MEMBER: A multi-task learning model with hybrid deep features for network intrusion detection
With the continuous occurrence of cybersecurity incidents, network intrusion detection has
become one of the most critical issues in cyber ecosystems. Although previous machine …
become one of the most critical issues in cyber ecosystems. Although previous machine …
Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection
A robust network intrusion detection system (NIDS) plays an important role in cyberspace
security for protecting confidential systems from potential threats. In real world network, there …
security for protecting confidential systems from potential threats. In real world network, there …
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 …
Improving the Performance of Machine Learning‐Based Network Intrusion Detection Systems on the UNSW‐NB15 Dataset
Networks are exposed to an increasing number of cyberattacks due to their vulnerabilities.
So, cybersecurity strives to make networks as safe as possible, by introducing defense …
So, cybersecurity strives to make networks as safe as possible, by introducing defense …
A supervised machine learning-based solution for efficient network intrusion detection using ensemble learning based on hyperparameter optimization
An efficient machine learning (ML) ensemble technique for categorizing Intrusion Detection
(ID) is proposed in this study. The tuning of the ML model's parameters is a critical topic …
(ID) is proposed in this study. The tuning of the ML model's parameters is a critical topic …
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 …
Machine‐learning approach to optimize smote ratio in class imbalance dataset for intrusion detection
The KDD CUP 1999 intrusion detection dataset was introduced at the third international
knowledge discovery and data mining tools competition, and it has been widely used for …
knowledge discovery and data mining tools competition, and it has been widely used for …
Network intrusion detection based on IE-DBN model
H Jia, J Liu, M Zhang, X He, W Sun - Computer Communications, 2021 - Elsevier
Existing network intrusion detection models suffer such problems as low detection accuracy
and high false alarm rates in face of massive data traffic. Deep-learning models provide a …
and high false alarm rates in face of massive data traffic. Deep-learning models provide a …
Effectiveness of focal loss for minority classification in network intrusion detection systems
As the rapid development of information and communication technology systems offers
limitless access to data, the risk of malicious violations increases. A network intrusion …
limitless access to data, the risk of malicious violations increases. A network intrusion …
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 …