Fusion of statistical importance for feature selection in Deep Neural Network-based Intrusion Detection System
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 …
towards securing the network against various vulnerabilities and threats. Over the past …
A dependable hybrid machine learning model for network intrusion detection
Network intrusion detection systems (NIDSs) play an important role in computer network
security. There are several detection mechanisms where anomaly-based automated …
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
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 …
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
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 …
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 …
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
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 …
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
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 …
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 …
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 …
sophistication of cyberthreats, develo** an adaptive intra-vehicular intrusion detection …
Intrusion Detection System (IDS) Classifications Using Hyperparameter Tuning for Machine Learning and Deep Learning
With the rapid advancement of technology, new security vulnerabilities are emerging.
Intrusion Detection Systems (IDS) are responsible for protecting corporate networks by …
Intrusion Detection Systems (IDS) are responsible for protecting corporate networks by …