Adversarial-Example Attacks Toward Android Malware Detection System

Li, Heng; Zhou, Shi-Yao; Yuan, Wei; Li, Jiahuan; Leung, Henry

doi:10.1109/jsyst.2019.2906120

Cited by 71 publications

(39 citation statements)

References 7 publications

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“…In [138], a system based on generative adversarial networks to increase botnet detection models (Bot-GAN) was presented, which improves detection efficiency and decreases the false positive rate. A new GAN-based adversarial-example attack method was implemented in [69], which outperforms the state-of-the-art method by 247.68%. In [85], the authors explore Generative Adversarial Networks (GANs) to improve the training and ultimately performance of cyber attack detection systems by balancing data sets with the generated data.…”

Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Sarker¹

2021

Preprint

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Deep learning (DL), which is originated from an artificial neural network (ANN), is one of the major technologies of today's smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN or ConvNet), Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM), Self-organizing Map (SOM), Auto-Encoder (AE), Restricted Boltzmann Machine (RBM), Deep Belief Networks (DBN), Generative Adversarial Network (GAN), Deep Transfer Learning (DTL or Deep TL), Deep Reinforcement Learning (DRL or Deep RL), or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today's diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyber-attacks, e.g. denial of service (DoS), fraud detection or cyber-anomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

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Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Sarker¹

2021

Preprint

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“…To a certain extent, it can protect the Android malware detection system based on deep learning by deploying an adversary sample detector to filter adversary samples. Heng Li et al [87]. proposed a new adversary sample attack method based on bi-objective GAN.…”

Section: ) Adversary Attackmentioning

confidence: 99%

“…More than 95% of the adversary samples generated by this attack method successfully misled the Android malware detection system equipped with an adversary sample detector. Heng Li et al [87] used bi-objective GAN to generate adversary samples with incomplete information. Bi-objective GAN extended the traditional GAN by using two discriminators with different targets.…”

Section: ) Adversary Attackmentioning

confidence: 99%

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Review of Android Malware Detection Based on Deep Learning

2020

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At present, smartphones running the Android operating system have occupied the leading market share. However, due to the Android operating system's open-source nature, Android malware has increased dramatically. Malware can steal user privacy and even maliciously charge fees and steal funds. It has posed a severe threat to cyberspace security because traditional detection methods have many limitations. With the widespread application of deep learning in recent years, the method of detecting Android malware using deep learning has gradually attracted widespread attention from scholars at home and abroad. Although scholars have researched Android malware detection using deep learning, there is currently a lack of a detailed and comprehensive introduction to malware detection's latest research results based on deep learning. In order to solve this problem, this study analyzes and summarizes the latest research results by investigating a large number of the latest domestic and international academic papers, summarizing malware detection architecture and detection schemes, and analyzing existing problems and challenges. This review will help researchers better understand the research status and future research directions in this field. INDEX TERMS Android, malware, deep learning, review.

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“…e coding part could improve the accuracy and the efficiency as the whole and improve the GAN network objective function optimization by adding a label classifier. In order to improve the convergence of the GAN network, in [47][48][49][50][51], an optimized objective function was proposed to improve the training process of the GAN network. Among them, authors in [47][48][49] used different models to achieve the loss objective function.…”

Section: Reasons For Proposed Algorithmmentioning

confidence: 99%

Detection for Multisatellite Downlink Signal Based on Generative Adversarial Neural Network

Qin

2020

Mathematical Problems in Engineering

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A method for satellite downlink signal detection based on a generative adversarial network is proposed. The generator adversarial network and adversarial network are established, respectively. The generator network realizes the local generator of satellite signals, and the adversarial network is used for high-precision signal detection. The error network is generated by the error signal to form the satellite link downlink. The network reconstructs the optimal weights by generating errors, forms an error matrix for different satellite downlink, and then forms an adaptive matrix weight adjustment. Through the reconstruction of the optimal detection matrix, detection for the downlink signals of multiple satellites is completed. The proposed generative adversarial network can realize the high-precision detection for the downlink signal.

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Adversarial-Example Attacks Toward Android Malware Detection System

Cited by 71 publications

References 7 publications

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Review of Android Malware Detection Based on Deep Learning

Detection for Multisatellite Downlink Signal Based on Generative Adversarial Neural Network

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