Network Intrusion Detection System using Deep Learning Techniques

A Rathee, P Malik, MK Parida - 2023 International Conference …, 2023 - ieeexplore.ieee.org
The importance of cyber security is rising as technology continues to develop throughout the
world. As a result, fraudsters are coming up with innovative and highly technical ways to …

Sin-Cos-bIAVOA: A new feature selection method based on improved African vulture optimization algorithm and a novel transfer function to DDoS attack detection

Z Sharifian, B Barekatain, AA Quintana… - Expert Systems with …, 2023 - Elsevier
Abstract Internet of Things (IoT) services and devices have raised numerous challenges
such as connectivity, computation, and security. Therefore, networks should provide and …

Threat actors' tenacity to disrupt: Examination of major cybersecurity incidents

OI Falowo, S Popoola, J Riep, VA Adewopo… - IEEE Access, 2022 - ieeexplore.ieee.org
The exponential growth in the interconnectedness of people and devices, as well as the
upward trend in cyberspace usage will continue to lead to a greater reliance on the internet …

Autoencoder for Design of Mitigation Model for DDOS Attacks via M‐DBNN

A Agrawal, R Singh, M Khari, S Vimal… - … and Mobile Computing, 2022 - Wiley Online Library
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 …

MAGNETO and deepinsight: extended image translation with semantic relationships for classifying attack data with machine learning models

A Dunmore, A Dunning, J Jang-Jaccard, F Sabrina… - Electronics, 2023 - mdpi.com
The translation of traffic flow data into images for the purposes of classification in machine
learning tasks has been extensively explored in recent years. However, the method of …

Automated Network Incident Identification through Genetic Algorithm-Driven Feature Selection

A Aksoy, L Valle, G Kar - Electronics, 2024 - mdpi.com
The cybersecurity landscape presents daunting challenges, particularly in the face of Denial
of Service (DoS) attacks such as DoS Http Unbearable Load King (HULK) attacks and DoS …

Performance evaluation of machine learning models for distributed denial of service attack detection using improved feature selection and hyper‐parameter …

B Habib, F Khursheed - Concurrency and Computation …, 2022 - Wiley Online Library
This article gives the framework of extensive experimentation of various machine learning
models to detect distributed denial of service attacks (DDoS). We use six‐tier feature ranking …

Detection and mitigation of DDoS attacks in internet of things using a fog computing hybrid approach

KF Hassan, ME Manaa - Bulletin of Electrical Engineering and Informatics, 2022 - beei.org
The introduction of a new technology has aided the exponential growth of the internet of
things (IoT), allowing for the connecting of more devices in the IoT network to be made …

Cybersecurity Defence Mechanism Against DDoS Attack with Explainability

AM Mahmood, İ Avcı - Mesopotamian Journal of …, 2024 - journals.mesopotamian.press
Application-layer attacks (Layer 7 attacks), a form of distributed denial-of-service (DDoS)
aimed at web servers, have become a significant concern in cybersecurity because of their …

A deep CNN-based framework for distributed denial of services (DDoS) attack detection in internet of things (IoT)

BB Gupta, A Gaurav, V Arya, P Kim - Proceedings of the 2023 …, 2023 - dl.acm.org
As the number of connected devices continues to rise, protecting those networks against
DDoS attacks is more important than ever. Due to IoT devices' specific features and limited …