16 papers with code • 0 benchmarks • 2 datasets
These leaderboards are used to track progress in Malware Analysis
Most implemented papers
SAFE: Self-Attentive Function Embeddings for Binary Similarity
We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions.
Dynamic Malware Analysis with Feature Engineering and Feature Learning
In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection.
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machine Learning methods to discover meaningful distinctions between malicious and benign software.
Malware triage for early identification of Advanced Persistent Threat activities
In order to early identify APT related malware, a semi-automatic approach for malware samples analysis is needed.
A Cross-Architecture Instruction Embedding Model for Natural Language Processing-Inspired Binary Code Analysis
As a showcase, we apply the model to resolving one of the most fundamental problems for binary code similarity comparison---semantics-based basic block comparison, and the solution outperforms the code statistics based approach.
AndrODet: An Adaptive Android Obfuscation Detector
This is typically applied to protect intellectual property in benign apps, or to hinder the process of extracting actionable information in the case malware.
KiloGrams: Very Large N-Grams for Malware Classification
N-grams have been a common tool for information retrieval and machine learning applications for decades.
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique
The proposed DLMD technique uses both the byte and ASM files for feature engineering, thus classifying malware families.
Integration of Static and Dynamic Analysis for Malware Family Classification with Composite Neural Network
In this paper, we combine static and dynamic analysis features with deep neural networks for Windows malware classification.
Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis
This is because the models are complex, and most of them work as a black-box.