论文标题
MalwareTraffic检测的数据集优化策略
Dataset Optimization Strategies for MalwareTraffic Detection
论文作者
论文摘要
机器学习正迅速成为恶意软件交通检测最重要的技术之一，因为恶意软件的持续发展需要持续的适应性和推广能力。但是，网络流量数据集通常超大，并且包含多余且无关紧要的信息，这可能会大大增加计算成本并降低大多数分类器的准确性，并有可能引入进一步的噪声。 我们提出了两种新颖的数据集优化策略，它们利用并结合了几种最先进的方法，以实现用于训练恶意软件探测器的网络流量数据集的有效优化。第一种方法是一种基于相互信息测量和感觉增强的特征选择技术。第二个是基于尺寸降低技术的自动编码器。这两种方法均已实验应用于MTA-KDD'19数据集，并使用多层PercePtron作为恶意软件检测的机器学习模型进行了评估和比较。
Machine learning is rapidly becoming one of the most important technology for malware traffic detection, since the continuous evolution of malware requires a constant adaptation and the ability to generalize. However, network traffic datasets are usually oversized and contain redundant and irrelevant information, and this may dramatically increase the computational cost and decrease the accuracy of most classifiers, with the risk to introduce further noise. We propose two novel dataset optimization strategies which exploit and combine several state-of-the-art approaches in order to achieve an effective optimization of the network traffic datasets used to train malware detectors. The first approach is a feature selection technique based on mutual information measures and sensibility enhancement. The second is a dimensional reduction technique based autoencoders. Both these approaches have been experimentally applied on the MTA-KDD'19 dataset, and the optimized results evaluated and compared using a Multi Layer Perceptron as machine learning model for malware detection.
