IoT-Based Android Malware Detection Using Graph Neural Network With Adversarial Defense

Yumlembam, Rahul; Issac, Biju; Jacob, Seibu Mary; Yang, Longzhi

doi:10.1109/jiot.2022.3188583

Cited by 38 publications

(9 citation statements)

References 26 publications

(24 reference statements)

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“…In this section, we mainly introduce the related research progress in adversarial samples from the authors, method names, essential techniques, and their respective advantages and disadvantages, as shown in Table 1. [14] It achieved an accuracy of 98.33% on the CICMaldroid dataset.…”

Section: Related Workmentioning

confidence: 94%

A Method for Generating Malware Countermeasure Samples Based on Pixel Attention Mechanism

2024

KSII TIIS

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With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

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Section: Related Workmentioning

confidence: 94%

A Method for Generating Malware Countermeasure Samples Based on Pixel Attention Mechanism

2024

KSII TIIS

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“…A more general approach is proposed by Yumlembam et al [46], who consider each Android app as a local graph, where nodes are APIs and an edge exists between two APIs if they co-exist in a same code block (i.e. a code segment from a smali file, located between .method and .endmethod).…”

Section: Fcg Approachesmentioning

confidence: 99%

“…An adversarial attack for GNN-based APK malware detection has been introduced in the work [46] to measure the robustness of the proposed detection model. The attack is based on a VGAE, that aims to effectively add nodes and edges to a FCG in order to fool the GNN classifier, in a black-box setting.…”

Section: Adversarial Attacks On Graph-based Malware Detectionmentioning

confidence: 99%

A Survey on Malware Detection with Graph Representation Learning

Bilot¹,

Madhoun²,

Agha³

et al. 2023

Preprint

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Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. More recently, the application of such techniques on graph-structured data has achieved state-of-the-art performance in various domains and demonstrates promising results in learning more robust representations from malware. Yet, no literature review focusing on graph-based deep learning for malware detection exists. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers. This paper also reviews adversarial attacks that are utilized to fool graph-based detection methods. Challenges and future research directions are discussed at the end of the paper. CCS Concepts: • General and reference → Surveys and overviews; • Security and privacy → Malware and its mitigation; • Computing methodologies → Learning latent representations; Neural networks; Spectral methods.

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“…Existing GNN models are judged insufficient for large-scale graphs that incorporate intricate topologies because they have only been evaluated on small graphs [70]. Examples of GNN implementation in cloud infrastructure have shown that graph analysis is scalable and effective in a variety of applications, such as recommender systems, traffic flow prediction, industrial IoT, privacy preservation, and matrix completion [71][72][73][74][75][76].…”

Section: Awanmentioning

confidence: 99%

Deep Learning Neural Networks in the Cloud

Awan

2023

IJAEMS

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Deep Neural Networks (DNNs) are currently used in a wide range of critical real-world applications as machine learning technology. Due to the high number of parameters that make up DNNs, learning and prediction tasks require millions of floating-point operations (FLOPs). Implementing DNNs into a cloud computing system with centralized servers and data storage sub-systems equipped with high-speed and high-performance computing capabilities is a more effective strategy. This research presents an updated analysis of the most recent DNNs used in cloud computing. It highlights the necessity of cloud computing while presenting and debating numerous DNN complexity issues related to various architectures. Additionally, it goes into their intricacies and offers a thorough analysis of several cloud computing platforms for DNN deployment. Additionally, it examines the DNN applications that are already running on cloud computing platforms to highlight the advantages of using cloud computing for DNNs. The study highlights the difficulties associated with implementing DNNs in cloud computing systems and provides suggestions for improving both current and future deployments.

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IoT-Based Android Malware Detection Using Graph Neural Network With Adversarial Defense

Cited by 38 publications

References 26 publications

A Method for Generating Malware Countermeasure Samples Based on Pixel Attention Mechanism

A Method for Generating Malware Countermeasure Samples Based on Pixel Attention Mechanism

A Survey on Malware Detection with Graph Representation Learning

Deep Learning Neural Networks in the Cloud

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