计算机科学
蜜罐
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软件定义的网络
服务拒绝攻击
前进飞机
阻塞(统计)
机器学习
块(置换群论)
软件
表(数据库)
计算机网络
贝叶斯网络
控制器(灌溉)
朴素贝叶斯分类器
集合(抽象数据类型)
人工智能
数据挖掘
互联网
操作系统
支持向量机
网络数据包
几何学
生物
程序设计语言
数学
农学
作者
Saurav Nanda,Faheem Zafari,Casimer DeCusatis,Eric Wedaa,Baijian Yang
标识
DOI:10.1109/nfv-sdn.2016.7919493
摘要
An experimental setup of 32 honeypots reported 17M login attempts originating from 112 different countries and over 6000 distinct source IP addresses. Due to decoupled control and data plane, Software Defined Networks (SDN) can handle these increasing number of attacks by blocking those network connections at the switch level. However, the challenge lies in defining the set of rules on the SDN controller to block malicious network connections. Historical network attack data can be used to automatically identify and block the malicious connections. There are a few existing open-source software tools to monitor and limit the number of login attempts per source IP address one-by-one. However, these solutions cannot efficiently act against a chain of attacks that comprises multiple IP addresses used by each attacker. In this paper, we propose using machine learning algorithms, trained on historical network attack data, to identify the potential malicious connections and potential attack destinations. We use four widely-known machine learning algorithms: C4.5, Bayesian Network (BayesNet), Decision Table (DT), and Naive-Bayes to predict the host that will be attacked based on the historical data. Experimental results show that average prediction accuracy of 91.68% is attained using Bayesian Networks.
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