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A short review on Applications of Deep learning for Cyber security [article]

Mohammed Harun Babu R, Vinayakumar R, Soman KP
2019 arXiv   pre-print
This paper outlines the survey of all the works related to deep learning based solutions for various cyber security use cases.  ...  Deep learning is an advanced model of traditional machine learning. This has the capability to extract optimal feature representation from raw input samples.  ...  Embedding technique is used with RNN and hybrid CNN networks which helps in studying how to develop a shelter for web page content analysis from malicious URL's with faster web page response.  ... 
arXiv:1812.06292v2 fatcat:o7pcaf7xyncrpdn64byjxh47im

Detecting phishing websites using machine learning technique

Ashit Kumar Dutta, Zhihan Lv
2021 PLoS ONE  
There is a demand for an intelligent technique to protect users from the cyber-attacks. In this study, the author proposed a URL detection technique based on machine learning approaches.  ...  A recurrent neural network method is employed to detect phishing URL. Researcher evaluated the proposed method with 7900 malicious and 5800 legitimate sites, respectively.  ...  Therefore, the study proposes Recurrent Neural Network (RNN) based URL detection approach. The objectives of the study are as follows: 1.  ... 
doi:10.1371/journal.pone.0258361 pmid:34634081 pmcid:PMC8504731 fatcat:jjju4ifzvze5xg5laxdpt7qcaq

I Spy with My Little Eye: Analysis and Detection of Spying Browser Extensions [article]

Anupama Aggarwal, Bimal Viswanath, Saravana Kumar, Ayush Shah, Liang Zhang, Ponnurangam Kumaraguru
2018 arXiv   pre-print
Our RNN based detection scheme achieves a high precision (90.02%) and recall (93.31%) in detecting spying extensions.  ...  We show that using a Recurrent Neural Network (RNN), the sequences of browser API calls can be a robust feature, outperforming hand-crafted features (used in prior work on malicious extensions) to detect  ...  For better understanding, the API methods are color coded based on whether they are access, store or transmit methods.  ... 
arXiv:1612.00766v3 fatcat:lom52fhjcva6lgmygzk6nzibrq

Predicting the Dynamic Behaviour of Malware using RNN

2020 International Journal of Engineering and Advanced Technology  
Static analysis involves the inspection of the malicious code by observing the features such as file signatures, strings etc.  ...  Dynamic or behavioural data is more difficult to obfuscate as the malicious payload may have already been executed before it is detected.  ...  [5] proposes a method for the automatic detection of malicious and benign malware executables sing machine activity features such as CPU, RAM etc. Catak et al.  ... 
doi:10.35940/ijeat.c6291.029320 fatcat:n3bithgfvbfwzhm4dplioddcre

A Review on Malware Detection Methods

Jaishri M. Waghmare, Mayuri M. Chitmogrekar
2022 SAMRIDDHI A Journal of Physical Sciences Engineering and Technology  
This paper aims to conduct a brief and systematic survey on the malware detection methods based on the soft computing model.  ...  Recent methods for identifying malicious codes and threats have indicated less precision and deficient speeds.  ...  This rapid growth of malware and malicious code variants placed a challenge in cloud computing for malware detection.  ... 
doi:10.18090/samriddhi.v14i01.6 fatcat:wnon2fdtmbcptmkiahelm7mlse

Malicious Requests Detection with Improved Bidirectional Long Short-term Memory Neural Networks [article]

Wenhao Li, Bincheng Zhang, Jiajie Zhang
2020 arXiv   pre-print
To address the above issues, a more general and rigorous detection method is required.  ...  Experimental results on HTTP dataset CSIC 2010 have demonstrated the effectiveness of the proposed method when compared with the state-of-the-arts.  ...  Compared to repairing a large number of web application vulnerabilities, deploying a HTTP-based intrusion detection system is more efficient.  ... 
arXiv:2010.13285v4 fatcat:cam4ylx4k5cxjm4ivo33mgldea

A Novel Approach for Malicious URL Detection Based on the Joint Model

JianTing Yuan, YiPeng Liu, Long Yu, Leandros Maglaras
2021 Security and Communication Networks  
From the experimental results, the method proposed in this study improves the classification accuracy of malicious web page detection compared with other researchers.  ...  Therefore, the detection of malicious websites is a task that needs continuous development.  ...  Acknowledgments is work was supported by the Xinjiang Autonomous Region Key R&D Project (2021B01002), National Natural Science Foundation of China (U2003208), and CERNET Innovation Project (NGII20190412  ... 
doi:10.1155/2021/4917016 fatcat:vud3i33uhncwvjj6fm2ouszkwm

Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads

Yong Fang, Yijia Xu, Peng Jia, Cheng Huang
2020 Applied Sciences  
Email often contains a large amount of personal privacy information, possible business agreements, and sensitive attachments, which make emails a good target for hackers.  ...  One of the most common attack method used by hackers is email XSS (Cross-site scripting).  ...  Conflicts of Interest: The authors declare that there is no conflict of interest.  ... 
doi:10.3390/app10134425 fatcat:sv6ztlaicnhhpitmy6h75m7hie

Malicious URL detection based on a parallel neural joint model

Jianting Yuan, Guanxin Chen, Shengwei Tian, Xinjun Pei
2021 IEEE Access  
A parallel neural joint model algorithm is proposed for the analysis and detection of malicious Uniform Resource Locator (URL).  ...  Based on the experimental results, it is demonstrated that this algorithm has higher accuracy compared to the traditional algorithms.  ...  A system was proposed [20] known as Cujo for automatic detection and prevention of download attacks. It is embedded in a web proxy to inspect web pages and identify malicious code in pages.  ... 
doi:10.1109/access.2021.3049625 fatcat:a432cyjqrbha7agdl2egdq65ve

AI-IDS: Application of Deep Learning to Realtime Web Intrusion Detection

Aechan Kim, Mohyun Park, Dong Hoon Lee
2020 IEEE Access  
Given the rapid evolution of web-attacks, we implemented and applied our Artificial Intelligence-based Intrusion Detection System (AI-IDS).  ...  It also helps to write and improve Snort rules for signature-based IDS based on newly identified patterns.  ...  [10] devised CNN and RNN for intrusion detection, but it was different from the model of this study because it performed experiments each separated model in CNN and RNN.  ... 
doi:10.1109/access.2020.2986882 fatcat:6drnu4kdvzbvxclltnronwipjy

Long short‐term memory on abstract syntax tree for SQL injection detection

Z. Zhuo, T. Cai, X. Zhang, F. Lv
2021 IET Software  
SQL injection attack (SQLIA) is a code injection technique, used to attack data-driven applications by executing malicious SQL statements.  ...  Experimental results clearly illustrate the superior performance of our method compared to other existing works when detecting with complete SQL raw queries.  ...  ACKNOWLEDGEMENTS This work is supported by the Fundamental Research Funds for the Central Universities of China (No. JBK1806002).  ... 
doi:10.1049/sfw2.12018 fatcat:k7ek57p7yrca3agftqya4ttmay

Detecting Malicious URLs via a Keyword-based Convolutional Gated-recurrent-unit Neural Network

WenChuan Yang, Wen Zuo, BaoJiang Cui
2019 IEEE Access  
Considering that malicious keywords are unique to URLs, a feature representation method of URLs based on malicious keywords is proposed, and a GRU is used in place of the original pooling layer to perform  ...  This paper designs a convolutional gated-recurrent-unit (GRU) neural network for the detection of malicious URLs detection based on characters as text classification features.  ...  and other web attacks simply by embedding executable code or malicious code in URLs.  ... 
doi:10.1109/access.2019.2895751 fatcat:ifq3wfm46nd4pe6kqb3narltie

Malicious JavaScript Detection Based on Bidirectional LSTM Model

Xuyan Song, Chen Chen, Baojiang Cui, Junsong Fu
2020 Applied Sciences  
In this paper, we propose a novel deep learning-based method for malicious JavaScript detection.  ...  To solve this problem, many learning-based methods for malicious JavaScript detection are being explored.  ...  RQ2: How effective is the BLSTM neural network compared to other machine learning-based detection methods?  ... 
doi:10.3390/app10103440 fatcat:uwkcmh5pbvachg5te3vxqkpkrm

AMSI-Based Detection of Malicious PowerShell Code Using Contextual Embeddings [article]

Amir Rubin, Shay Kels, Danny Hendler
2019 arXiv   pre-print
In this work, we conduct the first study of malicious PowerShell code detection using the information made available by AMSI.  ...  This makes the problem of detecting malicious PowerShell code both urgent and challenging.  ...  In this work, we conduct the first study of malicious PowerShell code detection using the information made available by AMSI.  ... 
arXiv:1905.09538v2 fatcat:ppvympp3qncm7h3njvje4gjcsa

BLATTA: Early Exploit Detection on Network Traffic with Recurrent Neural Networks

Baskoro A. Pratomo, Pete Burnap, George Theodorakopoulos
2020 Security and Communication Networks  
Our recurrent neural network- (RNN-) based model is the first work to our knowledge that provides early prediction of malicious application layer messages, thus detecting a potential attack earlier than  ...  Previous works on deep packet inspection for detecting malicious traffic regularly read the full length of application layer messages.  ...  ShadowDaemon is commonly installed on a web server and intercepts HTTP requests before being processed by a web server software. It detects attacks based on its signature database.  ... 
doi:10.1155/2020/8826038 fatcat:if5gccqfavbjlnhspvqazhe2wa
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