Detecting Unbalanced Network Traffic Intrusions with Deep Learning

S Pavithra, KV Vikas - IEEE Access, 2024 - ieeexplore.ieee.org
The growth of cyber threats demands a robust and adaptive intrusion detection system (IDS)
capable of effectively recognizing malicious activities from network traffic. However, the …

CSE-IDS: Using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems

N Gupta, V Jindal, P Bedi - Computers & Security, 2022 - Elsevier
In recent times, Network-based Intrusion Detection Systems (NIDSs) have become very
popular for detecting intrusions in computer networks. Existing NIDSs can easily identify …

[HTML][HTML] CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units

Y Imrana, Y Xiang, L Ali, A Noor, K Sarpong… - Complex & Intelligent …, 2024 - Springer
Identifying and preventing malicious network behavior is a challenge for establishing a
secure network communication environment or system. Malicious activities in a network …

MBC-UBEM-IDS: A Two-Step Intrusion Detection System for Imbalanced Network Traffic

H Xian, Z Lin, W Yao, L Bing - 2023 20th International …, 2023 - ieeexplore.ieee.org
Intrusion Detection System is a type of tool capable of effectively identifying malicious
network traffic. However, existing intrusion detection methods face the challenge of class …

[PDF][PDF] Towards Detecting and Classifying Network Intrusion Traffic Using Deep Learning Frameworks.

RB Basnet, R Shash, C Johnson… - J. Internet Serv. Inf …, 2019 - researchgate.net
Recent breakthroughs in deep learning algorithms have enabled researchers and
practitioners to make significant progress in various hard computer science problems and …

[PDF][PDF] Empirical solution for an optimized machine learning framework for anomaly-based network intrusion detection

AA Ojugo, RE Yoro - Technology Report of Kansai University, 2020 - researchgate.net
Advances in tech geared targeted at moving the society to a higher plain and sophistication
with ease. The further integration of Internet to ease resource dissemination is attributed to …

Machine learning for misuse-based network intrusion detection: overview, unified evaluation and feature choice comparison framework

L Le Jeune, T Goedeme, N Mentens - Ieee Access, 2021 - ieeexplore.ieee.org
Network Intrusion detection systems are essential for the protection of advanced
communication networks. Originally, these systems were hard-coded to identify specific …

Handling class Imbalance problem in Intrusion Detection System based on deep learning

M Mbow, H Koide, K Sakurai - International Journal of Networking …, 2022 - jstage.jst.go.jp
Network intrusion detection system (NIDS) is the most used tool to detect malicious network
activities. The NIDS has achieved in the recent years promising results for detecting known …

Knacks of a hybrid anomaly detection model using deep auto-encoder driven gated recurrent unit

E Mushtaq, A Zameer, R Nasir - Computer Networks, 2023 - Elsevier
The cyber-attacks have recently posed a threat to national security; meanwhile, the
pervasiveness of malware and cyber terrorism encumbers the beneficial utilization of the …

Deep Learning-Based High Performance Intrusion Detection System for Imbalanced Datasets

F Ahmed, TS Gunawan, AN Nordin… - … on Wireless and …, 2023 - ieeexplore.ieee.org
In recent years, the explosive growth in internet and technology use has led to an alarming
escalation in both the frequency and severity of cyberattacks. As such, proactive detection …