.png){kind=icon}
返回
Collaborative Framework for Early Detection of RAT-Bots Attacks
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2919680.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Attackers tend to use Remote Access Trojans (RATs) to compromise and control a targeted computer, which makes the RAT detection as an active research field. This paper introduces a machine learning-based framework for detecting compromised hosts and networks that are infected by the RAT-Bots. The proposed framework consists of two agents that are integrated to achieve reliable early detection of the RAT-bots. The first agent, the host agent, is responsible for monitoring the system behavior of the running host and raising an alarm for any anomalies. The second agent, the network agent, monitors the network traffic to extract any malicious patterns. The integrated approach improves both the detection ratio and accuracy. However, each approach cannot separately achieve the same performance as the proposed RAT-Bots detection framework. The performance of the introduced framework is evaluated by using real-world benchmark datasets. The experimental results show that the proposed approach can achieve an accuracy of 98.83% with 1.45% false positive rate.
Keyword:
Bots
botnets
host-based detection
network-based detection
machine learning algorithms
rootkit behavior
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
.png){kind=icon}
Is Flavored Milk Really a Bad Beverage Choice? The Nutritional Benefits of Flavored Milk Outweigh the Added Sugars Content风味牛奶真的是一个糟糕的饮料选择吗？风味牛奶的营养益处超过了其添加糖含量。
OAAI
OAAI分享收藏Capacity Building for a New Multicenter Network Within the ECHO IDeA States Pediatric Clinical Trials NetworkECHO IDeA States儿科临床 Trial 网络内新多中心网络的能力建设OAAI
OAAI分享收藏
查看更多内容