Optimization of vector convolutional deep neural network using binary real cumulative incarnation for detection of distributed denial of service attacks

NGB Amma, S Selvakumar - Neural Computing and Applications, 2022 - Springer
In today's technological world, distributed denial of service (DDoS) attacks threaten Internet
users by flooding huge network traffic to make critical Internet services unavailable to …

Botnet detection in the internet of things through all-in-one deep autoencoding

M Catillo, A Pecchia, U Villano - … of the 17th International Conference on …, 2022 - dl.acm.org
In the past years Internet of Things (IoT) has received increasing attention by academia and
industry due to the potential use in several human activities; however, IoT devices are …

Augmenting IoT intrusion detection system performance using deep neural network

N Sayed, M Shoaib, W Ahmed… - Computers …, 2022 - open-access.bcu.ac.uk
Due to their low power consumption and computing power, Internet of Things (IoT) devices
are difficult to secure, and the rapid growth of IoT devices in the home increases the risk of …

Anomaly based network intrusion detection for IoT attacks using convolution neural network

B Sharma, L Sharma, C Lal - 2022 IEEE 7th International …, 2022 - ieeexplore.ieee.org
IoT is widely used in many fields, and with the expansion of the network and increment of
devices, there is the dynamic growth of data in IoT systems, making the system more …

Enhanced ids with deep learning for iot-based smart cities security

C Hazman, A Guezzaz, S Benkirane… - Tsinghua Science and …, 2024 - ieeexplore.ieee.org
Cyberattacks against highly integrated Internet of Things (IoT) servers, apps, and telecoms
infrastructure are rapidly increasing when issues produced by IoT networks go unnoticed for …

[PDF][PDF] Assessment of existing cyber-attack detection models for web-based systems

OG Awuor - Global Journal of Engineering and Technology …, 2023 - gjeta.com
In the current technological environment, different entities engage in intricate cyber security
approaches in order to counter damages and disruptions in web-based systems. The design …

Honey-block: Edge assisted ensemble learning model for intrusion detection and prevention using defense mechanism in IoT

E Ntizikira, L Wang, J Chen, K Saleem - Computer Communications, 2024 - Elsevier
Abstract The Internet of Things (IoT) has gained popularity with interconnected devices and
diverse network applications, leading to increased vulnerability of sensitive data to security …

Towards zero-shot flow-based cyber-security anomaly detection framework

M Komisarek, R Kozik, M Pawlicki, M Choraś - Applied Sciences, 2022 - mdpi.com
Network flow-based cyber anomaly detection is a difficult and complex task. Although
several approaches to tackling this problem have been suggested, many research topics …

Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection

B Alwasel, A Aldribi, M Alreshoodi, IS Alsukayti… - Applied Sciences, 2023 - mdpi.com
In the dynamic and ever-evolving realm of network security, the ability to accurately identify
and classify portscan attacks both inside and outside networks is of paramount importance …

Research trends in deep learning and machine learning for cloud computing security

YI Alzoubi, A Mishra, AE Topcu - Artificial Intelligence Review, 2024 - Springer
Deep learning and machine learning show effectiveness in identifying and addressing cloud
security threats. Despite the large number of articles published in this field, there remains a …