Black-Box Evasion Attack Method Based on Confidence Score of Benign Samples

Black-Box Evasion Attack Method Based on Confidence Score of Benign Samples

Recently, malware detection models based on deep learning have gradually replaced manual analysis as the first line of defense for anti-malware systems. However, it has been shown that these models are vulnerable to a specific class of inputs called adversarial examples. It is possible to evade the...

Full description

Bibliographic Details
Main Authors:	Shaohan Wu, Jingfeng Xue, Yong Wang, Zixiao Kong
Format:	Article
Language:	English
Published:	MDPI AG 2023-05-01
Series:	Electronics
Subjects:	adversarial examples evasion attack malware detection artificial intelligence security
Online Access:	https://www.mdpi.com/2079-9292/12/11/2346

Similar Items

DroidEnemy: Battling adversarial example attacks for Android malware detection
by: Neha Bala, et al.
Published: (2022-12-01)

Restricted Evasion Attack: Generation of Restricted-Area Adversarial Example
by: Hyun Kwon, et al.
Published: (2019-01-01)

Instance attack: an explanation-based vulnerability analysis framework against DNNs for malware detection
by: Ruijin Sun, et al.
Published: (2023-11-01)

A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm Optimization
by: Naufal Suryanto, et al.
Published: (2020-12-01)

DDSG-GAN: Generative Adversarial Network with Dual Discriminators and Single Generator for Black-Box Attacks
by: Fangwei Wang, et al.
Published: (2023-02-01)

AdvGuard: Fortifying Deep Neural Networks Against Optimized Adversarial Example Attack
by: Hyun Kwon, et al.
Published: (2024-01-01)

Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks
by: Kaleel Mahmood, et al.
Published: (2022-01-01)

SSQLi: A Black-Box Adversarial Attack Method for SQL Injection Based on Reinforcement Learning
by: Yuting Guan, et al.
Published: (2023-03-01)

A Deep Reinforcement Learning Framework to Evade Black-Box Machine Learning Based IoT Malware Detectors Using GAN-Generated Influential Features
by: Rahat Maqsood Arif, et al.
Published: (2023-01-01)

Leveraging Adversarial Samples for Enhanced Classification of Malicious and Evasive PDF Files
by: Fouad Trad, et al.
Published: (2023-03-01)

A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity
by: Sicong Zhang, et al.
Published: (2020-01-01)

Maxwell’s Demon in MLP-Mixer: towards transferable adversarial attacks
by: Haoran Lyu, et al.
Published: (2024-03-01)

Partial Retraining Substitute Model for Query-Limited Black-Box Attacks
by: Hosung Park, et al.
Published: (2020-10-01)

A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach
by: Mohammed Nasser Al-Andoli, et al.
Published: (2024-01-01)

Adversarial Examples for CNN-Based Malware Detectors
by: Bingcai Chen, et al.
Published: (2019-01-01)

A Feasibility Study on Evasion Attacks Against NLP-Based Macro Malware Detection Algorithms
by: Mamoru Mimura, et al.
Published: (2023-01-01)

A CMA-ES-Based Adversarial Attack on Black-Box Deep Neural Networks
by: Xiaohui Kuang, et al.
Published: (2019-01-01)

On the Feasibility of Adversarial Sample Creation Using the Android System API
by: Fabrizio Cara, et al.
Published: (2020-09-01)

An Evasion Attack against Stacked Capsule Autoencoder
by: Jiazhu Dai, et al.
Published: (2022-01-01)

AppCon: Mitigating Evasion Attacks to ML Cyber Detectors
by: Giovanni Apruzzese, et al.
Published: (2020-04-01)

Adversarial attacks and defenses in deep learning
by: LIU Ximeng, et al.
Published: (2020-10-01)

An Effective Adversarial Attack on Person Re-Identification in Video Surveillance via Dispersion Reduction
by: Yu Zheng, et al.
Published: (2020-01-01)

Black-box attacks and defense for DNN-based power quality classification in smart grid
by: Liangheng Zhang, et al.
Published: (2022-11-01)

Timing Black-Box Attacks: Crafting Adversarial Examples through Timing Leaks against DNNs on Embedded Devices
by: Tsunato Nakai, et al.
Published: (2021-07-01)

Besting the Black-Box: Barrier Zones for Adversarial Example Defense
by: Kaleel Mahmood, et al.
Published: (2022-01-01)

A Hybrid Adversarial Attack for Different Application Scenarios
by: Xiaohu Du, et al.
Published: (2020-05-01)

LSGAN-AT: enhancing malware detector robustness against adversarial examples
by: Jianhua Wang, et al.
Published: (2021-12-01)

Dual-Targeted Textfooler Attack on Text Classification Systems
by: Hyun Kwon
Published: (2023-01-01)

Adversarial Attack and Defense Strategies of Speaker Recognition Systems: A Survey
by: Hao Tan, et al.
Published: (2022-07-01)

Malicious PDF Detection Model against Adversarial Attack Built from Benign PDF Containing JavaScript
by: Ah Reum Kang, et al.
Published: (2019-11-01)

On the Effectiveness of Adversarial Training in Defending against Adversarial Example Attacks for Image Classification
by: Sanglee Park, et al.
Published: (2020-11-01)

Evasion Attacks and Defense Mechanisms for Machine Learning-Based Web Phishing Classifiers
by: Manu J. Pillai, et al.
Published: (2024-01-01)

ABCAttack: A Gradient-Free Optimization Black-Box Attack for Fooling Deep Image Classifiers
by: Han Cao, et al.
Published: (2022-03-01)

Black-Box Audio Adversarial Attack Using Particle Swarm Optimization
by: Hyunjun Mun, et al.
Published: (2022-01-01)

Cognitive Bias-Inspired Deep Robust Neural Networks Against Transfer-Based Attacks Considering Confidence Score
by: Yuuki Ogasawara, et al.
Published: (2024-02-01)

Orthogonal Deep Models as Defense Against Black-Box Attacks
by: Mohammad A. A. K. Jalwana, et al.
Published: (2020-01-01)

Imperceptible black-box waveform-level adversarial attack towards automatic speaker recognition
by: Xingyu Zhang, et al.
Published: (2022-06-01)

Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview
by: Xiaojiao Chen, et al.
Published: (2021-09-01)

Enhancing the Transferability of Targeted Attacks with Adversarial Perturbation Transform
by: Zhengjie Deng, et al.
Published: (2023-09-01)

Adversarial Attacks with Defense Mechanisms on Convolutional Neural Networks and Recurrent Neural Networks for Malware Classification
by: Sharoug Alzaidy, et al.
Published: (2024-02-01)