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).

翻译：Android恶意软件已成为对组织、社会及个人日益严峻的威胁，对隐私、数据安全和基础设施构成重大风险。随着恶意软件在复杂性与精密性方面的持续演进，其缓解与检测过程变得愈发耗时且具有挑战性，这主要源于识别潜在恶意软件所需的大量特征需求。为应对这些挑战，本研究提出一种结合快速梯度符号方法的稀释卷积神经网络（FGSM DICNN）用于恶意软件分类。DICNN包含可扩大感受野的稀释卷积操作，使模型能够以更少的特征捕获长距离分布的恶意软件模式，且无需增加参数。此外，FGSM策略通过在训练阶段采用单步扰动来提升模型精度，这种以较低计算成本获得更强防御优势的方法，有助于在维持高分类准确率的同时降低对大规模特征集的依赖。所提出的FGSM DICNN模型取得了99.44%的分类准确率，其性能优于定制深度神经网络（DCNN）等现有方法。