Malware Detection
90 papers with code • 2 benchmarks • 4 datasets
Malware Detection is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as malicious malware redirects on WordPress site (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware
Source: The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey
EMBER
MalNet
IoT-23
Latest papers with no code
Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation
This study introduces RT-HMD, a Hardware-based Malware Detector (HMD) for mobile devices, that refines malware representation in segmented time-series through a Multiple Instance Learning (MIL) approach.
Counteracting Concept Drift by Learning with Future Malware Predictions
We use GANs to learn changes in data distributions within different time periods of training data and then apply these changes to generate samples that could be in testing data.
Optimization of Lightweight Malware Detection Models For AIoT Devices
Malware intrusion is problematic for Internet of Things (IoT) and Artificial Intelligence of Things (AIoT) devices as they often reside in an ecosystem of connected devices, such as a smart home.
Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis
We evaluate the effectiveness of machine learning algorithms, such as decision trees, ensemble methods, and neural networks, in detecting obfuscated malware within memory dumps.
Generative AI-Based Effective Malware Detection for Embedded Computing Systems
Furthermore, such constraints limit the detection of emerging malware samples due to the lack of sufficient malware samples required for efficient training.
A Transformer-Based Framework for Payload Malware Detection and Classification
Techniques such as Deep Packet Inspection (DPI) have been introduced to allow IDSs analyze the content of network packets, providing more context for identifying potential threats.
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection
Malware detection is an interesting and valuable domain to work in because it has significant real-world impact and unique machine-learning challenges.
Shifting the Lens: Detecting Malware in npm Ecosystem with Large Language Models
Our baseline comparison demonstrates a notable improvement over static analysis in precision scores above 25% and F1 scores above 15%.
Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection
This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset.
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks
This research explores the effectiveness of utilizing GAN-generated data to train a model for the detection of Android malware.
