论文信息 - Detection of Probe Attacks Using Machine Learning Techniques

Detection of Probe Attacks Using Machine Learning Techniques

In recent years, the number of attacks on the computer networks and its components are getting increasing. To protect from these attacks various Intrusion detection techniques have been used. Intrusion Detection System (IDS) is a system which collects and analyzes the information from the network to identify various attacks made against the components of a network. In this paper we presented a comprehensive analysis on Probe attacks, by applying various popular machine learning techniques such as Naïve Bayes, SVM, Multilayer Perceptron, Decision Trees etc. we used KDDcup99 data set to build the model. In this paper we proposed three layer architecture for detection of probe attacks. Principal Component Analysis is used for dimensionality reduction. We also removed duplicate samples from the training data set. Finally, we compared the performance of each classifier with the help of a line chart.

V. Kishore Babu

[1] V. Kishore Babu,et al. Detection of User to Root Attacks using Machine Learning Techniques , 2015 .

[2] R. Shanmugavadivu. NETWORK INTRUSION DETECTION SYSTEM USING FUZZY LOGIC , 2011 .

[3] Alpa Reshamwala,et al. A Review of Intrusion Detection System Using Neural Network and Machine Learning Technique , 2012 .

[4] Li Tian,et al. Research on Network Intrusion Detection System Based on Improved K-means Clustering Algorithm , 2009, 2009 International Forum on Computer Science-Technology and Applications.