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

Force Classification

Force Classification

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,...

Methods of Classification and Identification

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...

MALDI-TOF Mass Spectrometry

MALDI-TOF Mass Spectrometry

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...

You might also read

Related Articles

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

Sort by

Same author

BIO26-035: Validation of an Electronic Health Record Phenotype for Advanced Solid Cancer Across Five University of California Health Systems.

Journal of the National Comprehensive Cancer Network : JNCCN·2026

Same author

Privacy-preserving verification of preprocessing in federated learning for genomic data.

JAMIA open·2026

Same author

BIO26-035: MTAP Deficiency in Thoracic Oncology: Bridging Genomic Loss, Survival Deficits, and Immunosuppressive Microenvironments.

Journal of the National Comprehensive Cancer Network : JNCCN·2026

Same author

Sustainable Personalized Home Care for Pandemic Management: A Service-Oriented Approach.

Digital government (New York, N.Y.)·2026

Same author

Semantically Correct Policy Mining and Enforcement for Attribute based Access Control.

ACM transactions on Internet technology·2026

Same author

Performance Analysis of Dynamic ABAC Systems using a Queuing Theoretic Framework.

Computers & security·2026

Same journal

Engravings, Secrets, and Interpretability of Neural Networks.

IEEE transactions on emerging topics in computing·2025

Same journal

The Indoor Predictability of Human Mobility: Estimating Mobility with Smart Home Sensors.

IEEE transactions on emerging topics in computing·2023

Same journal

Enabling Attribute-based Access Control in NoSQL Databases.

IEEE transactions on emerging topics in computing·2023

Same journal

Privacy-Preserving Deep Learning NLP Models for Cancer Registries.

IEEE transactions on emerging topics in computing·2022

Same journal

Multi-Person Activity Recognition in Continuously Monitored Smart Homes.

IEEE transactions on emerging topics in computing·2022

Same journal

BD2K Training Coordinating Center's ERuDIte: the Educational Resource Discovery Index for Data Science.

IEEE transactions on emerging topics in computing·2022

See all related articles

Search research articles

Home
Malite: Lightweight Malware Detection And Classification For Constrained Devices.

Home
Malite: Lightweight Malware Detection And Classification For Constrained Devices.

Related Experiment Video

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

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

Published on: October 27, 2023

MALITE: Lightweight Malware Detection and Classification for Constrained Devices.

Sidharth Anand¹, Barsha Mitra², Soumyadeep Dey³

¹University of California, San Diego, USA.

IEEE Transactions on Emerging Topics in Computing

|October 17, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces MALITE, a lightweight malware analysis system for resource-constrained devices. MALITE accurately detects and classifies malware using minimal memory and battery power, outperforming existing methods.

Keywords:

Constrained environment Lightweight Malware classification Malware detection

More Related Videos

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

Published on: February 24, 2023

Related Experiment Videos

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

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

Published on: October 27, 2023

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

Author Spotlight: A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

Published on: February 24, 2023

Area of Science:

Cybersecurity
Machine Learning
Computer Forensics

Background:

Malware poses a significant threat to all computing devices, especially resource-constrained ones like IoT devices.
Existing machine learning malware analysis methods are often too resource-intensive for these environments.

Purpose of the Study:

To develop a lightweight malware analysis system (MALITE) suitable for resource-constrained devices.
To accurately distinguish benign binaries from malicious ones and classify malware families.

Main Methods:

MALITE converts binaries into images (grayscale or RGB) for analysis.
It employs two novel, lightweight architectures: MALITE-MN (neural network) and MALITE-HRF (random forest with histogram features).

Main Results:

MALITE-MN and MALITE-HRF demonstrate high accuracy in malware identification and classification.
Both methods significantly reduce memory and computational resource consumption compared to state-of-the-art baselines.

Conclusions:

MALITE offers an effective and resource-efficient solution for malware analysis on constrained devices.
The system's low resource requirements make it ideal for mobile and IoT security applications.