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 …
An end-to-end learning approach for enhancing intrusion detection in Industrial-Internet of Things
K Hassini, S Khalis, O Habibi, M Chemmakha… - Knowledge-Based …, 2024 - Elsevier
Abstract The Industrial-Internet of Things (I-IoT) stands out as one of the most dynamically
evolving subfields within the expansive realm of the Internet of Things (IoT). Its exponential …
evolving subfields within the expansive realm of the Internet of Things (IoT). Its exponential …
Enhanced CNN-LSTM deep learning for scada IDS featuring hurst parameter self-similarity
Supervisory Control and Data Acquisition (SCADA) systems are crucial for modern industrial
processes and securing them against increasing cyber threats is a significant challenge …
processes and securing them against increasing cyber threats is a significant challenge …
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 …
Enhancing network security with information-guided-enhanced Runge Kutta feature selection for intrusion detection
L Yuan, X Tian, J Yuan, J zhang, X Dai, AA Heidari… - Cluster …, 2024 - Springer
Intrusion detection system (IDS) classify network traffic as either threatening or normal based
on data features, aiming to identify malicious activities attempting to compromise computer …
on data features, aiming to identify malicious activities attempting to compromise computer …
[HTML][HTML] Application of Deep Neural Network with Frequency Domain Filtering in the Field of Intrusion Detection
Z Wang, J Li, Z Xu, S Yang, D He… - International Journal of …, 2023 - Wiley Online Library
In the field of intrusion detection, existing deep learning algorithms have limited capability to
effectively represent network data features, making it challenging to model the complex …
effectively represent network data features, making it challenging to model the complex …
Swarm Optimized Differential Evolution and Probabilistic Extreme Learning based Intrusion Detection in MANET
R Sathiya, N Yuvaraj - Computers & Security, 2024 - Elsevier
MANETs are an attracting mechanism foSr several applications, to name a few being rescue
functioning, environmental surveillance and so on due to the reason that they allow users to …
functioning, environmental surveillance and so on due to the reason that they allow users to …
A hierarchical hybrid intrusion detection model for industrial internet of things
Z Wang, X Yang, Z Zeng, D He, S Chan - Peer-to-Peer Networking and …, 2024 - Springer
With the continual evolution of network technologies, the Internet of Things (IoT) has
permeated various sectors of society. However, over the past decade, the annual discovery …
permeated various sectors of society. However, over the past decade, the annual discovery …
A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection
The vast amounts of data, lack of scalability, and low detection rates of traditional intrusion
detection technologies make it impossible to keep up with evolving and increasingly …
detection technologies make it impossible to keep up with evolving and increasingly …
[HTML][HTML] Collaborative intrusion detection using weighted ensemble averaging deep neural network for coordinated attack detection in heterogeneous network
AA Wardana, G Kołaczek, A Warzyński… - International Journal of …, 2024 - Springer
Detecting coordinated attacks in cybersecurity is challenging due to their sophisticated and
distributed nature, making traditional Intrusion Detection Systems often ineffective …
distributed nature, making traditional Intrusion Detection Systems often ineffective …