A novel two-stage deep learning model for network intrusion detection: LSTM-AE
Machine learning and deep learning techniques are widely used to evaluate intrusion
detection systems (IDS) capable of rapidly and automatically recognizing and classifying …
detection systems (IDS) capable of rapidly and automatically recognizing and classifying …
DDoS attack detection and mitigation using deep neural network in SDN environment
In the contemporary digital landscape, the escalating threat landscape of cyber attacks,
particularly distributed denial-of-service (DDoS) attacks, has become a paramount concern …
particularly distributed denial-of-service (DDoS) attacks, has become a paramount concern …
DeMi: A Solution to Detect and Mitigate DoS Attacks in SDN
LF Eliyan, R Di Pietro - IEEE Access, 2023 - ieeexplore.ieee.org
Software-defined networking (SDN) is becoming more and more popular due to its key
features of scalability and flexibility, simplifying network management and enabling …
features of scalability and flexibility, simplifying network management and enabling …
An efficient DDoS attack detection mechanism in SDN environment
Traditional intrusion detection systems are insufficient to identify recent and modern
sophisticated attempts with unpredictable patterns. The ability to reliably detect modern …
sophisticated attempts with unpredictable patterns. The ability to reliably detect modern …
Network Intrusion Detection using Deep Convolution Neural Network
In recent years, with the rise of cyber attacks, intrusion detection systems (IDS) have become
an essential component of network security. Deep learning-based approaches have shown …
an essential component of network security. Deep learning-based approaches have shown …
Design of an Efficient Entropy-based DDoS Attacks Detection Scheme in Software Defined Networking
In recent times, the occurrence of DDoS attacks has been increasing, thereby it boosts up
the vulnerability of SDN's security. These attacks have a damaging impact on the controller …
the vulnerability of SDN's security. These attacks have a damaging impact on the controller …
DDoS detection using hybrid deep neural network approaches
In this study, we provide Deep Neural Network (DNN) based approaches to detecting
Distributed Denial-of-Service (DDoS) attacks. In order to improve the DNN's accuracy, the …
Distributed Denial-of-Service (DDoS) attacks. In order to improve the DNN's accuracy, the …
Performance Evaluation of Network Intrusion Detection Using Machine Learning
S Gnanasivam, D Tveter, N Dinh - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
The development of 5G network and beyond has led to an explosion of data generation. It is
therefore crucial to have an intrusion detection system (IDS) to detect and remove malicious …
therefore crucial to have an intrusion detection system (IDS) to detect and remove malicious …
Enhanced Attacks Detection and Mitigation in Software Defined Networks
SC Forbacha, MK Kinteh, EM Hamza - American Journal of …, 2024 - ajpojournals.org
Purpose: The main aim of this research project was to develop a security simulation and
mitigation mechanism for Software Defined Networking (SDN) deploying machine learning …
mitigation mechanism for Software Defined Networking (SDN) deploying machine learning …
Exploring A Two-Phase Deep Learning Framework For Network Intrusion Detection
R Padmaja, PR Challagundla - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Intrusion detection systems (IDS) often use machine learning and deep learning to identify
and categorize cyber-attacks swiftly. However, as these attacks expand, a robust response …
and categorize cyber-attacks swiftly. However, as these attacks expand, a robust response …