[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 …
Survey on evolutionary deep learning: Principles, algorithms, applications, and open issues
Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …
industry and academia fields. However, finding the optimal hyperparameters of a DL model …
[HTML][HTML] A hybrid intrusion detection model using ega-pso and improved random forest method
AK Balyan, S Ahuja, UK Lilhore, SK Sharma… - Sensors, 2022 - mdpi.com
Due to the rapid growth in IT technology, digital data have increased availability, creating
novel security threats that need immediate attention. An intrusion detection system (IDS) is …
novel security threats that need immediate attention. An intrusion detection system (IDS) is …
An effective intrusion detection approach using SVM with naïve Bayes feature embedding
J Gu, S Lu - Computers & Security, 2021 - Elsevier
Network security has become increasingly important in recent decades, while intrusion
detection system plays a critical role in protecting it. Various machine learning techniques …
detection system plays a critical role in protecting it. Various machine learning techniques …
[HTML][HTML] Machine-learning-based DDoS attack detection using mutual information and random forest feature importance method
Cloud computing facilitates the users with on-demand services over the Internet. The
services are accessible from anywhere at any time. Despite the valuable services, the …
services are accessible from anywhere at any time. Despite the valuable services, the …
CSE-IDS: Using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems
In recent times, Network-based Intrusion Detection Systems (NIDSs) have become very
popular for detecting intrusions in computer networks. Existing NIDSs can easily identify …
popular for detecting intrusions in computer networks. Existing NIDSs can easily identify …
AI-IDS: Application of deep learning to real-time Web intrusion detection
Deep Learning has been widely applied to problems in detecting various network attacks.
However, no cases on network security have shown applications of various deep learning …
However, no cases on network security have shown applications of various deep learning …
A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
H Ahmetoglu, R Das - Internet of Things, 2022 - Elsevier
Rapid developments in network technologies and the amount and scope of data transferred
on networks are increasing day by day. Depending on this situation, the density and …
on networks are increasing day by day. Depending on this situation, the density and …
Hybrid approach to intrusion detection in fog-based IoT environments
CA De Souza, CB Westphall, RB Machado… - Computer Networks, 2020 - Elsevier
Abstract In the Internet of Things (IoT) systems, information of various kinds is continuously
captured, processed, and transmitted by systems generally interconnected by the Internet …
captured, processed, and transmitted by systems generally interconnected by the Internet …
Genetic convolutional neural network for intrusion detection systems
Intrusion detection is the identification of unauthorized access of a computer network. This
paper proposes a novel algorithm for a network intrusion detection system (NIDS) using an …
paper proposes a novel algorithm for a network intrusion detection system (NIDS) using an …