A systematic literature review on machine learning and deep learning approaches for detecting DDoS attacks in software-defined networking

AA Bahashwan, M Anbar, S Manickam, TA Al-Amiedy… - Sensors, 2023 - mdpi.com
Software-defined networking (SDN) is a revolutionary innovation in network technology with
many desirable features, including flexibility and manageability. Despite those advantages …

A comprehensive survey on knowledge-defined networking

PADSN Wijesekara, S Gunawardena - Telecom, 2023 - mdpi.com
Traditional networking is hardware-based, having the control plane coupled with the data
plane. Software-Defined Networking (SDN), which has a logically centralized control plane …

Network anomaly detection using LSTM based autoencoder

M Said Elsayed, NA Le-Khac, S Dev… - Proceedings of the 16th …, 2020 - dl.acm.org
Anomaly detection aims to discover patterns in data that do not conform to the expected
normal behaviour. One of the significant issues for anomaly detection techniques is the …

Machine-learning-enabled ddos attacks detection in p4 programmable networks

F Musumeci, AC Fidanci, F Paolucci, F Cugini… - Journal of Network and …, 2022 - Springer
Abstract Distributed Denial of Service (DDoS) attacks represent a major concern in modern
Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in …

Network anomaly detection using memory-augmented deep autoencoder

B Min, J Yoo, S Kim, D Shin, D Shin - IEEE Access, 2021 - ieeexplore.ieee.org
In recent years, attacks on network environments continue to rapidly advance and are
increasingly intelligent. Accordingly, it is evident that there are limitations in existing …

Comparative analysis of binary and one-class classification techniques for credit card fraud data

JL Leevy, J Hancock, TM Khoshgoftaar - Journal of Big Data, 2023 - Springer
The yearly increase in incidents of credit card fraud can be attributed to the rapid growth of e-
commerce. To address this issue, effective fraud detection methods are essential. Our …

An intrusion detection method based on stacked sparse autoencoder and improved gaussian mixture model

T Zhang, W Chen, Y Liu, L Wu - Computers & Security, 2023 - Elsevier
The analysis of a substantial portion of network data is a requirement for almost any
machine learning-based network intrusion detection method. High dimension features, a …

Effective one-class classifier model for memory dump malware detection

M Al-Qudah, Z Ashi, M Alnabhan… - Journal of Sensor and …, 2023 - mdpi.com
Malware complexity is rapidly increasing, causing catastrophic impacts on computer
systems. Memory dump malware is gaining increased attention due to its ability to expose …

Improved DDoS detection utilizing deep neural networks and feedforward neural networks as autoencoder

AL Yaser, HM Mousa, M Hussein - Future Internet, 2022 - mdpi.com
Software-defined networking (SDN) is an innovative network paradigm, offering substantial
control of network operation through a network's architecture. SDN is an ideal platform for …

Shieldrnn: A distributed flow-based ddos detection solution for iot using sequence majority voting

F Alasmary, S Alraddadi, S Al-Ahmadi… - IEEE Access, 2022 - ieeexplore.ieee.org
The Distributed Denial of Service (DDoS) attack is considered one of the most critical threats
on the Internet, blocking legitimate users from accessing online services. Botnets have …