A Survey of Deep Learning Methods for Cyber Security
TLDR
This survey paper describes a literature review of deep learning methods for cyber security applications, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others.Abstract:
This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.read more
Citations
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Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
TL;DR: A survey of deep learning approaches for cyber security intrusion detection, the datasets used, and a comparative study to evaluate the efficiency of several methods are presented.
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Cybersecurity data science: an overview from machine learning perspective
Iqbal H. Sarker,Iqbal H. Sarker,A. S. M. Kayes,Shahriar Badsha,Hamed Alqahtani,Paul A. Watters,Alex Hay-Man Ng +6 more
TL;DR: This paper focuses and briefly discusses on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions.
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Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition
Oludare Isaac Abiodun,Muhammad Ubale Kiru,Aman Jantan,Abiodun Esther Omolara,Kemi Victoria Dada,Abubakar Malah Umar,Okafor Uchenwa Linus,Humaira Arshad,Abdullahi Aminu Kazaure,Usman M. Gana +9 more
TL;DR: There is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems and the research focus on current models and the development of new models concurrently for more successes in the field.
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Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
Salvatore Cuomo,Vincenzo Schiano di Cola,Fabio Giampaolo,Gianluigi Rozza,Maizar Raissi,Francesco Piccialli +5 more
TL;DR: A comprehensive review of the literature on physics-informed neural networks can be found in this article , where the primary goal of the study was to characterize these networks and their related advantages and disadvantages, as well as incorporate publications on a broader range of collocation-based physics informed neural networks.
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Deep learning and big data technologies for IoT security
Mohamed Ahzam Amanullah,Riyaz Ahamed Ariyaluran Habeeb,Riyaz Ahamed Ariyaluran Habeeb,Fariza Hanum Nasaruddin,Abdullah Gani,Abdullah Gani,Ejaz Ahmed,Abdul Salam Mohamed Nainar,Nazihah Md Akim,Muhammad Imran +9 more
TL;DR: A comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies is conducted and a thematic taxonomy is derived from the comparative analysis of technical studies of the three aforementioned domains.
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