Into the Unknown: Unsupervised Machine Learning Algorithms for Anomaly-Based Intrusion Detection

机译：进入未知：基于异常的入侵检测的无监督机器学习算法

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Anomaly detection aims at identifying patterns in data that do not conform to the expected behavior, relying on machine-learning algorithms that are suited for binary classification. It has been arising as one of the most promising techniques to suspect intrusions, zero-day attacks and, under certain conditions, failures. This tutorial aims to instruct the attendees to the principles, application and evaluation of anomaly-based techniques for intrusion detection, with a focus on unsupervised algorithms, which are able to classify normal and anomalous behaviors without relying on input data with labeled attacks.

机译：异常检测旨在依靠适合于二进制分类的机器学习算法来识别不符合预期行为的数据模式。它已成为怀疑入侵，零时差攻击以及在某些情况下出现故障的最有前途的技术之一。本教程旨在指导参与者了解基于异常的入侵检测技术的原理，应用和评估，重点是无监督算法，该算法能够对正常行为和异常行为进行分类，而无需依赖带有标记攻击的输入数据。

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《Annual IEEE-IFIP International Conference on Dependable Systems and Networks-Supplemental Volume》|2020年|81-81|共1页
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Tommaso Zoppi; Andrea Ceccarelli; Andrea Bondavalli;
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Anomaly detection; Intrusion detection; Tutorials; Tools; Software algorithms; Machine learning algorithms; Machine learning;

机译：异常检测;入侵检测;教程;工具;软件算法;机器学习算法;机器学习;

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1. Analysis of Machine Learning Techniques for Anomaly-Based Intrusion Detection [J] . Winfred Yaokumah, Isaac Wiafe International journal of agent technologies and systems . 2020,第1期

机译：基于异常的入侵检测机器学习技术分析
2. Deep and Machine Learning Approaches for Anomaly-Based Intrusion Detection of Imbalanced Network Traffic [J] . Razan Abdulhammed, Miad Faezipour, Abdelshakour Abuzneid, IEEE Sensors Letters . 2019,第1期

机译：基于深度和机器学习的不平衡网络流量基于异常的入侵检测方法
3. Select the Best Machine Learning Algorithms for Prediction and Classification of Intrusions using KDD99 Intrusion Detection Dataset [J] . Sara Tamy, Hicham Belhadaoui, Mahmoud Almostafa Rabbah, Indian Journal of Science and Technology . 2019,第37期

机译：使用KDD99入侵检测数据集选择用于入侵预测和分类的最佳机器学习算法
4. A Comparative Study of Machine Learning and Deep Learning in Network Anomaly-Based Intrusion Detection Systems [C] . Mohab Sameh Abdel-Wahab, Ahmed M. Neil, Ayman Atia International Conference on Computer Engineering and Systems . 2020

机译：基于网络异常的入侵检测系统机器学习与深度学习的比较研究
5. Intrusion Detection in IoT Systems Using Machine Learning Algorithms [D] . ?Basalan, Abdurrahman 2020

机译：使用机器学习算法的IOT系统入侵检测
6. Unsupervised Classifications of Depression Levels Based on Machine Learning Algorithms Perform Well as Compared to Traditional Norm-Based Classifications [O] . Zhenkai Yang, Chuansheng Chen, Hanwen Li, 2020

机译：与传统的基于规范的分类相比基于机器学习算法的抑郁水平无监督分类表现良好
7. Anomaly-Based Intrusion Detection: Feature Selection and Normalization Influence to the Machine Learning Models Accuracy [O] . Danijela Protić, Miomir Stanković 2018

机译：基于异常的入侵检测：特征选择和归一化影响对机器学习模型的准确性

Into the Unknown: Unsupervised Machine Learning Algorithms for Anomaly-Based Intrusion Detection

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