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

Viruses with RNA Genomes

Viruses with RNA Genomes

RNA viruses are categorized into positive-strand, negative-strand, or double-stranded groups based on their genomic structure and replication mechanisms. This classification dictates how they exploit host cellular machinery for protein synthesis and replication. Some RNA viruses also utilize reverse transcription as part of their life cycle, further diversifying their replication strategies.Positive-Strand RNA VirusesPositive-strand RNA viruses have genomes that function directly as messenger...

Entropy Change in Reversible Processes

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.

Standard Entropy Change for a Reaction

Standard Entropy Change for a Reaction

Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.

You might also read

Related Articles

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

Sort by

Same author

Neutralization Method of Ransomware Detection Technology Using Format Preserving Encryption.

Sensors (Basel, Switzerland)·2023

Same author

Effective Ransomware Detection Using Entropy Estimation of Files for Cloud Services.

Sensors (Basel, Switzerland)·2023

Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026

Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026

Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026

Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026

Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026

Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 2, 2025

Optimization of a Quantitative Micro-neutralization Assay

Optimization of a Quantitative Micro-neutralization Assay

Published on: December 14, 2016

A Method for Neutralizing Entropy Measurement-Based Ransomware Detection Technologies Using Encoding Algorithms.

Jaehyuk Lee¹, Kyungroul Lee¹

¹School of Computer Software, Daegu Catholic University, Gyeongsan 38430, Korea.

Entropy (Basel, Switzerland)

|February 25, 2022

Summary

This summary is machine-generated.

Attackers can bypass ransomware detection by encoding encrypted files, lowering their entropy. This study proposes advanced entropy measurement to counter these evasion techniques, enhancing ransomware defense.

Keywords:

encoding algorithms entropy measurement malicious code ransomware

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Detection of Neutralization-sensitive Epitopes in Antigens Displayed on Virus-Like Particle VLP-Based Vaccines Using a Capture Assay

Detection of Neutralization-sensitive Epitopes in Antigens Displayed on Virus-Like Particle VLP-Based Vaccines Using a Capture Assay

Published on: February 10, 2022

Related Experiment Videos

Last Updated: Oct 2, 2025

Optimization of a Quantitative Micro-neutralization Assay

Optimization of a Quantitative Micro-neutralization Assay

Published on: December 14, 2016

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Detection of Neutralization-sensitive Epitopes in Antigens Displayed on Virus-Like Particle VLP-Based Vaccines Using a Capture Assay

Detection of Neutralization-sensitive Epitopes in Antigens Displayed on Virus-Like Particle VLP-Based Vaccines Using a Capture Assay

Published on: February 10, 2022

Area of Science:

Computer Science
Cybersecurity
Information Security

Background:

Ransomware encrypts files, increasing entropy, which is used for detection.
Attackers now use encoding (e.g., Base64) to mask entropy increases, evading detection.

Purpose of the Study:

To analyze the limitations of current entropy-based ransomware detection.
To propose a method to neutralize ransomware detection by employing sophisticated entropy measurement techniques.

Main Methods:

Analyzing existing entropy measurement-based ransomware detection.
Investigating the impact of various encoding algorithms (including Base64) on file entropy.
Developing a more advanced entropy measurement approach.

Main Results:

Demonstrated how encoding algorithms can neutralize entropy-based ransomware detection.
Identified specific limitations of current detection methods when faced with encoded files.
Proposed a refined entropy measurement strategy.

Conclusions:

Current entropy-based ransomware detection is vulnerable to encoding techniques.
Advanced entropy measurement methods are necessary to counter sophisticated evasion tactics.
The proposed method offers a more robust defense against evolving ransomware threats.