Malware Detection
91 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
Most implemented papers
Improving Malware Detection Accuracy by Extracting Icon Information
While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware.
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing
A dataset for malware detection in Grid computing is described.
Robust Neural Malware Detection Models for Emulation Sequence Learning
These models target the core of the malicious operation by learning the presence and pattern of co-occurrence of malicious event actions from within these sequences.
Deep learning at the shallow end: Malware classification for non-domain experts
ceadarireland/deeplearningattheshallowend
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Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification.
Statistical Estimation of Malware Detection Metrics in the Absence of Ground Truth
The accurate measurement of security metrics is a critical research problem because an improper or inaccurate measurement process can ruin the usefulness of the metrics, no matter how well they are defined.
Detecting DGA domains with recurrent neural networks and side information
alistairwgillespie/deep_dga_detection
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Our experiments show the model is capable of effectively identifying domains generated by difficult DGA families.
Deep Transfer Learning for Static Malware Classification
In the transfer learning scheme, we borrow knowledge from natural images or objects and apply to the target domain of static malware detection.
Transfer Learning for Image-Based Malware Classification
In this paper, we consider the problem of malware detection and classification based on image analysis.
ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation
cmikke97/Automatic-Malware-Signature-Generation
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In this work, we fit deep neural networks to multiple additional targets derived from metadata in a threat intelligence feed for Portable Executable (PE) malware and benignware, including a multi-source malicious/benign loss, a count loss on multi-source detections, and a semantic malware attribute tag loss.
Learning from Context: Exploiting and Interpreting File Path Information for Better Malware Detection
In this paper, we propose utilizing a static source of contextual information -- the path of the PE file -- as an auxiliary input to the classifier.
