用稀疏卷积+快速梯度法，99.44%准确率高效识别安卓恶意软件
Malware Classification using Diluted Convolutional Neural Network with Fast Gradient Sign Method
- 采用稀疏卷积扩大感受野，用更少特征捕捉长程恶意模式
- 训练中引入一步扰动，提升准确率至99.44%且计算开销低
- 适合需要高精度、低特征依赖的移动端恶意软件检测场景
安卓恶意软件已成为对组织、社会和个人隐私、数据安全及基础设施的重大威胁。随着恶意软件复杂性和隐蔽性不断提升，其检测变得愈发耗时且困难，尤其因需大量特征进行识别。为此，本文提出基于快速梯度符号法的稀疏卷积神经网络（FGSM DICNN）用于恶意软件分类。DICNN采用稀疏卷积，扩大感受野，可在不增加参数量的前提下，用更少特征捕获长距离恶意行为模式。同时，通过训练中引入一步扰动的FGSM策略，以较低计算成本增强模型鲁棒性，提升分类精度。实验表明，该方法在保持高准确率的同时，显著降低对海量特征的依赖。所提FGSM DICNN模型达到99.44%的准确率，优于现有自定义深度神经网络（DCNN）等方法。
原文摘要 · Abstract (English)
Android malware has become an increasingly critical threat to organizations, society and individuals, posing significant risks to privacy, data security and infrastructure. As malware continues to evolve in terms of complexity and sophistication, the mitigation and detection of these malicious software instances have become more time consuming and challenging particularly due to the requirement of large number of features to identify potential malware. To address these challenges, this research proposes Fast Gradient Sign Method with Diluted Convolutional Neural Network (FGSM DICNN) method for malware classification. DICNN contains diluted convolutions which increases receptive field, enabling the model to capture dispersed malware patterns across long ranges using fewer features without adding parameters. Additionally, the FGSM strategy enhance the accuracy by using one-step perturbations during training that provides more defensive advantage of lower computational cost. This integration helps to manage high classification accuracy while reducing the dependence on extensive feature sets. The proposed FGSM DICNN model attains 99.44% accuracy while outperforming other existing approaches such as Custom Deep Neural Network (DCNN).
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