On the public citation graph

On the Detection of Low-Rate Denial of Service Attacks at Transport and Application Layers

https://doi.org/10.3390/electronics10172105

0 signed · 69 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/dac.3497openalex-snapshot

Detection and defense of DDoS attack–based on deep learning in OpenFlow ‐based SDN

inferredcites →https://doi.org/10.1007/978-3-319-58469-0_2openalex-snapshot

Slow TCAM Exhaustion DDoS Attack

inferredcites →https://doi.org/10.1007/978-3-540-24680-0_68openalex-snapshot

Multifractal Characteristic Quantities of Network Traffic Models

inferredcites →https://doi.org/10.1007/978-3-642-22540-6_42openalex-snapshot

Evaluating Machine Learning Algorithms for Detecting DDoS Attacks

inferredcites →https://doi.org/10.1007/s10489-018-1141-2openalex-snapshot

Semi-supervised machine learning approach for DDoS detection

inferredcites →https://doi.org/10.1016/j.comnet.2006.07.002openalex-snapshot

Evaluation of a low-rate DoS attack against iterative servers

inferredcites →https://doi.org/10.1016/j.comnet.2012.07.003openalex-snapshot

Flow level detection and filtering of low-rate DDoS

inferredcites →https://doi.org/10.1016/j.comnet.2017.03.018openalex-snapshot

Detecting HTTP-based application layer DoS attacks on web servers in the presence of sampling

inferredcites →https://doi.org/10.1016/j.dcan.2020.04.002openalex-snapshot

The detection method of low-rate DoS attack based on multi-feature fusion

inferredcites →https://doi.org/10.1016/j.future.2018.07.017openalex-snapshot

An early detection of low rate DDoS attack to SDN based data center networks using information distance metrics

inferredcites →https://doi.org/10.1016/j.ipl.2018.06.001openalex-snapshot

Low rate cloud DDoS attack defense method based on power spectral density analysis

inferredcites →https://doi.org/10.1016/j.jpdc.2006.04.007openalex-snapshot

Collaborative detection and filtering of shrew DDoS attacks using spectral analysis

inferredcites →https://doi.org/10.1109/access.2020.2967478openalex-snapshot

Low-Rate DDoS Attack Detection Based on Factorization Machine in Software Defined Network

inferredcites →https://doi.org/10.1109/access.2020.2976609openalex-snapshot

Low-Rate DoS Attacks, Detection, Defense, and Challenges: A Survey

inferredcites →https://doi.org/10.1109/access.2020.3019330openalex-snapshot

A Flexible SDN-Based Architecture for Identifying and Mitigating Low-Rate DDoS Attacks Using Machine Learning

inferredcites →https://doi.org/10.1109/ccst.2019.8888419openalex-snapshot

Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy

inferredcites →https://doi.org/10.1109/ceec.2016.7835914openalex-snapshot

Change-point cloud DDoS detection using packet inter-arrival time

inferredcites →https://doi.org/10.1109/cloudtech.2017.8284731openalex-snapshot

DDoS attack detection using machine learning techniques in cloud computing environments

inferredcites →https://doi.org/10.1109/cscloud.2017.58openalex-snapshot

Machine Learning Based DDoS Attack Detection from Source Side in Cloud

inferredcites →https://doi.org/10.1109/hpcc/smartcity/dss.2019.00076openalex-snapshot

Low-Rate DoS Attack Detection Based on Improved Logistic Regression

inferredcites →https://doi.org/10.1109/hpcc/smartcity/dss.2019.00164openalex-snapshot

PCA-SVM-Based Approach of Detecting Low-Rate DoS Attack

inferredcites →https://doi.org/10.1109/icina.2010.5636786openalex-snapshot

An advanced entropy-based DDOS detection scheme

inferredcites →https://doi.org/10.1109/icnp.2004.1348109openalex-snapshot

Exploiting the transients of adaptation for roq attacks on internet resources

inferredcites →https://doi.org/10.1109/icoac.2014.7229711openalex-snapshot

DDoS detection and analysis in SDN-based environment using support vector machine classifier

inferredcites →https://doi.org/10.1109/lcn.2010.5735752openalex-snapshot

Lightweight DDoS flooding attack detection using NOX/OpenFlow

inferredcites →https://doi.org/10.1109/smartcomp.2017.7946998openalex-snapshot

DeepDefense: Identifying DDoS Attack via Deep Learning

inferredcites →https://doi.org/10.1109/smartworld.2018.00236openalex-snapshot

A Low-Rate DoS Attack Detection Method Based on Hilbert Spectrum and Correlation

inferredcites →https://doi.org/10.1109/spw.2018.00013openalex-snapshot

Machine Learning DDoS Detection for Consumer Internet of Things Devices

inferredcites →https://doi.org/10.1109/tdsc.2015.2443807openalex-snapshot

Low-Rate DoS Attacks Detection Based on Network Multifractal

inferredcites →https://doi.org/10.1109/tifs.2011.2107320openalex-snapshot

Low-Rate DDoS Attacks Detection and Traceback by Using New Information Metrics

inferredcites →https://doi.org/10.1145/1879141.1879186openalex-snapshot

Revisiting the case for a minimalist approach for network flow monitoring

inferredcites →https://doi.org/10.1145/2808797.2808810openalex-snapshot

Detectability of Low-Rate HTTP Server DoS Attacks using Spectral Analysis

inferredcites →https://doi.org/10.1145/2934872.2934906openalex-snapshot

One Sketch to Rule Them All

inferredcites →https://doi.org/10.1145/863955.863966openalex-snapshot

Low-rate TCP-targeted denial of service attacks

inferredcites →https://doi.org/10.1186/s13638-016-0623-3openalex-snapshot

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

inferredcites →https://doi.org/10.18844/ijire.v8i1.6445openalex-snapshot

Semi supervised machine learning approach for DDOS detection

inferredcites →https://doi.org/10.4108/eai.28-12-2017.153515openalex-snapshot

A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN)

inferredcites →https://doi.org/10.48550/arxiv.1409.3215openalex-snapshot

Sequence to Sequence Learning with Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/ett.4758openalex-snapshot

SDN and application layer DDoS attacks detection in IoT devices by attention‐based Bi‐LSTM‐CNN

inferred← citeshttps://doi.org/10.1007/s11277-026-11940-6openalex-snapshot

Approximate Entropy-Based DL Model with Adaptive Topology and Clustering Restriction for Semi-Flooding and Low-rate Attack Detection and Mitigation

inferred← citeshttps://doi.org/10.1007/s13369-022-07517-7openalex-snapshot

Use of Machine Learning for Web Denial-of-Service Attacks: A Multivocal Literature Review

inferred← citeshttps://doi.org/10.1007/s41870-023-01634-8openalex-snapshot

Detection of non-periodic low-rate denial of service attacks in software defined networks using machine learning

inferred← citeshttps://doi.org/10.1016/j.cose.2025.104508openalex-snapshot

Light-weight slow-rate attack detection framework for resource-constrained Industrial Cyber–Physical Systems

inferred← citeshttps://doi.org/10.1016/j.inffus.2024.102922openalex-snapshot

AI-driven fusion with cybersecurity: Exploring current trends, advanced techniques, future directions, and policy implications for evolving paradigms– A comprehensive review

inferred← citeshttps://doi.org/10.1016/j.knosys.2026.115995openalex-snapshot

A predictive neural network architecture for early detection of low-rate cyberattacks

inferred← citeshttps://doi.org/10.1016/j.procs.2026.06.351openalex-snapshot

Low-rate DDoS attack detection with low False Positives using Hybrid DL approaches

inferred← citeshttps://doi.org/10.1016/j.rico.2024.100512openalex-snapshot

Understanding issues and challenges posed by LDoS, FRC attacks on cloud environment

inferred← citeshttps://doi.org/10.1109/access.2023.3291686openalex-snapshot

On Early Detection of Anomalous Network Flows

inferred← citeshttps://doi.org/10.1109/ccece68150.2026.11610330openalex-snapshot

Enhancing Low-Rate DDoS Detection via Feature Engineering: Supervised and Unsupervised Learning

inferred← citeshttps://doi.org/10.1109/csr61664.2024.10679441openalex-snapshot

Attack-Specific Feature Construction to Detect Malicious TCP Flows

inferred← citeshttps://doi.org/10.1109/ctus-emea68989.2025.11592252openalex-snapshot

Multi-Class Network Intrusion Detection Using Flow-Based Features: A Machine Learning Approach

inferred← citeshttps://doi.org/10.1109/icodse59534.2023.10291240openalex-snapshot

Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

inferred← citeshttps://doi.org/10.1134/s0361768826700337openalex-snapshot

Adaptive Methods and Tools for Shallow Packet Inspection in Anomaly Detection within Encrypted Network Traffic

inferred← citeshttps://doi.org/10.1140/epjp/s13360-025-07213-zopenalex-snapshot

DDoSAE: a novel deep autoencoder framework for low-rate DDoS attack detection

inferred← citeshttps://doi.org/10.12785/ijcds/140184openalex-snapshot

REST-API based DDoS Detection Using Random Forest Classifier in a Platform as a Service Cloud Environment

inferred← citeshttps://doi.org/10.30574/wjaets.2024.11.2.0098openalex-snapshot

TCP/IP stack transport layer performance, privacy, and security issues

inferred← citeshttps://doi.org/10.30574/wjarr.2024.21.3.0886openalex-snapshot

A survey of transmission control protocol variants

inferred← citeshttps://doi.org/10.32604/cmc.2025.065260openalex-snapshot

Renovated Random Attribute-Based Fennec Fox Optimized Deep Learning Framework in Low-Rate DoS Attack Detection in IoT

inferred← citeshttps://doi.org/10.3390/a19080653openalex-snapshot

Artificial Intelligence for Cybersecurity: A Scoping Survey of Paradigms, Applications, and Emerging Trends

inferred← citeshttps://doi.org/10.3390/drones7070430openalex-snapshot

Cyber4Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones

inferred← citeshttps://doi.org/10.3390/electronics10232926openalex-snapshot

Intelligent Cyber-Security System for IoT-Aided Drones Using Voting Classifier

inferred← citeshttps://doi.org/10.3390/electronics12153324openalex-snapshot

A Rapidly Adaptive Collision Backoff Algorithm for Improving the Throughput in WLANs

inferred← citeshttps://doi.org/10.3390/network2040036openalex-snapshot

Detection of Malicious Network Flows with Low Preprocessing Overhead

inferred← citeshttps://doi.org/10.3390/s22072630openalex-snapshot

Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective

inferred← citeshttps://doi.org/10.3390/s23167154openalex-snapshot

An Intelligent Framework for Cyber–Physical Satellite System and IoT-Aided Aerial Vehicle Security Threat Detection

inferred← citeshttps://doi.org/10.4236/jcc.2025.132016openalex-snapshot

Weighted Voting Ensemble Model Integrated with IoT for Detecting Security Threats in Satellite Systems and Aerial Vehicles

inferred← citeshttps://doi.org/10.5281/zenodo.164238openalex-snapshot

Figures 116 – 126. Taracus Ubicki. Fig. 116, Female Palpus. Fig. 117, Male Palpus. Figs. 118, Penis. Fig. 119, Glans Penis. Fig. 120 In The Harvestman Genus Taracus Simon 1879, And The New Genus Oskoron (Opiliones: Ischyropsalidoidea: Taracidae)

inferred← citeshttps://doi.org/10.5753/wgrs.2023.766openalex-snapshot

Machine Learning for Detection of Distributed Denial-of-Service Attacks from Queries Executed in DBMS

inferred← citeshttps://doi.org/10.7717/peerj-cs.2162openalex-snapshot

A DoS attack detection method based on adversarial neural network

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.