SOREL-20M: A large scale benchmark dataset for malicious PE detection
In this paper we describe the SOREL-20M (Sophos/ReversingLabs-20 Million) dataset: a
large-scale dataset consisting of nearly 20 million files with pre-extracted features and …
large-scale dataset consisting of nearly 20 million files with pre-extracted features and …
Automated machine learning for deep learning based malware detection
Deep learning (DL) has proven to be effective in detecting sophisticated malware that is
constantly evolving. Even though deep learning has alleviated the feature engineering …
constantly evolving. Even though deep learning has alleviated the feature engineering …
Malware detection by control-flow graph level representation learning with graph isomorphism network
With society's increasing reliance on computer systems and network technology, the threat
of malicious software grows more and more serious. In the field of information security …
of malicious software grows more and more serious. In the field of information security …
[HTML][HTML] A study of the relationship of malware detection mechanisms using Artificial Intelligence
J Song, S Choi, J Kim, K Park, C Park, J Kim, I Kim - ICT Express, 2024 - Elsevier
Implementation of malware detection using Artificial Intelligence (AI) has emerged as a
significant research theme to combat evolving various types of malwares. Researchers …
significant research theme to combat evolving various types of malwares. Researchers …
Quo Vadis: hybrid machine learning meta-model based on contextual and behavioral malware representations
D Trizna - Proceedings of the 15th ACM Workshop on Artificial …, 2022 - dl.acm.org
We propose a hybrid machine learning architecture that simultaneously employs multiple
deep learning models analyzing contextual and behavioral characteristics of Windows …
deep learning models analyzing contextual and behavioral characteristics of Windows …
{URET}: Universal Robustness Evaluation Toolkit (for Evasion)
Machine learning models are known to be vulnerable to adversarial evasion attacks as
illustrated by image classification models. Thoroughly understanding such attacks is critical …
illustrated by image classification models. Thoroughly understanding such attacks is critical …
Malware detection using LightGBM with a custom logistic loss function
The increased spread of malicious software (malware) through the internet remains a
serious threat. Malware authors use obfuscation and deformation techniques to generate …
serious threat. Malware authors use obfuscation and deformation techniques to generate …
Stealing and evading malware classifiers and antivirus at low false positive conditions
Abstract Model stealing attacks have been successfully used in many machine learning
domains, but there is little understanding of how these attacks work against models that …
domains, but there is little understanding of how these attacks work against models that …
Malware detection using attributed CFG generated by pre-trained language model with graph isomorphism network
Y Gao, H Hasegawa, Y Yamaguchi… - 2022 IEEE 46th …, 2022 - ieeexplore.ieee.org
Traditional malware detection methods cannot keep up with the massive amount of newly
created malware quickly and effectively. Machine learning is a promising method for the …
created malware quickly and effectively. Machine learning is a promising method for the …
TCG-IDS: Robust Network Intrusion Detection via Temporal Contrastive Graph Learning
C Wu, J Sun, J Chen, M Alazab, Y Liu… - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
In the era of zero trust security models and next-generation networks (NGN), the primary
challenge is that network nodes may be untrusted, even if they have been verified …
challenge is that network nodes may be untrusted, even if they have been verified …