Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation
Fuente:
arXiv
Saved in:
| Main Authors: | Kumar, Harshit, Sharma, Sudarshan, Chakraborty, Biswadeep, Mukhopadhyay, Saibal |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
XMD: An Expansive Hardware-telemetry based Mobile Malware Detector to enhance Endpoint Detection
by: Kumar, Harshit, et al.
Published: (2022)
by: Kumar, Harshit, et al.
Published: (2022)
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks
by: Chakraborty, Biswadeep, et al.
Published: (2024)
by: Chakraborty, Biswadeep, et al.
Published: (2024)
Hydra: Robust Hardware-Assisted Malware Detection
by: Propp, Eli, et al.
Published: (2026)
by: Propp, Eli, et al.
Published: (2026)
Malware Detection based on API calls
by: Fellicious, Christofer, et al.
Published: (2025)
by: Fellicious, Christofer, et al.
Published: (2025)
Unraveling the Key of Machine Learning-based Android Malware Detection
by: Liu, Jiahao, et al.
Published: (2024)
by: Liu, Jiahao, et al.
Published: (2024)
Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset
by: Fu, Annan, et al.
Published: (2026)
by: Fu, Annan, et al.
Published: (2026)
FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning
by: Chakraborty, Biswadeep, et al.
Published: (2025)
by: Chakraborty, Biswadeep, et al.
Published: (2025)
Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction
by: Kang, Beomseok, et al.
Published: (2024)
by: Kang, Beomseok, et al.
Published: (2024)
Label-efficient Training Updates for Malware Detection over Time
by: Minnei, Luca, et al.
Published: (2026)
by: Minnei, Luca, et al.
Published: (2026)
Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks
by: Giri, Nandakrishna, et al.
Published: (2026)
by: Giri, Nandakrishna, et al.
Published: (2026)
Has the Deep Neural Network learned the Stochastic Process? An Evaluation Viewpoint
by: Kumar, Harshit, et al.
Published: (2024)
by: Kumar, Harshit, et al.
Published: (2024)
Recent Advances in Malware Detection: Graph Learning and Explainability
by: Shokouhinejad, Hossein, et al.
Published: (2025)
by: Shokouhinejad, Hossein, et al.
Published: (2025)
A Comparison of Adversarial Learning Techniques for Malware Detection
by: Louthánová, Pavla, et al.
Published: (2023)
by: Louthánová, Pavla, et al.
Published: (2023)
Regression-aware Continual Learning for Android Malware Detection
by: Ghiani, Daniele, et al.
Published: (2025)
by: Ghiani, Daniele, et al.
Published: (2025)
A Survey of Malware Detection Using Deep Learning
by: Bensaoud, Ahmed, et al.
Published: (2024)
by: Bensaoud, Ahmed, et al.
Published: (2024)
On the Robustness of Malware Detectors to Adversarial Samples
by: Salman, Muhammad, et al.
Published: (2024)
by: Salman, Muhammad, et al.
Published: (2024)
Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks
by: Nasr, Milad, et al.
Published: (2025)
by: Nasr, Milad, et al.
Published: (2025)
R+R: Revisiting Static Feature-Based Android Malware Detection using Machine Learning
by: Alam, Md Tanvirul, et al.
Published: (2024)
by: Alam, Md Tanvirul, et al.
Published: (2024)
Efficient Malware Detection with Optimized Learning on High-Dimensional Features
by: Choudhary, Aditya, et al.
Published: (2025)
by: Choudhary, Aditya, et al.
Published: (2025)
McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware
by: Kamol, Md Mahmuduzzaman, et al.
Published: (2026)
by: Kamol, Md Mahmuduzzaman, et al.
Published: (2026)
MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation
by: Roh, Eunjin, et al.
Published: (2025)
by: Roh, Eunjin, et al.
Published: (2025)
LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection
by: Gill, Naveen, et al.
Published: (2026)
by: Gill, Naveen, et al.
Published: (2026)
DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
by: McFadden, Shae, et al.
Published: (2025)
by: McFadden, Shae, et al.
Published: (2025)
Explainable Malware Detection through Integrated Graph Reduction and Learning Techniques
by: Mohammadian, Hesamodin, et al.
Published: (2024)
by: Mohammadian, Hesamodin, et al.
Published: (2024)
Learning Joint Embeddings of Function and Process Call Graphs for Malware Detection
by: Aneja, Kartikeya, et al.
Published: (2025)
by: Aneja, Kartikeya, et al.
Published: (2025)
Machine Learning Transferability for Malware Detection
by: Vieira, César, et al.
Published: (2026)
by: Vieira, César, et al.
Published: (2026)
Adversarial Patterns: Building Robust Android Malware Classifiers
by: Bhusal, Dipkamal, et al.
Published: (2022)
by: Bhusal, Dipkamal, et al.
Published: (2022)
Machine Learning Driven Smishing Detection Framework for Mobile Security
by: Goel, Diksha, et al.
Published: (2024)
by: Goel, Diksha, et al.
Published: (2024)
Comparison of Multiple Classifiers for Android Malware Detection with Emphasis on Feature Insights Using CICMalDroid 2020 Dataset
by: Abedin, Md Min-Ha-Zul, et al.
Published: (2026)
by: Abedin, Md Min-Ha-Zul, et al.
Published: (2026)
Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques
by: Fahim, Abrar, et al.
Published: (2025)
by: Fahim, Abrar, et al.
Published: (2025)
Cluster Analysis and Concept Drift Detection in Malware
by: Mishra, Aniket, et al.
Published: (2025)
by: Mishra, Aniket, et al.
Published: (2025)
LLM-Generated Samples for Android Malware Detection
by: Rollinson, Nik, et al.
Published: (2025)
by: Rollinson, Nik, et al.
Published: (2025)
DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
by: Pulido-Cortázar, Daniel, et al.
Published: (2025)
by: Pulido-Cortázar, Daniel, et al.
Published: (2025)
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection
by: Bostani, Hamid, et al.
Published: (2022)
by: Bostani, Hamid, et al.
Published: (2022)
Optimization of Lightweight Malware Detection Models For AIoT Devices
by: Lo, Felicia, et al.
Published: (2024)
by: Lo, Felicia, et al.
Published: (2024)
Dual Explanations via Subgraph Matching for Malware Detection
by: Shokouhinejad, Hossein, et al.
Published: (2025)
by: Shokouhinejad, Hossein, et al.
Published: (2025)
Trident: Improving Malware Detection with LLMs and Behavioral Features
by: Saul, Rebecca, et al.
Published: (2026)
by: Saul, Rebecca, et al.
Published: (2026)
Leveraging VAE-Derived Latent Spaces for Enhanced Malware Detection with Machine Learning Classifiers
by: Ajayi, Bamidele, et al.
Published: (2025)
by: Ajayi, Bamidele, et al.
Published: (2025)
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
by: Daoudi, Nadia, et al.
Published: (2021)
by: Daoudi, Nadia, et al.
Published: (2021)
Adversarially Robust and Interpretable Magecart Malware Detection
by: Pereira, Pedro, et al.
Published: (2025)
by: Pereira, Pedro, et al.
Published: (2025)
Similar Items
-
XMD: An Expansive Hardware-telemetry based Mobile Malware Detector to enhance Endpoint Detection
by: Kumar, Harshit, et al.
Published: (2022) -
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks
by: Chakraborty, Biswadeep, et al.
Published: (2024) -
Hydra: Robust Hardware-Assisted Malware Detection
by: Propp, Eli, et al.
Published: (2026) -
Malware Detection based on API calls
by: Fellicious, Christofer, et al.
Published: (2025) -
Unraveling the Key of Machine Learning-based Android Malware Detection
by: Liu, Jiahao, et al.
Published: (2024)