A comprehensive survey of databases and deep learning methods for cybersecurity and intrusion detection systems
This survey presents a comprehensive overview of machine learning methods for
cybersecurity intrusion detection systems, with a specific focus on recent approaches based …
cybersecurity intrusion detection systems, with a specific focus on recent approaches based …
Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review
Globally, the external internet is increasingly being connected to industrial control systems.
As a result, there is an immediate need to protect these networks from a variety of threats …
As a result, there is an immediate need to protect these networks from a variety of threats …
An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges
In many machine learning tasks, learning a good representation of the data can be the key
to building a well-performant solution. This is because most learning algorithms operate with …
to building a well-performant solution. This is because most learning algorithms operate with …
Deep unsupervised multi-modal fusion network for detecting driver distraction
Y Zhang, Y Chen, C Gao - Neurocomputing, 2021 - Elsevier
The risk of incurring a road traffic crash has increased year by year. Studies show that lack of
attention during driving is one of the major causes of traffic accidents. In this work, in order to …
attention during driving is one of the major causes of traffic accidents. In this work, in order to …
Deep learning-based intrusion detection system for electric vehicle charging station
The integration of the open communication layer to the physical layer of the power grids
facilitates bidirectional communication, automation, remote control, distributed, and …
facilitates bidirectional communication, automation, remote control, distributed, and …
[PDF][PDF] Deep transfer learning applications in intrusion detection systems: A comprehensive review
Globally, the external Internet is increasingly being connected to the contemporary industrial
control system. As a result, there is an immediate need to protect the network from several …
control system. As a result, there is an immediate need to protect the network from several …
基于LSTM 与改进残差网络优化的异常流量检测方法
麻文刚， 张亚东， 郭进 - 通信学报, 2021 - infocomm-journal.com
传统的网络异常流量检测方法往往存在特征选择差与泛化能力较弱等缺陷, 导致检测精度较低.
为此, 提出了一种基于长短记忆网络(LSTM) 与改进残差神经网络优化的异常流量检测方法 …
为此, 提出了一种基于长短记忆网络(LSTM) 与改进残差神经网络优化的异常流量检测方法 …
Learning from Limited Heterogeneous Training Data: Meta-Learning for Unsupervised Zero-Day Web Attack Detection across Web Domains
Recently unsupervised machine learning based systems have been developed to detect
zero-day Web attacks, which can effectively enhance existing Web Application Firewalls …
zero-day Web attacks, which can effectively enhance existing Web Application Firewalls …
A survey of graph-based deep learning for anomaly detection in distributed systems
Anomaly detection is a crucial task in complex distributed systems. A thorough
understanding of the requirements and challenges of anomaly detection is pivotal to the …
understanding of the requirements and challenges of anomaly detection is pivotal to the …
Landmine detection using autoencoders on multipolarization GPR volumetric data
P Bestagini, F Lombardi, M Lualdi… - … on Geoscience and …, 2020 - ieeexplore.ieee.org
Buried landmines and unexploded remnants of war are a constant threat for the population
of many countries that have been hit by wars in the past years. The huge amount of …
of many countries that have been hit by wars in the past years. The huge amount of …