[HTML][HTML] A systematic literature review of methods and datasets for anomaly-based network intrusion detection
As network techniques rapidly evolve, attacks are becoming increasingly sophisticated and
threatening. Network intrusion detection has been widely accepted as an effective method to …
threatening. Network intrusion detection has been widely accepted as an effective method to …
A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
With the increase in the usage of the Internet, a large amount of information is exchanged
between different communicating devices. The data should be communicated securely …
between different communicating devices. The data should be communicated securely …
Fusion of statistical importance for feature selection in Deep Neural Network-based Intrusion Detection System
Abstract Intrusion Detection System (IDS) is an essential part of network as it contributes
towards securing the network against various vulnerabilities and threats. Over the past …
towards securing the network against various vulnerabilities and threats. Over the past …
A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges
Abstract Internet of Things (IoT) is widely accepted technology in both industrial as well as
academic field. The objective of IoT is to combine the physical environment with the cyber …
academic field. The objective of IoT is to combine the physical environment with the cyber …
A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions
The stock market has been an attractive field for a large number of organizers and investors
to derive useful predictions. Fundamental knowledge of stock market can be utilised with …
to derive useful predictions. Fundamental knowledge of stock market can be utilised with …
A survey and analysis of intrusion detection models based on cse-cic-ids2018 big data
The exponential growth in computer networks and network applications worldwide has been
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …
Attack classification of imbalanced intrusion data for IoT network using ensemble-learning-based deep neural network
With the increase in popularity of Internet of Things (IoT) and the rise in interconnected
devices, the need to foster effective security mechanism to handle vulnerabilities and risks in …
devices, the need to foster effective security mechanism to handle vulnerabilities and risks in …
A new ensemble-based intrusion detection system for internet of things
The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few
years by drastically transforming human lives to automate their ordinary daily tasks. This is …
years by drastically transforming human lives to automate their ordinary daily tasks. This is …
Adversarial machine learning attacks against intrusion detection systems: A survey on strategies and defense
Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …
Intrusion detection system based on fast hierarchical deep convolutional neural network
Currently, with the increasing number of devices connected to the Internet, search for
network vulnerabilities to attackers has increased, and protection systems have become …
network vulnerabilities to attackers has increased, and protection systems have become …