.png){kind=icon}
返回
Adversarial ELF Malware Detection Method Using Model Interpretation
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2022.3192901.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent research shows that executable and linkable format (ELF) malware detection models based on deep learning are vulnerable to adversarial attacks. The most commonly used method in previous work is adversarial training to defend adversarial examples. Nevertheless, it is inefficient and only effective for specific adversarial attacks. Given that the perturbation byte insertion positions of existing adversarial malware generation methods are relatively fixed, we propose a new method to detect adversarial ELF malware. Using model interpretation techniques, we analyze the decision-making basis of the malware detection model and extract the features of adversarial examples. We further use anomaly detection techniques to identify adversarial examples. As an add-on module of the malware detection model, the proposed method does not require modifying the original model and does not need to retrain the model. Evaluating results show that the method can effectively defend the adversarial attacks against the malware detection model.
Keyword:
Malware
Analytical models
Deep learning
Ground penetrating radar
Geophysical measurement techniques
Training
Feature extraction
Artificial neural networks
computer security
invasive software
smart devices
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
机构
引用论文
Understanding adversarial training: Increasing local stability of supervised models through robust optimization.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏Static malware detection and attribution in android byte-code through an end-to-end deep system
PREAI
分享收藏Recurrent Neural Network Model for IoT and Networking Malware Threat Detection用于物联网和网络恶意软件威胁检测的递归神经网络模型
PREAI
分享收藏Welding characteristics of aluminum, copper, nickel and aluminum alloy with alumina coating using ultrasonic complex vibration welding equipments铝、铜、镍及铝合金氧化铝涂层超声复合振动焊接特性研究
PREAI
分享收藏分享收藏A Flying Squirrel Search Optimization for MPPT Under Partial Shaded Photovoltaic System局部遮挡光伏系统MPPT的飞鼠搜索优化
PREAI
分享收藏
查看更多内容