Long short-term memory-based Malware classification method for information security

2019 ◽  
Vol 77 ◽  
pp. 366-375 ◽  
Author(s):  
Jungho Kang ◽  
Sejun Jang ◽  
Shuyu Li ◽  
Young-Sik Jeong ◽  
Yunsick Sung
Keyword(s):  
Information Security ◽  
Short Term Memory ◽  
Classification Method ◽  
Short Term ◽  
Term Memory ◽  
Malware Classification ◽  
Long Short Term Memory

scholarly journals DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism

2019 ◽  
Vol 9 (20) ◽  
pp. 4205 ◽  
Author(s):  
Yanchen Qiao ◽  
Bin Zhang ◽  
Weizhe Zhang ◽  
Arun Kumar Sangaiah ◽  
Hualong Wu
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Classification Method ◽  
Short Term ◽  
Domain Name ◽  
Term Memory ◽  
General Data Protection Regulation ◽  
Domain Names ◽  
Long Short Term Memory ◽  
And Control

Currently, many cyberattacks use the Domain Generation Algorithm (DGA) to generate random domain names, so as to maintain communication with the Communication and Control (C&C) server. Discovering DGA domain names in advance could help to detect attacks and response in time. However, in recent years, the General Data Protection Regulation (GDPR) has been promulgated and implemented, and the method of DGA classification based on the context information, such as the WHOIS (the information about the registered users or assignees of the domain name) , is no longer applicable. At the same time, acquiring the DGA algorithm by reversing malware samples encounters the problem of no malware samples for various reasons, such as fileless malware. We propose a DGA domain name classification method based on Long Short-Term Memory (LSTM) with attention mechanism. This method is oriented to the character sequence of the domain name, and it uses the LSTM combined with attention mechanism to construct the DGA domain name classifier to achieve the rapid classification of domain names. The experimental results show that the method has a good classification result.

scholarly journals Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3643
Author(s):  
Sora Hayashi ◽  
Kenshi Saho ◽  
Keitaro Shioiri ◽  
Masahiro Fujimoto ◽  
Masao Masugi
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Doppler Radar ◽  
Classification Method ◽  
Elderly Adults ◽  
Short Term ◽  
Term Memory ◽  
Daily Monitoring ◽  
Long Short Term Memory ◽  
Leg Motion

To develop a daily monitoring system for early detection of fall risk of elderly people during walking, this study presents a highly accurate micro-Doppler radar (MDR)-based gait classification method for the young and elderly adults. Our method utilizes a time-series of velocity corresponding to leg motion during walking extracted from the MDR spectrogram (time-velocity distribution) in an experimental study involving 300 participants. The extracted time-series was inputted to a long short-term memory recurrent neural network to classify the gaits of young and elderly participant groups. We achieved a classification accuracy of 94.9%, which is significantly higher than that of a previously presented velocity-parameter-based classification method.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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