.png){kind=icon}
返回
Implementing a Deep Learning Model for Intrusion Detection on Apache Spark Platform
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3019931.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Internet evolution produced a connected world with a massive amount of data. This connectivity advantage came with the price of more complex and advanced attacks. Intrusion Detection System (IDS) is an essential component for security in modern networks. The IDS methodology is either signature-based detection or anomaly behavior detection. Recently, researchers adopted Deep Learning (DL) because it has a better performance than traditional machine learning algorithms. The use of DL to produce a model for the IDS may take a long time because of computation complexity and a large number of hyperparameters. Different DL models for IDS on Apache Spark have been implemented in this article. This article uses the famous Network Security Lab - Knowledge Discovery and Data Mining (NSL-KDD) dataset and presents a computation delay comparison between Apache Spark and regular implementation. Moreover, an enhanced model is used to improve attack detection accuracy.
Keyword:
Training
Machine learning
Sparks
Computational modeling
Intrusion detection
Support vector machines
Intrusion detection
bigdata
Hadoop
apache spark
deep learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
The ADAMTS13 Gene as the Immunological Culprit in Acute Acquired TTP - First Evidence of Genetic Out-Breeding Depression in Humans..png)
Blood
BloodIF0
PREAI
分享收藏Training cost-sensitive neural networks with methods addressing the class imbalance problem用解决类不平衡问题的方法训练代价敏感的神经网络
PREAI
分享收藏
Deep Learning Approach for Intelligent Intrusion Detection System面向智能入侵检测系统的深度学习方法IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容