PSO-ACO-based bi-phase lightweight intrusion detection system combined with GA optimized ensemble classifiers
A Srivastava, D Sinha - Cluster Computing, 2024 - Springer
Features within the dataset carry a significant role; however, resource utilization, prediction-
time, and model weight are increased by utilizing high-dimensional data in intrusion …
time, and model weight are increased by utilizing high-dimensional data in intrusion …
[HTML][HTML] Comprehensive botnet detection by mitigating adversarial attacks, navigating the subtleties of perturbation distances and fortifying predictions with conformal …
Botnets are computer networks controlled by malicious actors that present significant
cybersecurity challenges. They autonomously infect, propagate, and coordinate to conduct …
cybersecurity challenges. They autonomously infect, propagate, and coordinate to conduct …
MFT: A novel memory flow transformer efficient intrusion detection method
X Jiang, L Xu, L Yu, X Fang - Computers & Security, 2025 - Elsevier
Intrusion detection is a critical field in network security research that is devoted to detecting
malicious traffic or attacks on networks. Even with the advances in today's Internet …
malicious traffic or attacks on networks. Even with the advances in today's Internet …
Real-time fusion multi-tier DNN-based collaborative IDPS with complementary features for secure UAV-enabled 6G networks
UAV-enabled Integrated Sensing and Communication (ISAC) in sixth-generation (6G)
wireless networks has sparked significant research interest. UAVs are positioned as aerial …
wireless networks has sparked significant research interest. UAVs are positioned as aerial …
Cost-sensitive stacked long short-term memory with an evolutionary framework for minority class detection
Abstract 'Minority attack detection'is a matter of great concern while designing a secure
network and safeguarding it against cyber criminals who attempt to breach its defenses …
network and safeguarding it against cyber criminals who attempt to breach its defenses …
Intrusion Detection With Deep Learning Classifiers: A Synergistic Approach of Probabilistic Clustering and Human Expertise to Reduce False Alarms
Intrusion detection systems (IDS) have seen an increasing number of proposals by
researchers utilizing deep learning (DL) to safeguard critical networks. However, they often …
researchers utilizing deep learning (DL) to safeguard critical networks. However, they often …
A novel optimization-driven deep learning framework for the detection of DDoS attacks
Distributed denial of service (DDoS) attack is one of the most hazardous assaults in cloud
computing or networking. By depleting resources, this attack renders the services …
computing or networking. By depleting resources, this attack renders the services …
Towards detection of network anomalies using machine learning algorithms on the NSL-KDD benchmark datasets
In the present era, everyone is connected via the Internet for sharing digital information. The
digital data is stored using the cloud technology. However, cloud technology is speedily …
digital data is stored using the cloud technology. However, cloud technology is speedily …
An interpretable Bayesian deep learning-based approach for sustainable clean energy
Abstract Sustainable Development Goal 7 is dedicated to ensuring access to clean and
affordable energy that can be utilized in various applications. Solar panels (SP) are utilized …
affordable energy that can be utilized in various applications. Solar panels (SP) are utilized …
MGFEEN: a multi-granularity feature encoding ensemble network for remote sensing image classification
M Jean Bosco, R Jean Pierre, MSA Muthanna… - Neural Computing and …, 2024 - Springer
Deep convolutional neural networks (DCNNs) have emerged as powerful tools in diverse
remote sensing domains, but their optimization remains challenging due to their complex …
remote sensing domains, but their optimization remains challenging due to their complex …