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

Detection of Black Holes01:10

Detection of Black Holes

2.3K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.3K
Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

788
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
788
MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

5.1K
Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
5.1K
Apparent Weight01:09

Apparent Weight

8.4K
True weight is the measure of the gravitational force acting on an object. However, if the object accelerates, its measured weight is different from its true weight. Similar observations can be made when the object is submerged in water. An object's weight in water is its apparent weight, which is equal to the difference between its true weight and the buoyant forces.
Consider a person standing on a bathroom scale inside an elevator. If the scale is accurate at rest, its reading equals the...
8.4K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.8K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.8K

You might also read

Related Articles

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

Sort by
Same author

Intelligent Mobile Wireless Network for Toxic Gas Cloud Monitoring and Tracking.

Sensors (Basel, Switzerland)·2021
See all related articles

Related Experiment Video

Updated: Aug 29, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Lightweight On-Device Detection of Android Malware Based on the Koodous Platform and Machine Learning.

Mateusz Krzysztoń1, Bartosz Bok1, Marcin Lew1

  • 1NASK PIB, Kolska 12, 01-045 Warsaw, Poland.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary

BotSense Mobile enhances Android security by detecting unknown malware using a lightweight, edge-deployed neural network. This machine learning approach improves mobile safety, particularly for sensitive activities like e-banking.

Keywords:
Android securityKoodous platformedge computinglightweight modelsmachine learningmalware detectionmodels agingneural networks

More Related Videos

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out
07:10

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Published on: November 9, 2018

9.5K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

851

Related Experiment Videos

Last Updated: Aug 29, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K
Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out
07:10

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Published on: November 9, 2018

9.5K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

851

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Mobile Security

Background:

  • Android's dominance in mobile OS increases vulnerability to malware.
  • Smartphones are critical for sensitive activities like e-banking and e-identity verification.
  • Existing security tools like BotSense Mobile protect critical applications.

Purpose of the Study:

  • To introduce novel malware detection functionality for BotSense Mobile.
  • To develop and evaluate a machine learning model for identifying unknown malicious Android applications.
  • To ensure user data privacy by deploying the model on edge devices.

Main Methods:

  • Developed a lightweight neural network for malware detection.
  • Deployed the model on edge devices, utilizing only manifest-related features.
  • Conducted empirical analysis using recent data from the Koodous platform (May-June 2022).

Main Results:

  • The machine learning model achieved an f1-score of 0.77 and a precision of 0.9 on recent data.
  • Highlighted the challenge of machine learning model aging in malware detection.
  • Demonstrated the feasibility of on-device malware detection using lightweight models.

Conclusions:

  • The proposed machine learning model offers effective detection of unknown Android malware.
  • Edge deployment and manifest-feature utilization maintain user data privacy.
  • Addressing model aging is crucial for sustained performance in mobile malware detection systems.