论文标题
移动僵尸网络检测：一种使用卷积神经网络的深度学习方法
Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks
论文作者
论文摘要
Android，成为最广泛的移动操作系统正在越来越多地成为恶意软件的目标。旨在将移动设备变成机器人的恶意应用程序可能构成较大僵尸网络的一部分已经变得很普遍，从而构成了严重的威胁。这要求使用更有效的方法来检测Android平台上的僵尸网络。因此，在本文中，我们提出了一种基于卷积神经网络（CNN）的Android僵尸网络检测的深度学习方法。我们提出的僵尸网络检测系统被实现为基于CNN的模型，该模型在342个静态应用功能上进行了训练，以区分僵尸网络应用程序和普通应用程序。对经过训练的僵尸网络检测模型进行了评估，该模型在一组6,802个实际应用程序中，其中包含来自公开可用的ISCX僵尸网络数据集的1,929个僵尸网络。结果表明，与其他流行的机器学习分类器相比，我们基于CNN的方法的总体预测准确性最高。此外，从我们的模型中观察到的性能结果比以前有关基于机器学习的Android僵尸网络检测的研究中报道的要好。
Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger botnet have become quite common, thus posing a serious threat. This calls for more effective methods to detect botnets on the Android platform. Hence, in this paper, we present a deep learning approach for Android botnet detection based on Convolutional Neural Networks (CNN). Our proposed botnet detection system is implemented as a CNN-based model that is trained on 342 static app features to distinguish between botnet apps and normal apps. The trained botnet detection model was evaluated on a set of 6,802 real applications containing 1,929 botnets from the publicly available ISCX botnet dataset. The results show that our CNN-based approach had the highest overall prediction accuracy compared to other popular machine learning classifiers. Furthermore, the performance results observed from our model were better than those reported in previous studies on machine learning based Android botnet detection.
