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Enhancing the detection of metamorphic malware using call graphs

Computers & security, 2014-10, Vol.46, p.62-78

2014

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Improving the Detection of Malware Behaviour Using Simplified Data Dependent API Call Graph

International Journal of Security and Its Applications, 2013-09, Vol.7 (5), p.29-42

2013

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Intelligent mobile malware detection using permission requests and API calls

Future generation computer systems, 2020-06, Vol.107, p.509-521

2020

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DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network

Future generation computer systems, 2021-02, Vol.115, p.844-856

2021

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A survey on machine learning-based malware detection in executable files

Journal of systems architecture, 2021-01, Vol.112, p.101861, Article 101861

2021

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An ensemble of pre-trained transformer models for imbalanced multiclass malware classification

Computers & security, 2022-10, Vol.121, p.102846, Article 102846

2022

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DMalNet: Dynamic malware analysis based on API feature engineering and graph learning

Computers & security, 2022-11, Vol.122, p.102872, Article 102872

2022

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Robust deep learning early alarm prediction model based on the behavioural smell for android malware

Computers & security, 2022-05, Vol.116, p.102670, Article 102670

2022

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A machine learning technique for Android malicious attacks detection based on API calls

Decision Science Letters, 2024-01, Vol.13 (1), p.29-44

2024

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MLDroid—framework for Android malware detection using machine learning techniques

Neural computing & applications, 2021-05, Vol.33 (10), p.5183-5240

2021

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XRan: Explainable deep learning-based ransomware detection using dynamic analysis

Computers & security, 2024-04, Vol.139, p.103703, Article 103703

2024

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

Knowledge-based systems, 2024-04, Vol.290, p.111543, Article 111543

2024

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

Computers & security, 2019-06, Vol.83, p.208-233

2019

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

Computers & security, 2020-05, Vol.92, p.101760-15, Article 101760

2020

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Recommending API Function Calls and Code Snippets to Support Software Development

IEEE transactions on software engineering, 2022-07, Vol.48 (7), p.2417-2438

2022

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Association rule-based malware classification using common subsequences of API calls

Applied soft computing, 2021-07, Vol.105, p.107234, Article 107234

2021

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API-MalDetect: Automated malware detection framework for windows based on API calls and deep learning techniques

Journal of network and computer applications, 2023-09, Vol.218, p.103704, Article 103704

2023

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Hybrids of support vector machine wrapper and filter based framework for malware detection

Future generation computer systems, 2016-02, Vol.55, p.376-390

2016

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MAAR: Robust features to detect malicious activity based on API calls, their arguments and return values

Engineering applications of artificial intelligence, 2017-03, Vol.59, p.93-102

2017

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Detection of malicious PE files using synthesized DNA artifacts

Computers & security, 2023-11, Vol.134, p.103457, Article 103457

2023

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

Computers & security, 2022-07, Vol.118, p.102741, Article 102741

2022

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Extensible Android Malware Detection and Family Classification Using Network-Flows and API-Calls

2019 International Carnahan Conference on Security Technology (ICCST), 2019, p.1-8

2019

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Deep learning for effective Android malware detection using API call graph embeddings

Soft computing (Berlin, Germany), 2020, Vol.24 (2), p.1027-1043

2020

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Android Malware Detection Using Deep Learning

Procedia computer science, 2021, Vol.184, p.847-852

2021

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TS-Mal: Malware detection model using temporal and structural features learning

Computers & security, 2024-05, Vol.140, p.103752, Article 103752

2024

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Enhancing the detection of metamorphic malware using call graphs

Improving the Detection of Malware Behaviour Using Simplified Data Dependent API Call Graph

Intelligent mobile malware detection using permission requests and API calls

DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network

A survey on machine learning-based malware detection in executable files

An ensemble of pre-trained transformer models for imbalanced multiclass malware classification

DMalNet: Dynamic malware analysis based on API feature engineering and graph learning

Robust deep learning early alarm prediction model based on the behavioural smell for android malware

A machine learning technique for Android malicious attacks detection based on API calls

MLDroid—framework for Android malware detection using machine learning techniques

XRan: Explainable deep learning-based ransomware detection using dynamic analysis

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

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

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

Recommending API Function Calls and Code Snippets to Support Software Development

Association rule-based malware classification using common subsequences of API calls

API-MalDetect: Automated malware detection framework for windows based on API calls and deep learning techniques

Hybrids of support vector machine wrapper and filter based framework for malware detection

MAAR: Robust features to detect malicious activity based on API calls, their arguments and return values

Detection of malicious PE files using synthesized DNA artifacts

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

Extensible Android Malware Detection and Family Classification Using Network-Flows and API-Calls

Deep learning for effective Android malware detection using API call graph embeddings

Android Malware Detection Using Deep Learning

TS-Mal: Malware detection model using temporal and structural features learning

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