Android Malware

Android malware detection is a critical area of cybersecurity research aiming to automatically identify malicious applications. Current research focuses on improving the accuracy and robustness of detection methods, employing various machine learning techniques including graph neural networks, support vector machines, and transformer-based models like BERT, often incorporating feature selection and data augmentation strategies to address challenges like obfuscation and adversarial attacks. These advancements are crucial for enhancing mobile security and protecting users from increasingly sophisticated malware threats, with ongoing efforts to improve model explainability and address issues of reproducibility and fairness in evaluation.

Papers

September 29, 2024

MASKDROID: Robust Android Malware Detection with Masked Graph Representations
Jingnan Zheng, Jiaohao Liu, An Zhang, Jun Zeng, Ziqi Yang, Zhenkai Liang, Tat-Seng Chua
Adversarial Attack Adversarial Example Android Malware

September 28, 2024

Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach
Safayat Bin Hakim, Muhammad Adil, Kamal Acharya, Houbing Herbert Song
Support Vector Machine Feature Wise Attention Based Deep Android Malware

September 11, 2024

Revisiting Static Feature-Based Android Malware Detection
Md Tanvirul Alam, Dipkamal Bhusal, Nidhi Rastogi
Machine Learning Malware Classification Android Malware Malware Analysis

August 29, 2024

August 27, 2024

Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations
Hamid Bostani, Zhengyu Zhao, Veelasha Moonsamy
Adversarial Example Adversarial Robustness Global Impact Spurious Correlation Android Malware Malware Classifier

May 6, 2024

Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid
Meryam Chaieb, Mostafa Anouar Ghorab, Mohamed Aymen Saied
Malware Detection Feature Embeddings Android Malware BERT Architecture

April 19, 2024

Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation
Harshit Kumar, Sudarshan Sharma, Biswadeep Chakraborty, Saibal Mukhopadhyay
Real Time Multiple Instance Learning Android Malware Malware Representation Novel Malware Malware Feature Malicious Training

March 1, 2024

Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks
Kawana Stalin, Mikias Berhanu Mekoya
Generative Adversarial Network Data Augmentation GAN Model Android Malware Wasserstein Generative Adversarial Network

February 5, 2024

Unraveling the Key of Machine Learning Solutions for Android Malware Detection
Jiahao Liu, Jun Zeng, Fabio Pierazzi, Lorenzo Cavallaro, Zhenkai Liang
Machine Learning Malware Detection Feature Engineering Android Malware Machine Learning Solution

January 30, 2024

ActDroid: An active learning framework for Android malware detection
Ali Muzaffar, Hani Ragab Hassen, Hind Zantout, Michael A Lones
Active Learning Concept Drift Malware Detection Active Learning Framework Malware Attack Android Malware

December 17, 2023

Android Malware Detection with Unbiased Confidence Guarantees
Harris Papadopoulos, Nestoras Georgiou, Charalambos Eliades, Andreas Konstantinidis
Machine Learning Random Forest Malware Detection Android Malware

December 11, 2023

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
Yuyang Zhou, Guang Cheng, Zongyao Chen, Shui Yu
Android Malware Adversarial Purification Evasion Attack Malware Datasets

October 24, 2023

Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection
Borja Molina-Coronado, Antonio Ruggia, Usue Mori, Alessio Merlo, Alexander Mendiburu, Jose Miguel-Alonso
Obfuscation Technique Static Analysis Android Malware Based Malware Detection

September 18, 2023

Efficient Concept Drift Handling for Batch Android Malware Detection Models
Molina-Coronado B., Mori U., Mendiburu A., Miguel-Alonso J
Active Learning Concept Drift Malware Detection Android Malware

September 5, 2023

Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting
Ping He, Yifan Xia, Xuhong Zhang, Shouling Ji
High Efficiency Android Malware Zero Knowledge QuEry Based Attack

June 12, 2023

On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities
Masoud Mehrabi Koushki, Ibrahim AbuAlhaol, Anandharaju Durai Raju, Yang Zhou, Ronnie Salvador Giagone, Huang Shengqiang
Machine Learning Timely Survey Technical Challenge New Machine Machine Learning Algorithm Malware Detection Malware Attack Android Malware Energy Pipeline

March 22, 2023

A Comparison of Graph Neural Networks for Malware Classification
Vrinda Malhotra, Katerina Potika, Mark Stamp
Graph Neural Network Consistent Comparison Malware Classification Android Malware Graph Neural Network Model

March 15, 2023

Android Malware Detection using Machine learning: A Review
Md Naseef-Ur-Rahman Chowdhury, Ahshanul Haque, Hamdy Soliman, Mohammad Sahinur Hossen, Tanjim Fatima, Imtiaz Ahmed
Machine Learning Narrative Review Android Malware

February 8, 2023

Continuous Learning for Android Malware Detection
Yizheng Chen, Zhoujie Ding, David Wagner
Active Learning Concept Drift Continuous Learning Android Malware

Android Malware

Papers

MASKDROID: Robust Android Malware Detection with Masked Graph Representations

Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach

Revisiting Static Feature-Based Android Malware Detection

Android Malware Detection Based on RGB Images and Multi-feature Fusion

DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware

Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations

Detecting Android Malware: From Neural Embeddings to Hands-On Validation with BERTroid

Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation

Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks

Unraveling the Key of Machine Learning Solutions for Android Malware Detection

ActDroid: An active learning framework for Android malware detection

Android Malware Detection with Unbiased Confidence Guarantees

MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks

Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection

Efficient Concept Drift Handling for Batch Android Malware Detection Models

Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting

On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities

A Comparison of Graph Neural Networks for Malware Classification

Android Malware Detection using Machine learning: A Review

Continuous Learning for Android Malware Detection