这里包含了每一种攻击的数量, 后面会做简单的分析:UNSW-NB15_LIST_EVENTS.csv. 该数据集已经进行了训练集和测试集的分割, 文件分别如下:UNSW_NB15_training-set.csvandUNSW_NB15_testing-set.csv. 在训练集中共有175341条记录, 在测试集中共有82332条记录. The number of records in the training set is 17...
UNSW-NB15总体介绍 数据集的官⽹:数据集下载链接:数据集中⼀共有9种攻击: This dataset has nine types of attacks, namely, Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode and Worms.数据集⼀共有49个特征, 我们会在后⾯对每⼀种特征进⾏介绍.在csv中保存的数据...
Feature coded UNSW_NB15 intrusion detection data. intrusion-detectionintrusionkdd99unsw-nb15 UpdatedJan 24, 2018 zhmhbest/python-nidsdata Star41 Code Issues Pull requests 这是一个封装了KDDCup99、NSL-KDD、UNSW-NB15等入侵监测数据集的Python包。
UNSW_NB15，可以尝试从kaggle中找这个数据的代码，基本的机器学习模型效果也挺好的。https://www.kaggle...
他们将这些在 KDD-99 上表现良好的深度神经网络(Deep Neural Network,DNN)模型应用于其他数据集,例如 NSL-KDD、UNSW-NB15、Kyoto、WSN-DS 和 CICIDS-2017,并对其进行了全面而完整的分析。实验结果表明,使用深度学习作为分类器学习人工特征所含信息做出决策的能力要比传统机器学习更好。此外,Xu 等人 详细对比了采用...
This paper work focus on analysis and comparison of UNSW-NB15 with NSL-KDD dataset based on performance analysis and accuracy using machine learning classifiers. Feasibility, reliability and dependability of the dataset is reviewed and discussed by considering various performance measures such as ...
UNSW-NB15 is a network intrusion dataset. It contains nine different attacks, includes DoS, worms, Backdoors, and Fuzzers. The dataset contains raw network packets. The number of records in the training set is 175,341 records and the testing set is 82,33
综上所述,不同的深度学习模型在入侵检测系统中会产生不同的应用效果。本章节中所提到的研究工作主要使用 4 种典型的深度学习模 型, 结合 KDD Cup99、NSL-KDD、UNSWNB15 数据集,通过实验来测试入侵检测的准确率和检测性能。这对未来的研究有着一定的指导意义。
Existing and novel methods are utilised to generate the features of the UNSWNB15 data set. This data set is available for research purposes and can be accessed from the link. 展开 关键词: testbed UNSW-NB15 data set NIDS low footprint attacks pcap files ...