Abstract
Detection of Intrusion is an essential expertise business segment as well as a dynamic area of study and expansion caused by its requirement. Modern day intrusion detection systems still have these limitations of time sensitivity. The main requirement is to develop a system which is able of handling large volume of network data to detect attacks more accurately and proactively. Research conducted by on the KDDCUP99 dataset resulted in a various set of attributes for each of the four major attack types. Without reducing the number of features, detecting attack patterns within the data is more difficult for rule generation, forecasting, or classification. The goal of this research is to present a new method that Compare results of appropriately categorized and inaccurately categorized as proportions and the features chosen. Data mining is used to clean, classify and examine large amount of network data. Since a large volume of network traffic that requires processing, we use data mining techniques. Different Data Mining techniques such as clustering, classification and association rules are proving to be useful for analyzing network traffic. This paper presents the survey on data mining techniques applied on intrusion detection systems for the effective identification of both known and unknown patterns of attacks, thereby helping the users to develop secure information systems. Keywords: IDS, Data Mining, Machine Learning, Clustering, Classification DOI : 10.7176/CEIS/11-1-02 Publication date: January 31 st 2020
Highlights
A day’s usage of internet and world wide connectivity has been grown, well-proportionate with cyber attacks
We provided a general taxonomy of attack tactics against intrusion detection systems; We subdivided the Intrusion Detection System (IDS) task into three different phases, throughout the paper we identified a number of challenging issues that should be addressed by future research activities on intrusion detection
This paper provides the details of two types of intrusion detection and general working principle of IDS
Summary
A day’s usage of internet and world wide connectivity has been grown, well-proportionate with cyber attacks. 1: Classification of IDS based on its characteristics Because Intrusion Detection Systems performance is increased with usage of the Soft Computing methods to IDS, the computer security researchers are trying to apply. Host Based IDS HIDS monitors incoming and outgoing activity on a particular system in the network. It monitors the dynamic behavior and the state of the computer system. Audit logs contain records for events and activities taking place at individual Network resources It is done because these HIDS can detect attacks that cannot be seen by NIDS such as Intrusion and can be misused by trusted insider. APIDS finds, and enforces the correct use of the protocol
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
