[PDF][PDF] Intelligent detection of distributed denial of service attacks: A supervised machine learning and ensemble approach
MSI Alsumaidaie, KMA Alheeti, AK Alaloosy - Iraqi Journal for Computer …, 2023 - iasj.net
The rapid growth of The Internet of Things (IoT), intelligent devices, and 5G networks has
increased the prevalence and complexity of Distributed Denial of Service (DDoS) attacks …
increased the prevalence and complexity of Distributed Denial of Service (DDoS) attacks …
Comparative evaluation of machine learning algorithms for network intrusion detection and attack classification
With the increasing use of the internet and reliance on computer-based systems for our daily
lives, any vulnerability in those systems is one of the most important issues for the …
lives, any vulnerability in those systems is one of the most important issues for the …
Blockchain meets IIoT: An architecture for privacy preservation and security in IIoT
V Puri, I Priyadarshini, R Kumar… - … Conference on Computer …, 2020 - ieeexplore.ieee.org
Industry 4.0 articulates that modern intelligent machines are better than humans that are
enough capable to capture and analyze data in real-time, as well as in communicating …
enough capable to capture and analyze data in real-time, as well as in communicating …
Autoencoder for Design of Mitigation Model for DDOS Attacks via M‐DBNN
Distributed Denial of Service (DDoS) attacks pose the greatest threat to the continued and
efficient operation of the Internet. It can lead to website downtime, lost time and money …
efficient operation of the Internet. It can lead to website downtime, lost time and money …
Detection of application-layer DDoS attacks produced by various freely accessible toolkits using machine learning
Distributed Denial of Service (DDoS) attacks are a growing threat to online services, and
various methods have been developed to detect them. However, past research has mainly …
various methods have been developed to detect them. However, past research has mainly …
Iot network cybersecurity assessment with the associated random neural network
This paper proposes a method to assess the security of an device, or IP address, IoT
network by simultaneously identifying all the compromised IoT devices and IP addresses. It …
network by simultaneously identifying all the compromised IoT devices and IP addresses. It …
Machine learning and deep learning approaches in IoT
The internet is a booming sector for exchanging information because of all the gadgets in
today's world. Attacks on Internet of Things (IoT) devices are alarming as these devices …
today's world. Attacks on Internet of Things (IoT) devices are alarming as these devices …
Dynamic malware attack dataset leveraging virtual machine monitor audit data for the detection of intrusions in cloud
AAR Melvin, GJW Kathrine, SS Ilango… - Transactions on …, 2022 - Wiley Online Library
In this new era of cloud computing, Intrusion Detection System (IDS) is very essential for the
continual monitoring of computing resources for signs of compromise since the number of …
continual monitoring of computing resources for signs of compromise since the number of …
Comparative study of AI-enabled DDoS detection technologies in SDN
Software-defined networking (SDN) is becoming the standard for the management of
networks due to its scalability and flexibility to program the network. SDN provides many …
networks due to its scalability and flexibility to program the network. SDN provides many …
On the empirical effectiveness of unrealistic adversarial hardening against realistic adversarial attacks
While the literature on security attacks and defenses of Machine Learning (ML) systems
mostly focuses on unrealistic adversarial examples, recent research has raised concern …
mostly focuses on unrealistic adversarial examples, recent research has raised concern …