On the public citation graph

Features Engineering for Malware Family Classification Based API Call

https://doi.org/10.3390/computers11110160

0 signed · 42 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-00470-5_23openalex-snapshot

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

inferredcites →https://doi.org/10.1007/978-3-030-86749-2_12openalex-snapshot

Malware Discernment Using Machine Learning

inferredcites →https://doi.org/10.1007/978-3-319-45719-2_11openalex-snapshot

AVclass: A Tool for Massive Malware Labeling

inferredcites →https://doi.org/10.1016/j.comnet.2019.06.015openalex-snapshot

A multi-dimensional machine learning approach to predict advanced malware

inferredcites →https://doi.org/10.1016/j.cose.2018.05.010openalex-snapshot

Early-stage malware prediction using recurrent neural networks

inferredcites →https://doi.org/10.1016/j.cose.2019.02.007openalex-snapshot

MalDAE: Detecting and explaining malware based on correlation and fusion of static and dynamic characteristics

inferredcites →https://doi.org/10.1016/j.cose.2019.04.005openalex-snapshot

A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding

inferredcites →https://doi.org/10.1016/j.cose.2019.101682openalex-snapshot

A novel method for malware detection on ML-based visualization technique

inferredcites →https://doi.org/10.1016/j.cose.2022.102871openalex-snapshot

Efficient and Robust Malware Detection Based on Control Flow Traces Using Deep Neural Networks

inferredcites →https://doi.org/10.1016/j.csi.2020.103443openalex-snapshot

Metamorphic malware identification using engine-specific patterns based on co-opcode graphs

inferredcites →https://doi.org/10.1016/j.future.2021.01.004openalex-snapshot

Intelligent Dynamic Malware Detection using Machine Learning in IP Reputation for Forensics Data Analytics

inferredcites →https://doi.org/10.1016/j.procs.2018.03.072openalex-snapshot

ASSCA: API based Sequence and Statistics features Combined malware detection Architecture

inferredcites →https://doi.org/10.1049/iet-ifs.2018.5268openalex-snapshot

Dynamic API call sequence visualisation for malware classification

inferredcites →https://doi.org/10.1109/access.2020.3019282openalex-snapshot

Multifamily Classification of Android Malware With a Fuzzy Strategy to Resist Polymorphic Familial Variants

inferredcites →https://doi.org/10.1109/access.2022.3168794openalex-snapshot

Deep-Ensemble and Multifaceted Behavioral Malware Variant Detection Model

inferredcites →https://doi.org/10.1109/eisic.2012.57openalex-snapshot

To Incorporate Sequential Dynamic Features in Malware Detection Engines

inferredcites →https://doi.org/10.1109/hora55278.2022.9800057openalex-snapshot

A Deep Learning Based Android Malware Detection System with Static Analysis

inferredcites →https://doi.org/10.1109/iccnc.2016.7440587openalex-snapshot

An approach for detection and family classification of malware based on behavioral analysis

inferredcites →https://doi.org/10.1109/siu.2019.8806571openalex-snapshot

Classification of Methamorphic Malware with Deep Learning(LSTM)

inferredcites →https://doi.org/10.1109/tii.2018.2822680openalex-snapshot

Detection of Malicious Code Variants Based on Deep Learning

inferredcites →https://doi.org/10.1109/trustcom.2013.36openalex-snapshot

Analyzing Malware by Abstracting the Frequent Itemsets in API Call Sequences

inferredcites →https://doi.org/10.1145/1743546.1743567openalex-snapshot

A tour through the visualization zoo

inferredcites →https://doi.org/10.1201/9781315151670openalex-snapshot

Data Science and Analytics with Python

inferredcites →https://doi.org/10.13052/jcsm2245-1439.1036openalex-snapshot

API Call-Based Malware Classification Using Recurrent Neural Networks

inferredcites →https://doi.org/10.3390/app12136672openalex-snapshot

MLMD—A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm

inferredcites →https://doi.org/10.3390/app12157546openalex-snapshot

Research on the Construction of Malware Variant Datasets and Their Detection Method

inferredcites →https://doi.org/10.5120/ijca2021921218openalex-snapshot

Behavioral Malware Detection using Deep Graph Convolutional Neural Networks

inferredcites →https://doi.org/10.5121/csit.2021.110106openalex-snapshot

Convolutional Neural Network for Malware Classification Based on API Call Sequence

inferredcites →https://doi.org/10.7717/peerj-cs.285openalex-snapshot

Deep learning based Sequential model for malware analysis using Windows exe API Calls

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4679260openalex-extracted

Bespoke Sequence of Transformations for an Enhanced Entropic Wavelet Energy Spectrum Discernment for Higher Efficacy Detection of Metamorphic Malware

inferred← citeshttps://doi.org/10.1007/s11416-026-00654-9openalex-snapshot

HELIX: malware detection and classification through temporal sequence modeling of system-call group behavior

inferred← citeshttps://doi.org/10.1016/j.compeleceng.2024.109233openalex-snapshot

Malware detection for mobile computing using secure and privacy-preserving machine learning approaches: A comprehensive survey

inferred← citeshttps://doi.org/10.1109/icera72709.2026.11666675openalex-snapshot

A Comparison of the Performance of Random Forest, SVM, and XGBoost for Malware Family Detection and Classification Using Dynamic Analysis-Based Class Imbalance Handling

inferred← citeshttps://doi.org/10.1109/isdfs60797.2024.10527239openalex-snapshot

Malware API Call-Based Multiclass-Classification Using Machine Learning and Deep Learning

inferred← citeshttps://doi.org/10.1109/msn63567.2024.00077openalex-snapshot

MalBET: A Multiclass Malware Detection Method Using Improved Bidirectional Encoders from Transformers

inferred← citeshttps://doi.org/10.1109/synasc61333.2023.00031openalex-snapshot

Using sequences of API Calls to identify and classify ransomware families

inferred← citeshttps://doi.org/10.22214/ijraset.2023.57649openalex-snapshot

A Static Approach for Malware Analysis: A Guide to Analysis Tools and Techniques

inferred← citeshttps://doi.org/10.3390/app13031972openalex-snapshot

Features Engineering to Differentiate between Malware and Legitimate Software

inferred← citeshttps://doi.org/10.3390/app14010092openalex-snapshot

TTDAT: Two-Step Training Dual Attention Transformer for Malware Classification Based on API Call Sequences

inferred← citeshttps://doi.org/10.3390/app14031015openalex-snapshot

Dynamic Malware Classification and API Categorisation of Windows Portable Executable Files Using Machine Learning

inferred← citeshttps://doi.org/10.3390/telecom4040034openalex-snapshot

Lightweight and Robust Malware Detection Using Dictionaries of API Calls

inferred← citeshttps://doi.org/10.38094/jastt42153openalex-snapshot

An Investigation of Quantum and Parallel Computing Effects on Malware Families Classification

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.