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

Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

You might also read

Related Articles

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

Sort by
Same author

Sustained reduction in program-reported TB death rate in six districts following Tamil Nadu <i>Kasanoi Erappila Thittam</i> in southern India.

Global health action·2026
Same author

Approaches To Managing Relapsed Myeloma: Switching Drug Class or Retreatment With Same Drug Class?

Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion·2025
Same author

India's 2021 differentiated TB care guidance: Is it feasible to implement and act upon?

The Indian journal of tuberculosis·2025
Same author

Plant-based calcitriol reduced the requirements of calcium and available phosphorus in broiler chicken diet.

British poultry science·2025
Same author

Practical guidance on the prevention and management of infection in multiple myeloma patients: A case-based approach.

Blood reviews·2025
Same author

6-Bromo-9,9-diethyl-<i>N</i>,<i>N</i>-di-phenyl-fluoren-2-amine.

IUCrData·2025
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
See all related articles

Related Experiment Videos

Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

S Ganapathy1, P Yogesh, A Kannan

  • 1Department of Information Science and Technology, Anna University, Guindy, Chennai, India. ganapathy.sannasi@gmail.com

Computational Intelligence and Neuroscience
|October 12, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent agent-based intrusion detection model for mobile ad hoc networks. The new system effectively detects network intrusions with a low false alarm rate and high detection accuracy.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Network Security

Background:

  • Traditional intrusion detection systems (IDS) often suffer from high false alarm rates.
  • Mobile ad hoc networks (MANETs) present unique challenges for intrusion detection due to their dynamic nature.

Purpose of the Study:

  • To propose a novel intelligent agent-based intrusion detection model for MANETs.
  • To enhance intrusion detection accuracy and reduce false alarms in network security.

Main Methods:

  • Utilized attribute selection, outlier detection, and enhanced multiclass Support Vector Machine (SVM) classification.
  • Developed two new algorithms: Intelligent Agent Weighted Distance Outlier Detection and Intelligent Agent-based Enhanced Multiclass SVM.
  • Implemented an effective preprocessing technique to improve detection and reduce processing time.

Main Results:

  • The proposed model demonstrated a high detection rate for network anomalies.
  • Achieved a significantly low false alarm rate compared to existing methods.
  • Validated performance using the KDD Cup 99 dataset.

Conclusions:

  • The intelligent agent-based model offers a robust solution for intrusion detection in MANETs.
  • The combination of proposed techniques effectively addresses limitations of traditional IDS.
  • The system shows promise for real-world network security applications.