[HTML][HTML] Cyber risk and cybersecurity: a systematic review of data availability
Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020,
indicating an increase of more than 50% since 2018. With the average cyber insurance …
indicating an increase of more than 50% since 2018. With the average cyber insurance …
A comprehensive survey on the process, methods, evaluation, and challenges of feature selection
Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
[HTML][HTML] HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system
MA Khan - Processes, 2021 - mdpi.com
Nowadays, network attacks are the most crucial problem of modern society. All networks,
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …
Cyber intrusion detection system based on a multiobjective binary bat algorithm for feature selection and enhanced bat algorithm for parameter optimization in neural …
The staggering development of cyber threats has propelled experts, professionals and
specialists in the field of security into the development of more dependable protection …
specialists in the field of security into the development of more dependable protection …
[HTML][HTML] Toward developing efficient Conv-AE-based intrusion detection system using heterogeneous dataset
Recently, due to the rapid development and remarkable result of deep learning (DL) and
machine learning (ML) approaches in various domains for several long-standing artificial …
machine learning (ML) approaches in various domains for several long-standing artificial …
[HTML][HTML] Network traffic analysis through node behaviour classification: a graph-based approach with temporal dissection and data-level preprocessing
Network traffic analysis is an important cybersecurity task, which helps to classify
anomalous, potentially dangerous connections. In many cases, it is critical not only to detect …
anomalous, potentially dangerous connections. In many cases, it is critical not only to detect …
[PDF][PDF] Deep Learning-Based Hybrid Intelligent Intrusion Detection System.
MA Khan, Y Kim - Computers, Materials & Continua, 2021 - cdn.techscience.cn
Machine learning (ML) algorithms are often used to design effective intrusion detection (ID)
systems for appropriate mitigation and effective detection of malicious cyber threats at the …
systems for appropriate mitigation and effective detection of malicious cyber threats at the …
Deep learning for proactive network monitoring and security protection
The work presented in this paper deals with a proactive network monitoring for security and
protection of computing infrastructures. We provide an exploitation of an intelligent module …
protection of computing infrastructures. We provide an exploitation of an intelligent module …
An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons
One of the most persistent challenges concerning network security is to build a model
capable of detecting intrusions in network systems. The issue has been extensively …
capable of detecting intrusions in network systems. The issue has been extensively …
[HTML][HTML] A deep density based and self-determining clustering approach to label unknown traffic
Analyzing non-labeled data is a major concern in the field of intrusion detection as the attack
clusters are continuously evolving which are unknown for the system. Many studies have …
clusters are continuously evolving which are unknown for the system. Many studies have …