PDF Malware Detection Using Visualization and Machine Learning

Liu, Ching Yuan; Chiu, Min-Yi; Huang, Qi-Xian; Sun, Hung–Min

doi:10.1007/978-3-030-81242-3_12

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Optimum results were achieved using a byte plot with a Gabor filter and RF, which resulted in a 99.48% F1 score. In [36], the author used a PDF malware detection technique based on malware visualization and image classification. VGG19 with CNN architecture was used to train the model, which resulted in an accuracy of 97.3% and an F1-score of 97.5%.…”

Section: Grayscale-based Transform Featuresmentioning

confidence: 99%

A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System

Copiaco,

El Neel,

Nazzal

et al. 2023

Applied Sciences

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This study introduces an innovative all-in-one malware identification model that significantly enhances convenience and resource efficiency in classifying malware across diverse file types. Traditional malware identification methods involve the extraction of static and dynamic features, followed by comparisons with signature-based databases or machine learning-based classifiers. However, many malware detection applications that rely on transfer learning and image transformation suffer from excessive resource consumption. In recent years, transfer learning has emerged as a powerful tool for developing effective classifiers, leveraging pre-trained neural network models. In this research, we comprehensively explore various pre-trained network architectures, including compact and conventional networks, as well as series and directed acyclic graph configurations for malware classification. Our approach utilizes grayscale transform-based features as a standardized set of characteristics, streamlining malware classification across various file types. To ensure the robustness and generalization of our classification models, we integrate multiple datasets into the training process. Remarkably, we achieve an optimal model with 96% accuracy, while maintaining a modest 5 MB size using the SqueezeNet classifier. Overall, our model efficiently classifies malware across file types, reducing the computational load, which can be useful for cybersecurity professionals and organizations.

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Section: Grayscale-based Transform Featuresmentioning

confidence: 99%

A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System

Copiaco,

El Neel,

Nazzal

et al. 2023

Applied Sciences

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“…In which, model parameter includes 𝑊 ∈ ℝ 𝑑×𝑘 , 𝑉 ∈ ℝ 𝑑×𝑑 , and 𝑏 ∈ ℝ 𝑑 are learned at the time of training and share at every time step, 𝜎 denotes sigmoid activation, ⊙ indicates elementwise product, and 𝑘 represent a hyperparameter that symbolizes the dimension of hidden states [18]. At first, LSTM is employed to handle time-series data.…”

Section: S-lstm Based Classificationmentioning

confidence: 99%

Invasive weed optimization with stacked long short term memory for PDF malware detection and classification

Chandran

Hema²,

Jeyakarthic

2022

ijhs

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Due to high versatility and widespread adoption, PDF documents are widely exploited for launching attacks by cyber criminals. PDFs have been conventionally utilized as an effective method for spreading malware. Automated detection and classification of PDF malware are essential to accomplish security. Latest developments of artificial intelligence (AI) and deep learning (DL) models pave a way for automated detection of PDF malware. In this view, this article develops an Invasive Weed Optimization with Stacked Long Short Term Memory (IWO-S-LSTM) technique for PDF malware detection and classification. The presented IWO-S-LSTM model focuses on the recognition and classification of different kinds of malware that exist in PDF documents. The proposed IWO-S-LSTM model initially undergoes pre-processing in two stages namely categorical encoding and null value removal. Besides, autoencoder (AE) based outlier detection approach is presented to remove the existence of outliers. In addition, S-LSTM model is utilized to detect and classify PDF malware. Finally, IWO algorithm is applied to fine tune the hyperparameters involved in the S-LSTM model. To determine the enhanced outcomes of the IWO-S-LSTM model, a series of simulations were executed on two benchmark datasets. The experimental outcomes outperformed the promising performance of the IWO-S-LSTM technique on the other approaches.

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“…Acknowledgements. The first author is grateful to Cosmin Pohoata for bringing reference [23] to his attention and for useful discussions. We are also grateful to Noga Alon, Zach Hunter and the two referees for helpful comments.…”

mentioning

confidence: 99%

On the Turán number of the hypercube

Janzer,

Sudakov

2024

Forum of Mathematics, Sigma

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In 1964, Erdős proposed the problem of estimating the Turán number of the d-dimensional hypercube $Q_d$ . Since $Q_d$ is a bipartite graph with maximum degree d, it follows from results of Füredi and Alon, Krivelevich, Sudakov that $\mathrm {ex}(n,Q_d)=O_d(n^{2-1/d})$ . A recent general result of Sudakov and Tomon implies the slightly stronger bound $\mathrm {ex}(n,Q_d)=o(n^{2-1/d})$ . We obtain the first power-improvement for this old problem by showing that $\mathrm {ex}(n,Q_d)=O_d\left (n^{2-\frac {1}{d-1}+\frac {1}{(d-1)2^{d-1}}}\right )$ . This answers a question of Liu. Moreover, our techniques give a power improvement for a larger class of graphs than cubes. We use a similar method to prove that any n-vertex, properly edge-coloured graph without a rainbow cycle has at most $O(n(\log n)^2)$ edges, improving the previous best bound of $n(\log n)^{2+o(1)}$ by Tomon. Furthermore, we show that any properly edge-coloured n-vertex graph with $\omega (n\log n)$ edges contains a cycle which is almost rainbow: that is, almost all edges in it have a unique colour. This latter result is tight.

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PDF Malware Detection Using Visualization and Machine Learning

Cited by 5 publications

References 11 publications

A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System

A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System

Invasive weed optimization with stacked long short term memory for PDF malware detection and classification

On the Turán number of the hypercube

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