Naive Bayes and SVM based NIDS
Naive Bayes and SVM based NIDS
复制标题
基于朴素贝叶斯和 SVM 的 NIDS
DOI：
10.1109/icict43934.2018.9034411
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Devashree Limaye
中科院分区：
文献类型：
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作者：
Mrudul Dixit;Ankita Moholkar;S. Limaye;Devashree Limaye
在当今世界，互联网服务的使用已经广泛增长。随着用户数量的增加，对互联网服务的攻击数量也呈指数级增长。虽然有许多入侵检测系统，但确保数据安全仍然是一个具有挑战性的问题。DDoS攻击已经在网络中存在了很长一段时间，大量的主机仍然容易受到DDoS攻击。考虑到所有的情况下，本文的目的是定义一个新的入侵检测系统的统计行为和机器学习。所提出的方法提供了一种解决方案，以确保网络资源，通过使用流记录的统计参数进行异常检测，并在一个准确和高效的系统，具有更快的响应时间的结果。
In today's world the use of internet services has widely grown. As the number of users has increased the number of attacks on an internet service has also increased exponentially. Although there are a number of intrusion detection systems, ensuring the data security is still a challenging issue. DDoS attacks have been in the networks for a very long time and a large number of hosts are still vulnerable to DDoS attacks. Considering all the scenarios this paper aims at defining a new Intrusion Detection System using statistical behavior and Machine Learning. The proposed approach offers a solution to secure the network resources by using statistical parameters of the flow records for anomaly detection and results in an accurate and efficient system, having a faster response time.