Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026

Related Experiment Video

Updated: Oct 12, 2025

Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

2.0K

Multiresolution dendritic cell algorithm for network anomaly detection.

David Limon-Cantu1, Vicente Alarcon-Aquino1

  • 1Department of Computing, Electronics and Mechatronics, Universidad de las Americas Puebla, San Andres Cholula, Puebla, Mexico.

Peerj. Computer Science
|November 22, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel network anomaly detection model inspired by the immune system. The Dendritic Cell Algorithm (DCA) combined with Multiresolution Analysis (MRA) achieved high accuracy in identifying network attacks.

Keywords:
Artificial immune systemsDendritic cell algorithmIntruder detection systemsMachine learningNetwork anomaly detectionWavelet transformsWavelets

More Related Videos

Analyzing Dendritic Morphology in Columns and Layers
08:41

Analyzing Dendritic Morphology in Columns and Layers

Published on: March 23, 2017

9.5K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

Related Experiment Videos

Last Updated: Oct 12, 2025

Automatic Identification of Dendritic Branches and their Orientation
06:08

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

2.0K
Analyzing Dendritic Morphology in Columns and Layers
08:41

Analyzing Dendritic Morphology in Columns and Layers

Published on: March 23, 2017

9.5K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Network anomaly detection is crucial for information security to prevent data breaches and service disruptions.
  • Intrusion Detection Systems (IDS) are vital for identifying malicious activities like denial-of-service attacks and backdoors.

Purpose of the Study:

  • To develop and evaluate a novel binary classifier for network anomaly detection.
  • To enhance network attack detection by combining the Dendritic Cell Algorithm (DCA) with Multiresolution Analysis (MRA) and a segmented deterministic DCA (S-dDCA).

Main Methods:

  • The proposed model utilizes the Maximal Overlap Discrete Wavelet Transform (MODWT) for signal decomposition and feature extraction.
  • A segmented deterministic DCA (S-dDCA) approach is employed for classifying time-frequency representations of network traffic data.
  • The model analyzes time-series data from high-level network features to distinguish between normal and anomalous traffic.

Main Results:

  • The MRA S-dDCA model achieved high accuracy across multiple benchmark datasets: NSL-KDD (97.37%), UNSW-NB15 (99.97%), CIC-IDS2017 (99.56%), and CSE-CIC-IDS2018 (99.75%).
  • The proposed approach outperformed state-of-the-art methods on the UNSW-NB15 and CSE-CIC-IDS2018 datasets.
  • Results on NSL-KDD and CIC-IDS2017 were competitive with existing machine learning techniques.

Conclusions:

  • The MRA S-dDCA model demonstrates significant effectiveness in network anomaly detection.
  • The immune system-inspired approach offers a robust solution for identifying contemporary network threats.
  • This method provides a competitive and high-performing alternative for intrusion detection systems.