A Survey of Deep Learning Methods for Cyber Security
By Daniel S. Berman, Anna L. Buczak, Jeffrey S. Chavis, Cherita L. Corbett. Inf., 2019
510 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing
View Paper: https://www.semanticscholar.org/paper/0664e0a00a3cdeb4fd518d00b84abb5ccc3a1c57
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Gianluigi Rozza
- Francesco Piccialli
- Fabio Giampaolo
- Salvatore Cuomo
- Vincenzo Schiano Di Cola
- Vijay Janappa Reddi
- Jun Zhang
- Humaira Arshad
In Computer Networks and Communications
- Jun Zhang
- Maizar Raissi
- Surya Nepal
- Helge Janicke
- Vijay Janappa Reddi
- Ernesto Damiani
- Zhibo Zhang
- Fatma Taher
In Signal Processing
- Maizar Raissi
- Wei Luo
- Surya Nepal
- Helge Janicke
- Vijay Janappa Reddi
- Ernesto Damiani
- Lei Pan
- Zhibo Zhang
Citations in the corpus
- Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, background): “19 [2] 2019 √ √ ≈ √ N/A √ √ √ N/A N/A √”
- Deep Reinforcement Learning for Cyber Security (2019, methodology): “Among these ML applications, unsupervised and supervised learning methods have been used widely for intrusion detection [10]–[15], malware detection [16]–[19], cyber-physical attacks [20], [21], and data privacy protection [22].”
- Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition (2019, background): “This rapid growth of the Malware market is affecting the security of the computer industry [248-250].”
- Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next (2022, methodology): “Formore on these topic the reader can look at LeCun et al [92], Chen et al [29],Muhammad et al [117], Aldweesh et al [2], Berman et al [17], Calin [23]”
- A Survey of Android Malware Detection with Deep Neural Models (2020, background): “The breakthroughs of DL in image analysis [7], neural machine translation [8, 9, 13], and many other areas [68] have enabled DL to become the building block for many state-of-the-art artificial intelligence (AI)-based applications [69].”
- Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, background): “Our study and four works [13], [18], [19], [20] focus on deep learning approaches that are designed for cyber security intrusion detection.”