Fusion of statistical importance for feature selection in Deep Neural Network-based Intrusion Detection System

A Thakkar, R Lohiya - Information Fusion, 2023 - Elsevier
Abstract Intrusion Detection System (IDS) is an essential part of network as it contributes
towards securing the network against various vulnerabilities and threats. Over the past …

A dependable hybrid machine learning model for network intrusion detection

MA Talukder, KF Hasan, MM Islam, MA Uddin… - Journal of Information …, 2023 - Elsevier
Network intrusion detection systems (NIDSs) play an important role in computer network
security. There are several detection mechanisms where anomaly-based automated …

A fully streaming big data framework for cyber security based on optimized deep learning algorithm

N Hussen, SM Elghamrawy, M Salem… - IEEE …, 2023 - ieeexplore.ieee.org
Real-time deep learning faces the challenge of balancing accuracy and time, especially in
cybersecurity where intrusion detection is crucial. Traditional deep learning techniques have …

CIS feature selection based dynamic ensemble selection model for human stress detection from EEG signals

L Malviya, S Mal - Cluster Computing, 2023 - Springer
Stress has an impact not only on a person's physical health but also on his or her ability to
perform at work, passion, and attitude in day-to-day life. It is one of the most difficult …

Polymorphic adversarial cyberattacks using WGAN

R Chauhan, U Sabeel, A Izaddoost… - Journal of Cybersecurity …, 2021 - mdpi.com
Intrusion Detection Systems (IDS) are essential components in preventing malicious traffic
from penetrating networks and systems. Recently, these systems have been enhancing their …

[PDF][PDF] A novel distributed machine learning model to detect attacks on edge computing network

TM Hoang, TL Le Thi, NM Quy - Journal of Advances in Information …, 2023 - jait.us
To meet the growing number and variety of IoT devices in 5G and 6G network environments,
the development of edge computing technology is a powerful strategy for offloading …

A study on heuristic algorithms combined with lr on a dnn-based ids model to detect iot attacks

TTT Thuy, LD Thuan, NH Duc, HT Minh - Mendel, 2023 - ib-b2b.test.infv.eu
Current security challenges are made more difficult by the complexity and difficulty of
spotting cyberattacks due to the Internet of Things explosive growth in connected devices …

[HTML][HTML] Cascaded intrusion detection system using machine learning

MKU Ahamed, A Karim - Systems and Soft Computing, 2025 - Elsevier
Cybercrime is becoming an increasing concern these days. In response to the growing
cyberthreat, various intrusion detection systems have been developed and proposed to …

GenCoder: A Generative AI-based Adaptive Intra-Vehicle Intrusion Detection System

M Smolin - IEEE Access, 2024 - ieeexplore.ieee.org
With the rapid expansion of the vehicular cybersecurity (VCS) market and the increasing
sophistication of cyberthreats, develo** an adaptive intra-vehicular intrusion detection …

Intrusion Detection System (IDS) Classifications Using Hyperparameter Tuning for Machine Learning and Deep Learning

TM May, Z Zainudin, N Muslim, NS Jamil… - … and Data Sciences …, 2024 - ieeexplore.ieee.org
With the rapid advancement of technology, new security vulnerabilities are emerging.
Intrusion Detection Systems (IDS) are responsible for protecting corporate networks by …