.png){kind=icon}
返回
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization
.png){kind=icon}
OA
AI
DOI：10.1186/s40537-019-0248-6.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the growing use of information technology in all life domains, hacking has become more negatively effective than ever before. Also with developing technologies, attacks numbers are growing exponentially every few months and become more sophisticated so that traditional IDS becomes inefficient detecting them. This paper proposes a solution to detect not only new threats with higher detection rate and lower false positive than already used IDS, but also it could detect collective and contextual security attacks. We achieve those results by using Networking Chatbot, a deep recurrent neural network: Long Short Term Memory (LSTM) on top of Apache Spark Framework that has an input of flow traffic and traffic aggregation and the output is a language of two words, normal or abnormal. We propose merging the concepts of language processing, contextual analysis, distributed deep learning, big data, anomaly detection of flow analysis. We propose a model that describes the network abstract normal behavior from a sequence of millions of packets within their context and analyzes them in near real-time to detect point, collective and contextual anomalies. Experiments are done on MAWI dataset, and it shows better detection rate not only than signature IDS, but also better than traditional anomaly IDS. The experiment shows lower false positive, higher detection rate and better point anomalies detection. As for prove of contextual and collective anomalies detection, we discuss our claim and the reason behind our hypothesis. But the experiment is done on random small subsets of the dataset because of hardware limitations, so we share experiment and our future vision thoughts as we wish that full prove will be done in future by other interested researchers who have better hardware infrastructure than ours.
Keyword:
Intrusion Detection System (IDS)
Long Short Term Memory (LSTM)
Recurrent neural network (RNN)
Distributed deep learning
Big data analysis
Spark
MAWI dataset
MAWILAB gold standard
AGURIM
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:
6.4
论文数:
1.5K
被引数:
1.1W
机构
暂无机构信息
引用论文
A contact model of traveling-wave ultrasonic motors considering preload and load torque effects
PREAI
分享收藏分享收藏Selection of Candidate Support Vectors in incremental SVM for network intrusion detection.png)
COMPUTERS & SECURITY
COMPUTERS & SECURITYIF5.4
PREAI
分享收藏
查看更多内容