Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Signals

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...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

You might also read

Related Articles

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

Sort by

Same author

Siamese-based metric joint learning for intent detection and slot filling using triplet loss optimization.

Scientific reports·2025

Same author

Enhancing IDS for the IoMT based on advanced features selection and deep learning methods to increase the model trustworthiness.

PloS one·2025

Same author

An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks.

Scientific reports·2025

Same author

Joint intent detection and slot filling with syntactic and semantic features using multichannel CNN-BiLSTM.

PeerJ. Computer science·2024

Same author

An efficient intrusion detection model based on convolutional spiking neural network.

Scientific reports·2024

Same author

A Kullback-Liebler divergence-based representation algorithm for malware detection.

PeerJ. Computer science·2023

Search research articles

Related Experiment Video

Updated: Jul 17, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation.

Taqwa Ahmed Alhaj¹, Maheyzah Md Siraj¹, Anazida Zainal¹

¹Information Assurance and Security Research Group, Faculty of Computing, Universiti Teknologi Malaysia, UTM, Johor Bahru, Johor, Malaysia.

|November 29, 2016

Summary

This study introduces a two-tier feature selection method to improve intrusion detection alert correlation. The approach enhances attack step identification and clustering accuracy by selecting significant alert features.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Jul 17, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Cybersecurity
Intrusion Detection Systems
Machine Learning

Background:

Structurally based alert correlation groups intrusion detection alerts by feature similarity to identify attack steps.
Manual feature selection in prior research led to inaccurate attack step identification and inconsistent clustering performance.
Existing systems struggle with large, noisy datasets, making alert analysis difficult and error-prone.

Purpose of the Study:

To enhance structural-based alert correlation by selecting accurate and significant alert features.
To improve the representation of attack steps for more effective analysis.
To address limitations of manual feature selection and noisy data in intrusion detection.

Main Methods:

A novel two-tier feature selection method is proposed.
The first tier ranks features using information gain entropy.
The second tier incorporates additional features with superior discriminative ability.

Main Results:

The proposed method effectively selects significant features for alert correlation.
Enhanced feature selection leads to improved clustering accuracy.
Performance was validated using the DARPA intrusion detection dataset.

Conclusions:

The two-tier feature selection method significantly enhances structural-based alert correlation.
Accurate feature selection is crucial for improving intrusion detection and attack step identification.
The findings contribute to more robust and accurate alert correlation systems.