A survey on data-driven network intrusion detection
Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …
classes compared to normal traffic. Many datasets are collected in simulated environments …
Variational few-shot learning for microservice-oriented intrusion detection in distributed industrial IoT
Along with the popularity of the Internet of Things (IoT) techniques with several
computational paradigms, such as cloud and edge computing, microservice has been …
computational paradigms, such as cloud and edge computing, microservice has been …
Intrusion detection of imbalanced network traffic based on machine learning and deep learning
L Liu, P Wang, J Lin, L Liu - IEEE access, 2020 - ieeexplore.ieee.org
In imbalanced network traffic, malicious cyber-attacks can often hide in large amounts of
normal data. It exhibits a high degree of stealth and obfuscation in cyberspace, making it …
normal data. It exhibits a high degree of stealth and obfuscation in cyberspace, making it …
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 …
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 …
A hybrid intrusion detection system based on scalable K-means+ random forest and deep learning
C Liu, Z Gu, J Wang - Ieee Access, 2021 - ieeexplore.ieee.org
Digital assets have come under various network security threats in the digital age. As a kind
of security equipment to protect digital assets, intrusion detection system (IDS) is less …
of security equipment to protect digital assets, intrusion detection system (IDS) is less …
Autoencoder-based deep metric learning for network intrusion detection
Nowadays intrusion detection systems are a mandatory weapon in the war against the ever-
increasing amount of network cyber attacks. In this study we illustrate a new intrusion …
increasing amount of network cyber attacks. In this study we illustrate a new intrusion …
A double-layered hybrid approach for network intrusion detection system using combined naive bayes and SVM
T Wisanwanichthan, M Thammawichai - Ieee Access, 2021 - ieeexplore.ieee.org
A pattern matching method (signature-based) is widely used in basic network intrusion
detection systems (IDS). A more robust method is to use a machine learning classifier to …
detection systems (IDS). A more robust method is to use a machine learning classifier to …
I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems
Abstract Network-based Intrusion Detection Systems (NIDSs) identify malicious activities by
analyzing network traffic. NIDSs are trained with the samples of benign and intrusive …
analyzing network traffic. NIDSs are trained with the samples of benign and intrusive …
[HTML][HTML] Supervised contrastive learning over prototype-label embeddings for network intrusion detection
Contrastive learning makes it possible to establish similarities between samples by
comparing their distances in an intermediate representation space (embedding space) and …
comparing their distances in an intermediate representation space (embedding space) and …