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A Malware-Detection Method Using Deep Learning to Fully Extract API Sequence Features

https://doi.org/10.3390/electronics14010167

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What this work says

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

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An opcode‐based technique for polymorphic Internet of Things malware detection

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A Method for Windows Malware Detection Based on Deep Learning

inferredcites →https://doi.org/10.1016/j.comnet.2020.107138openalex-snapshot

IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture

inferredcites →https://doi.org/10.1016/j.cose.2020.101760openalex-snapshot

A dynamic Windows malware detection and prediction method based on contextual understanding of API call sequence

inferredcites →https://doi.org/10.1016/j.cose.2020.102037openalex-snapshot

Recurrent neural network for detecting malware

inferredcites →https://doi.org/10.1016/j.cose.2021.102547openalex-snapshot

Jadeite: A novel image-behavior-based approach for Java malware detection using deep learning

inferredcites →https://doi.org/10.1016/j.cose.2022.102686openalex-snapshot

A novel deep framework for dynamic malware detection based on API sequence intrinsic features

inferredcites →https://doi.org/10.1016/j.cose.2022.102741openalex-snapshot

MalSPM: Metamorphic malware behavior analysis and classification using sequential pattern mining

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.cose.2024.103862openalex-snapshot

Bon-APT: Detection, attribution, and explainability of APT malware using temporal segmentation of API calls

inferredcites →https://doi.org/10.1016/j.eij.2024.100478openalex-snapshot

MalRed: An innovative approach for detecting malware using the red channel analysis of color images

inferredcites →https://doi.org/10.1016/j.engappai.2023.106030openalex-snapshot

A novel deep learning-based approach for malware detection

inferredcites →https://doi.org/10.1016/j.knosys.2024.111543openalex-snapshot

CNN-LSTM and transfer learning models for malware classification based on opcodes and API calls

inferredcites →https://doi.org/10.1109/cisce58541.2023.10142573openalex-snapshot

Government affairs message text classification based on RoBerta and TextCNN

inferredcites →https://doi.org/10.1109/iesys.2017.8233569openalex-snapshot

NLP-based approaches for malware classification from API sequences

inferredcites →https://doi.org/10.1109/tdsc.2021.3071092openalex-snapshot

A Coprocessor-Based Introspection Framework Via Intel Management Engine

inferredcites →https://doi.org/10.1109/tifs.2022.3152360openalex-snapshot

CruParamer: Learning on Parameter-Augmented API Sequences for Malware Detection

inferredcites →https://doi.org/10.1145/2016904.2016908openalex-snapshot

Malware images

inferredcites →https://doi.org/10.1145/3576915.3623077openalex-snapshot

SHERLOC: Secure and Holistic Control-Flow Violation Detection on Embedded Systems

inferredcites →https://doi.org/10.14722/ndss.2020.24167openalex-snapshot

You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis

inferredcites →https://doi.org/10.3390/electronics12153253openalex-snapshot

Deep Learning-Based Attack Detection and Classification in Android Devices

inferredcites →https://doi.org/10.3390/electronics13061092openalex-snapshot

A Malicious Program Behavior Detection Model Based on API Call Sequences

inferredcites →https://doi.org/10.3390/s24020580openalex-snapshot

Channel Features and API Frequency-Based Transformer Model for Malware Identification

inferredcites →https://doi.org/10.3390/sym15030758openalex-snapshot

A Malware Detection Approach Based on Deep Learning and Memory Forensics

What points at this work

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

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AI Based Malware Detection Using Machine Learning, Random Forest, Extra Tree Algorithms

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Multimodal Feature Fusion for Portable Executable Malware Detection and Classification

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Deconstructing android messaging clones: A hybrid reverse engineering and risk assessment framework

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Design of an improved validation model with dynamic attribution and graph-based anomaly analysis for malware forensics

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Feature Fusion-Based Ensemble Approach for Robust Malware Detection with Reduced False Positives

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KANPoolConvNeXt: an innovative malware detection method

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Cross-environment AI-based malware detection: A systematic review of Android applications, IoT ecosystems, and windows PE executables

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Detecting Malware in Portable Executable Files based on Reverse Engineering

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Malware Detection Using Transfer Learning with ResNet-50: An Image-Based Approach

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Explainable Deep Learning for Behavioral Malware Classification: An Attention-Based Analysis of API Call Sequences

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Deep Adversarial Learning for Phishing and Malware Detection in Cloud Systems

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Real Time Malware Detection using Explainable AI and Deep Learning

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Comparative Analysis of Machine Learning Techniques for Android Malware Detection

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TraGe: A Generic Packet Representation for Traffic Classification Based on Header-Payload Differences

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Intelligent malware detection method based on memory segments

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Dynamic Features for Robust Malware Detection: A Systematic Review, Taxonomy, and Practical Analysis Framework

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CoBeMPD: Weakly Supervised Malicious Package Detection and File-Level Localization via Multi-Instance Learning over Code and Behavior Semantics

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Innovative Method for Detecting Malware by Analysing API Request Sequences Based on a Hybrid Recurrent Neural Network for Applied Forensic Auditing

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An Explainable Deep Learning Pipeline for Malware Family Classification: GAF Image Encoding and API-Grounded LLM Interpretation

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A Machine Learning Approach for Malware Detection in Database Systems

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