Malware Classification

41 papers with code • 2 benchmarks • 5 datasets

Malware Classification is the process of assigning a malware sample to a specific malware family. Malware within a family shares similar properties that can be used to create signatures for detection and classification. Signatures can be categorized as static or dynamic based on how they are extracted. A static signature can be based on a byte-code sequence, binary assembly instruction, or an imported Dynamic Link Library (DLL). Dynamic signatures can be based on file system activities, terminal commands, network communications, or function and system call sequences.

Source: Behavioral Malware Classification using Convolutional Recurrent Neural Networks

High-resolution Image-based Malware Classification using Multiple Instance Learning

timppeters/mil-malware-images 21 Nov 2023

This paper proposes a novel method of classifying malware into families using high-resolution greyscale images and multiple instance learning to overcome adversarial binary enlargement.

6
21 Nov 2023

Nebula: Self-Attention for Dynamic Malware Analysis

dtrizna/nebula 19 Sep 2023

Dynamic analysis enables detecting Windows malware by executing programs in a controlled environment, and storing their actions in log reports.

18
19 Sep 2023

Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance

eurecom-s3/decodingmlsecretsofwindowsmalwareclassification 27 Jul 2023

As a consequence, our community still lacks an understanding of malware classification results: whether they are tied to the nature and distribution of the collected dataset, to what extent the number of families and samples in the training dataset influence performance, and how well static and dynamic features complement each other.

11
27 Jul 2023

Recasting Self-Attention with Holographic Reduced Representations

neuromorphiccomputationresearchprogram/hrrformer 31 May 2023

In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains.

36
31 May 2023

Sequential Embedding-based Attentive (SEA) classifier for malware classification

Muhammad4hmed/SEA 11 Feb 2023

The tremendous growth in smart devices has uplifted several security threats.

1
11 Feb 2023

A Dynamic Weighted Federated Learning for Android Malware Classification

officialarijit/dw-fedavg 23 Nov 2022

In traditional FL, Federated Averaging (FedAvg) is utilized to construct the global model at each round by merging all of the local models obtained from all of the customers that participated in the FL.

16
23 Nov 2022

Self-Supervised Vision Transformers for Malware Detection

sachith500/sherlock 15 Aug 2022

Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks.

17
15 Aug 2022

On the Limitations of Continual Learning for Malware Classification

msrocean/continual-learning-malware 13 Aug 2022

To our surprise, continual learning methods significantly underperformed naive Joint replay of the training data in nearly all settings -- in some cases reducing accuracy by more than 70 percentage points.

12
13 Aug 2022

On deceiving malware classification with section injection

adeilsonsilva/malware-injection 12 Aug 2022

Our results show that a mere increase of 7% in the malware size causes an accuracy drop between 25% and 40% for malware family classification.

36
12 Aug 2022

An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification

Ferhat94/Random-Transformer-Forest 25 Dec 2021

Furthermore, the proposed bagging-based random transformer forest (RTF), an ensemble of BERT or CANINE, has reached the state-of-the-art evaluation scores on three out of four datasets, particularly state-of-the-art F1-score of 0. 6149 on one of the commonly used benchmark dataset.

8
25 Dec 2021