Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 60-65

Figures :04

Tables : 01

Publication Link : Comparative Study of Data Mining and Machine Learning Approach for Anomaly Detection

 

 

 

Sunil M. Sangve : Computer Department, Savitribai Phule Pune University , ZCOER Pune, Maharashtra , India

Ravindra C. Thool : Computer Science and Engineering Department, SRTMU,SGGSIE &T, Nanded, Maharashtra, India

 

 

 

 

 

 

 

Intrusion Detection Systems (IDSs)

Data Mining

Machine Learning

Challenges

Considering the studied literature, it is clear that in order to have the capacity to secure a system against the novel attacks, the anomaly based intrusion detection is the best way. We have discussed the data mining and machine learning approach for anomaly detection. The data mining approaches are used for clustering and classification to divide the large dataset so that processing and computational complexity will reduce. The machine learning approach trains the system and gives the prediction in testing stage. The machine learning has many advantages in anomaly detection without human interference. Depending on labels used in input dataset, the anomaly detection is classified as supervised, semisupervised, and un-supervised. We are also focusing on combining the best features from data mining and machine learning approach and will propose the hybrid approach which gives better result than discussing methodology.

 

 

 

 

 

 

 

 

 

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