.png){kind=icon}
返回
DeepBot: a time-based botnet detection with deep learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-020-04963-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Over the decades, as the technology of Internet thrives rapidly, more and more kinds of cyber-attacks are blasting out around the world. Among them, botnet is one of the most noxious attacks which has always been challenging to overcome. The difficulties of botnet detection stem from the various forms of attack since the viruses keep evolving to avoid themselves from being found. Rule-based botnet detection has its shortcoming of detecting dynamically changing features. On the other hand, the more the Internet functionalities are developed, the severer the impacts botnets may cause. In recent years, many network devices have suffered from botnet attacks as the Internet of things technology prospers, which caused great damage in many industries. Consequently, botnet detection has always been a critical issue in computer security field. In this paper, we introduce a method to detect potential botnets by inspecting the behaviors of network traffics from network packets. In the beginning, we sample the given packets by a period of time and extract the behavioral features from a series of packets. By analyzing these features with proposed deep learning models, we can detect the threat of botnets and classify them into different categories.
Keyword:
Botnet
Deep learning
RNN
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:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
A study of the inferior mesenteric and pelvic ganglia of guinea‐pigs with intracellular electrodes
OAAI
OAAI分享收藏A LSTM based framework for handling multiclass imbalance in DGA botnet detection基于LSTM的DGA僵尸网络检测中多类不平衡处理框架.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Purification of Bacteriophage λ Particles by Isopycnic Centrifugation through CsCl Gradients通过CsCl梯度等密度离心纯化λ噬菌体颗粒
PREAI
分享收藏
查看更多内容