Multi-stage learning framework using convolutional neural network and decision tree-based classification for detection of DDoS pandemic attacks in SDN-based …
Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in
monitoring, managing, and controlling industrial processes, face flexibility, scalability, and …
monitoring, managing, and controlling industrial processes, face flexibility, scalability, and …
Mitigating Cyber Anomalies in Virtual Power Plants Using Artificial-Neural-Network-Based Secondary Control with a Federated Learning-Trust Adaptation
Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive
communication layer. FL-trust, an improved federated learning (FL) approach, has been …
communication layer. FL-trust, an improved federated learning (FL) approach, has been …
Feature Importance-Based Backdoor Attack in NSL-KDD
J Jang, Y An, D Kim, D Choi - Electronics, 2023 - mdpi.com
In this study, we explore the implications of advancing AI technology on the safety of
machine learning models, specifically in decision-making across diverse applications. Our …
machine learning models, specifically in decision-making across diverse applications. Our …
NHAPMAD: Novel hybrid approaches for privacy‐preserved multiple attacks detection
Detecting anomalies is crucial for maintaining security in Wireless Sensor Networks (WSNs),
as they are susceptible to various attacks that compromise nodes and yield inaccurate …
as they are susceptible to various attacks that compromise nodes and yield inaccurate …
[PDF][PDF] A Time Series Intrusion DetectionMethod Based on SSAE, TCN and Bi-LSTM.
Z He, X Wang, C Li - Computers, Materials & Continua, 2024 - cdn.techscience.cn
In the fast-evolving landscape of digital networks, the incidence of network intrusions has
escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion …
escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion …
Efficient Feature Selection for IoT Security: A Comparative Analysis of Swarm Optimization Algorithms in Attack Detection
Abstract As the Internet of Things continues to expand, securing this interconnected space
becomes increasingly critical. This research presents a wrapper-based approach to feature …
becomes increasingly critical. This research presents a wrapper-based approach to feature …
DoS Attack Detection Using Feature Selection with Information Gain and ML Classification
Designing an Intrusion Detection System (IDS) for attack detection in IoT networks is done
by applying different machine learning and deep learning approaches. Standard CIE …
by applying different machine learning and deep learning approaches. Standard CIE …
SmartDefend-IoT Security Using Machine Learning
YM Saumya, P Vinay, C Pinto… - 2024 IEEE …, 2024 - ieeexplore.ieee.org
With the advent of the fourth industrial revolution in recent years, technological
advancements have led to massive exponential growth in the Internet of Things (loTs), fog …
advancements have led to massive exponential growth in the Internet of Things (loTs), fog …
Security: A Comparative Analysis of Swarm Optimization Algorithms
Abstract As the Internet of Things continues to expand, securing this interconnected space
becomes increasingly critical. This research presents a wrapper-based approach to feature …
becomes increasingly critical. This research presents a wrapper-based approach to feature …
A Novel Approach of Evasive Malware Analysis Through Binary Opcode and BERT
S Wang, B Xu - 2024 - researchsquare.com
This study addressed the growing challenge of evasive malware detection by introducing an
innovative approach that integrates binary opcode analysis with the advanced machine …
innovative approach that integrates binary opcode analysis with the advanced machine …