Anomaly detection using libsvm training tools

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Dependable and Secure Computing , , 4 1 : 56— Histogram-based traffic anomaly detection. Please, try again. If the error persists, contact the administrator by writing to support infona.

You can change the active elements on the page buttons and links by pressing a combination of keys:. I accept. Polski English Login or register account. Abstract Intrusion detection is the means to identify the intrusive behaviors and provides useful information to intruded systems to respond fast and to avoid or reduce damages.

In recent years, learning machine technology is often used as a detection method in anomaly detection. In this research, we use support vector machine as a learning method for anomaly detection, and use LibSVM as the support vector machine tool.

By using this tool, we get rid of numerous and complex operation and do not have to use external tools for finding parameters as need by using other algorithms such as the genetic algorithm. Experimental results show that high average detection rates and low average false positive rates in anomaly detection are achieved by our proposed approach. Authors Close. Steve Steve 1 1 1 bronze badge. Do you see the same with a reasonable test set? I'm new to stats and machine learnng, is there a better way I should be going about this?

That's the case even if later on you want to deploy and test one point at a time. I would suggest reading some introductory material on machine learning before going any further The problem I'm trying to address is the ability to authenticate users based on their keystroke timing patterns during login.

Therefore I cant get testing data of imposters trying hack into the account for security reasons. Would it possible to generate data for the testing purposes? Add a comment. Active Oldest Votes. Improve this answer. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.