a much greater variety of learning techniques. Consequently, our results are the most comprehensive and complete that have yet been published.

乞討

https://doi.org/10.1007/978-3-030-62582-5Malware identification and analysis; Intrusion detection; Computer forensics; Spam detection; Phishing d

sacrum

Mark Stamp,Mamoun Alazab,Andrii ShalaginovExplores how deep learning and artificial intelligence can effectively be used in malware detection and analysis.Showcases state-of-the-art tools, frameworks and techniques to enable readers to implem

BRACE

MAL

A Selective Survey of Deep Learning Techniques and Their Application to Malware Analysisluding multilayer perceptrons (MLP), convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), residual networks (ResNet), generative adversarial networks (GAN), and Word2Vec. We provide a selective survey of applications of each of these architectures to malware-related problems.

candle

Deep Learning Techniques for Behavioral Malware Analysis in Cloud IaaSThis chapter focuses on online malware detection techniques in cloud IaaS using machine learning and discusses comparative analysis on the performance metrics of various deep learning models.