A review of recent approaches on wrapper feature selection for intrusion detection
In this paper, we present a review of recent advances in wrapper feature selection
techniques for attack detection and classification, applied in intrusion detection area. Due to …
techniques for attack detection and classification, applied in intrusion detection area. Due to …
Machine learning and deep learning approaches for cybersecurity: A review
A Halbouni, TS Gunawan, MH Habaebi… - IEEE …, 2022 - ieeexplore.ieee.org
The rapid evolution and growth of the internet through the last decades led to more concern
about cyber-attacks that are continuously increasing and changing. As a result, an effective …
about cyber-attacks that are continuously increasing and changing. As a result, an effective …
Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection
H Ding, L Chen, L Dong, Z Fu, X Cui - Future Generation Computer Systems, 2022 - Elsevier
With the continuous emergence of various network attacks, it is becoming more and more
important to ensure the security of the network. Intrusion detection, as one of the important …
important to ensure the security of the network. Intrusion detection, as one of the important …
Features dimensionality reduction approaches for machine learning based network intrusion detection
The security of networked systems has become a critical universal issue that influences
individuals, enterprises and governments. The rate of attacks against networked systems …
individuals, enterprises and governments. The rate of attacks against networked systems …
A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets
The widespread acceptance and increase of the Internet and mobile technologies have
revolutionized our existence. On the other hand, the world is witnessing and suffering due to …
revolutionized our existence. On the other hand, the world is witnessing and suffering due to …
A method of few-shot network intrusion detection based on meta-learning framework
C Xu, J Shen, X Du - IEEE Transactions on Information …, 2020 - ieeexplore.ieee.org
Conventional intrusion detection systems based on supervised learning techniques require
a large number of samples for training, while in some scenarios, such as zero-day attacks …
a large number of samples for training, while in some scenarios, such as zero-day attacks …
Binary biogeography-based optimization based SVM-RFE for feature selection
Rapid data growth presents many challenges for Machine Learning (ML) tasks as it can
include lots of irrelevant, noisy, and redundant features. Thus, it is vital to select the most …
include lots of irrelevant, noisy, and redundant features. Thus, it is vital to select the most …
[PDF][PDF] Ransomware, threat and detection techniques: A review
S Kok, A Abdullah, N Jhanjhi… - Int. J. Comput. Sci. Netw …, 2019 - academia.edu
The popularity of ransomware has created a unique ecosystem of cybercriminals. Therefore,
the objectives of this paper are to provide a thorough understanding of ransomware's threat …
the objectives of this paper are to provide a thorough understanding of ransomware's threat …
Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks
Intrusion detection systems assume a noteworthy job in the provision of security in wireless
Sensor networks. The existing intrusion detection systems focus only on the detection of the …
Sensor networks. The existing intrusion detection systems focus only on the detection of the …
Intrusion detection methods based on integrated deep learning model
Intrusion detection system can effectively identify abnormal data in complex network
environments, which is an effective method to ensure computer network security. Recently …
environments, which is an effective method to ensure computer network security. Recently …