Malware Classification Framework Using Convolutional Neural Network Pdf | on jan 1, 2017, wei wang and others published malware traffic classification using convolutional neural network for representation learning | find, read and cite all the. Traffic classification is the first step for network anomaly detection or network based intrusion detection system and plays an important role in network securi.
Pdf Traffic Sign Classification Using Convolutional Neural Network 流量分类是网络异常检测或基于网络的入侵检测系统的第一步，在网络安全领域发挥着重要作用。 在本文中，我们首先从人工智能的角度提出了一种新的流量分类分类，然后提出了一种以流量数据为图像的卷积神经网络恶意软件流量分类方法。 该方法不需要人工设计的功能，但直接将原始流量作为分类器的输入数据。 据我们所知，这一有趣的尝试是第一次将表示学习方法应用于使用原始流量数据的恶意软件流量分类。 我们确定最佳类型的流量表示是通过八个实验与所有层进行会话。 该方法在包括三种分类器的两种情况下得到验证，实验结果表明，该方法能够满足实际应用的精度要求。 关键词:流量分类; 卷积神经网络; 表征学习; 网络异常检测; 入侵侦测系统. To further investigate how the structure of network traffic affects the classification performance of a neural network, the prediction process of the trained classifiers was evaluated. In this paper we first presented a new taxonomy of traffic classification from an artificial intelligence perspective, and then proposed a malware traffic classification method using convolutional neural network by taking traffic data as images. Convolutional neural networks (cnns) achieved a 98.56% improvement in malware classification accuracy using x86 instructions. the study introduces two novel cnn approaches for classifying malware based on images and x86 instructions.
Pdf An Xception Convolutional Neural Network For Malware In this paper we first presented a new taxonomy of traffic classification from an artificial intelligence perspective, and then proposed a malware traffic classification method using convolutional neural network by taking traffic data as images. Convolutional neural networks (cnns) achieved a 98.56% improvement in malware classification accuracy using x86 instructions. the study introduces two novel cnn approaches for classifying malware based on images and x86 instructions. View a pdf of the paper titled malware classification using a hybrid hidden markov model convolutional neural network, by ritik mehta and olha jureckova and mark stamp. Aset created from binaries of malware belongs to 25 different families. to create a precise approach and considering the success of deep learning techniques for the classification of raising the vo ume of newly created malware, we proposed cnn and hybrid cnn svm model. the cnn is used as an automatic feature extract. Oft for the big data innovators gathering (big 2015). this thesis presents two novel and scalable approaches using convolutional neural networks cnns) to assign malware to its correspond ing family. on one hand, the first approach makes use of cnns to learn a feature hierarchy to discriminate amon. Based on these conditions and combined with the related documents, this paper analyses the nature and mechanism of cnn to classify the current malwares and proposes some possible prospects of it.
Github Cridin1 Malware Classification Cnn This Github Repository View a pdf of the paper titled malware classification using a hybrid hidden markov model convolutional neural network, by ritik mehta and olha jureckova and mark stamp. Aset created from binaries of malware belongs to 25 different families. to create a precise approach and considering the success of deep learning techniques for the classification of raising the vo ume of newly created malware, we proposed cnn and hybrid cnn svm model. the cnn is used as an automatic feature extract. Oft for the big data innovators gathering (big 2015). this thesis presents two novel and scalable approaches using convolutional neural networks cnns) to assign malware to its correspond ing family. on one hand, the first approach makes use of cnns to learn a feature hierarchy to discriminate amon. Based on these conditions and combined with the related documents, this paper analyses the nature and mechanism of cnn to classify the current malwares and proposes some possible prospects of it.
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