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Using API Call Sequences for IoT Malware Classification Based on Convolutional Neural Networks
International Journal of Software Engineering and Knowledge Engineering ( IF 0.6 ) Pub Date : 2021-05-18 , DOI: 10.1142/s021819402140009x
Qianguang Lin 1 , Ni Li 2, 3 , Qi Qi 1 , Jiabin Hu 4
Affiliation  

Internet of Things (IoT) devices built on different processor architectures have increasingly become targets of adversarial attacks. In this paper, we propose an algorithm for the malware classification problem of the IoT domain to deal with the increasingly severe IoT security threats. Application executions are represented by sequences of consecutive API calls. The time series of data is analyzed and filtered based on the improved information gains. It performs more effectively than chi-square statistics, in reducing the sequence lengths of input data meanwhile keeping the important information, according to the experimental results. We use a multi-layer convolutional neural network to classify various types of malwares, which is suitable for processing time series data. When the convolution window slides down the time sequence, it can obtain higher-level positions by collecting different sequence features, thereby understanding the characteristics of the corresponding sequence position. By comparing the iterative efficiency of different optimization algorithms in the model, we select an algorithm that can approximate the optimal solution to a small number of iterations to speed up the convergence of the model training. The experimental results from real world IoT malware sample show that the classification accuracy of this approach can reach more than 98%. Overall, our method has demonstrated practical suitability for IoT malware classification with high accuracies and low computational overheads by undergoing a comprehensive evaluation.

中文翻译:

使用 API 调用序列进行基于卷积神经网络的物联网恶意软件分类

建立在不同处理器架构上的物联网 (IoT) 设备越来越成为对抗性攻击的目标。在本文中,我们针对物联网领域的恶意软件分类问题提出了一种算法,以应对日益严重的物联网安全威胁。应用程序执行由一系列连续的 API 调用表示。基于改进的信息增益对数据的时间序列进行分析和过滤。根据实验结果,它比卡方统计更有效地减少输入数据的序列长度,同时保留重要信息。我们使用多层卷积神经网络对各种类型的恶意软件进行分类,适用于处理时间序列数据。当卷积窗口顺着时序下滑时,它可以通过收集不同的序列特征来获得更高层次的位置,从而了解对应序列位置的特征。通过比较模型中不同优化算法的迭代效率,我们选择一种能够将最优解逼近到迭代次数较少的算法,以加快模型训练的收敛速度。来自真实世界物联网恶意软件样本的实验结果表明,该方法的分类准确率可以达到 98% 以上。总体而言,通过全面评估,我们的方法证明了对物联网恶意软件分类的实际适用性,具有高精度和低计算开销。从而了解对应序列位置的特征。通过比较模型中不同优化算法的迭代效率,我们选择一种能够将最优解逼近到迭代次数较少的算法,以加快模型训练的收敛速度。来自真实世界物联网恶意软件样本的实验结果表明,该方法的分类准确率可以达到 98% 以上。总体而言,通过全面评估,我们的方法证明了对物联网恶意软件分类具有高精度和低计算开销的实际适用性。从而了解对应序列位置的特征。通过比较模型中不同优化算法的迭代效率,我们选择一种能够将最优解逼近到迭代次数较少的算法,以加快模型训练的收敛速度。来自真实世界物联网恶意软件样本的实验结果表明,该方法的分类准确率可以达到 98% 以上。总体而言,通过全面评估,我们的方法证明了对物联网恶意软件分类具有高精度和低计算开销的实际适用性。我们选择一种可以在少量迭代中逼近最优解的算法,以加快模型训练的收敛速度。来自真实世界物联网恶意软件样本的实验结果表明,该方法的分类准确率可以达到 98% 以上。总体而言,通过全面评估,我们的方法证明了对物联网恶意软件分类具有高精度和低计算开销的实际适用性。我们选择一种可以在少量迭代中逼近最优解的算法,以加快模型训练的收敛速度。来自真实世界物联网恶意软件样本的实验结果表明,该方法的分类准确率可以达到 98% 以上。总体而言,通过全面评估,我们的方法证明了对物联网恶意软件分类具有高精度和低计算开销的实际适用性。
更新日期:2021-05-18
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