A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM

J Liu, Y Gao, F Hu - Computers & Security, 2021 - Elsevier
Network intrusion detection systems play an important role in protecting the network from
attacks. However, Existing network intrusion data is imbalanced, which makes it difficult to …

A detailed analysis of benchmark datasets for network intrusion detection system

M Ghurab, G Gaphari, F Alshami… - Asian Journal of …, 2021 - papers.ssrn.com
The enormous increase in the use of the Internet in daily life has provided an opportunity for
the intruder attempt to compromise the security principles of availability, confidentiality, and …

An enhanced anomaly detection in web traffic using a stack of classifier ensemble

BA Tama, L Nkenyereye, SMR Islam, KS Kwak - IEEE Access, 2020 - ieeexplore.ieee.org
A Web attack protection system is extremely essential in today's information age. Classifier
ensembles have been considered for anomaly-based intrusion detection in Web traffic …

Recurrent neural network based intrusion detection system

S Nayyar, S Arora, M Singh - 2020 international conference on …, 2020 - ieeexplore.ieee.org
An increase in connectivity through the internet and increased freedom of data access has
led to numerous attempts to attack network servers. These attacks have become …

Few-shot class-adaptive anomaly detection with model-agnostic meta-learning

T Feng, Q Qi, J Wang, J Liao - 2021 IFIP networking conference …, 2021 - ieeexplore.ieee.org
Anomaly detection in encrypted traffic is a growing problem, and many approaches have
been proposed to solve it. However, those approaches need to be trained in the massive of …

Intrusion detection system over real-time data traffic using machine learning methods with feature selection approaches

G Sah, S Banerjee, S Singh - International Journal of Information Security, 2023 - Springer
The intrusion detection system (IDS) plays an important role in extracting and analysing the
network traffics to detect aberrant activity. However, emerging technologies, like cloud …

[PDF][PDF] A New Hybrid Approach Using GWO and MFO Algorithms to Detect Network Attack.

H Dalmaz, E Erdal, HM Ünver - CMES-Computer Modeling in …, 2023 - cdn.techscience.cn
This paper addresses the urgent need to detect network security attacks, which have
increased significantly in recent years, with high accuracy and avoid the adverse effects of …

An Ensemble of Text Convolutional Neural Networks and Multi-Head Attention Layers for Classifying Threats in Network Packets

H Kim, Y Yoon - Electronics, 2023 - mdpi.com
Using traditional methods based on detection rules written by human security experts
presents significant challenges for the accurate detection of network threats, which are …

A Comparative Analysis of Supervised Machine Learning Models for Smart Intrusion Detection in IoT Network

S Mittal, AK Mishra, V Tripathi, P Singh… - 2023 3rd Asian …, 2023 - ieeexplore.ieee.org
IoT will become an integral part of everyone's life in making the world more intelligent and
smarter. In an IoT ecosystem, objects collect and share information in order to communicate …

Few-shot network intrusion detection based on model-agnostic meta-learning with l2f method

Z Shi, M Xing, J Zhang, BH Wu - 2023 IEEE Wireless …, 2023 - ieeexplore.ieee.org
Network Intrusion Detection (NID) plays an important role in identifying network threats and
ensuring the security of computer and communication systems. However, the existing NID …