• Corpus ID: 56390787

A short review on Applications of Deep learning for Cyber security

  title={A short review on Applications of Deep learning for Cyber security},
  author={R MohammedHarunBabu and R. Vinayakumar and P. SomanK.},
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… 

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

Cyber security to detect, predict and respond to cyber threats in real time using machine learning and deep learning algorithms which spread across Information technology, operational technology, internet of things, control system, security systems, and the cloud in general.

Deep Learning Approach to DGA Classification for Effective Cyber Security

This research focuses on analyzing the traffic of botnets for the domain name determination to the IP address of the server, and the proposed algorithm is used to detect DGA which generates malicious domains randomly.

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.



Deep Learning for Network Flow Analysis and Malware Classification

The results obtained by applying deep learning techniques to classification of network protocols and applications using flow features and data signatures are presented and a similar classification of malware using their binary files is presented.

Classification of Android apps and malware using deep neural networks

  • R. NixJian Zhang
  • Computer Science
    2017 International Joint Conference on Neural Networks (IJCNN)
  • 2017
This work designs a Convolutional Neural Network for sequence classification and conducts a set of experiments on malware detection and categorization of software into functionality groups to test and compare it with classifications by recurrent neural network (LSTM), and significantly outperformed n-gram based methods.

Deep neural network based malware detection using two dimensional binary program features

A deep neural network based malware detection system that Invincea has developed is introduced, which achieves a usable detection rate at an extremely low false positive rate and scales to real world training example volumes on commodity hardware.

DroidDetector: Android Malware Characterization and Detection Using Deep Learning

An online deep-learning-based Android malware detection engine (DroidDetector) that can automatically detect whether an app is a malware or not is implemented and shows that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data.

Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security

DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS) and it is concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.

Android malware detection based on system call sequences and LSTM

A novel detection method based on deep learning is proposed to distinguish malware from trusted applications by treating one system call sequence as a sentence in the language and constructing a classifier based on the Long Short-Term Memory language model.

Autoencoder-based feature learning for cyber security applications

It is shown how well the AE is capable of automatically learning a reasonable notion of semantic similarity among input features, and how the scheme can reduce the dimensionality of the features thereby signicantly minimising the memory requirements.

Comparative Study of the Detection of Malicious URLs Using Shallow and Deep Networks

A comparative study between classical machine learning technique - logistic regression using bigram, deep learning techniques like convolution neural network and CNN long short-term memory as architectures used to detect malicious URLs shows CNN-LSTM gave the best accuracy for the classification of phishing URLs.

MtNet: A Multi-Task Neural Network for Dynamic Malware Classification

A new multi-task, deep learning architecture for malware classification for the binary i.e. malware versus benign malware classification task, which achieves a binary classification error rate of 0.358i¾?%, and for the first time, sees improvements using multiple layers in a deep neural network architecture for ransomware classification.

Deep learning LSTM based ransomware detection

An automated approach to extract API calls from the log of modified sandbox environment and detect ransomware behavior by employing Long-Short Term Memory networks for binary sequence classification of API calls is presented.