• Corpus ID: 56390787

A short review on Applications of Deep learning for Cyber security

@article{MohammedHarunBabu2018ASR,
  title={A short review on Applications of Deep learning for Cyber security},
  author={R MohammedHarunBabu and R. Vinayakumar and P. SomanK.},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.06292}
}
Deep learning is an advanced model of traditional machine learning. This has the capability to extract optimal feature representation from raw input samples. This has been applied towards various use cases in cyber security such as intrusion detection, malware classification, android malware detection, spam and phishing detection and binary analysis. This paper outlines the survey of all the works related to deep learning based solutions for various cyber security use cases. Keywords: Deep… 
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18 Citations
Highly Influential Citations
1
Background Citations
6
Methods Citations
1

18 Citations

Neural Networks and Deep Learning in Cyber Security

A brief overview of artificial neural networks and some examples of deep learning based solutions in cyber security.

A study on deep learning approaches over Malware detection

  • P. KavithaB. Muruganantham
  • Computer Science
    2020 IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE)
  • 2020
This survey provides a survey on deep learning algorithms applied on detection of infection and presents a brief report on Deep learning methodologies and malware detection.

Machine Learning Algorithms Applied to System Security: A Systematic Review

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Deep Learning Approach to DGA Classification for Effective Cyber Security

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Predicting the Impact of Android Malicious Samples via Machine Learning

This paper proposes a light-weight solution to automatically identify the Android malicious samples with high security and privacy impact and trains highly accurate support vector machine and deep neural network classifiers to categorize the candidateAndroid malicious samples into low impact or high impact.

Preventing Data Poisoning Attacks By Using Generative Models

This study has conducted data poisoning attacks on MNIST, a widely used character detection data set, and built classification models more reliable by using a generative model such as AutoEncoder.

Hybrid Deep Learning Model for Real-Time Detection of Distributed Denial of Service Attacks in Software Defined Networks

This paper proposed the hybrid DL model that utilises Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for DDoS attack detection that produced a detection accuracy of 99.72%.

On detecting and mitigating phishing attacks through featureless machine learning techniques

This work proposes PhishKiller, a tool capable of detecting and mitigating phishing attacks by means a proxy approach employed to intercept user‐accessed addresses, and featureless machine learning techniques to classify URLs.

Applications of deep learning for phishing detection: a systematic literature review

A systematic literature review (SLR) is performed to identify, assess, and synthesize the results on deep learning approaches for phishing detection as reported by the selected scientific publications and provides an overview of how deep learning algorithms have been used forphishing detection from several aspects.

Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research Directions

This study provides an ample list of future directions which will pave the way for future research in ransomware detection utilizing machine learning, deep learning, and blend of both techniques while capitalizing on the advantages of dynamic analysis for the ransomware detection.

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