PhD in Intrusion Detection Using Machine Learning

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PhD in Intrusion Detection System Using Machine Learning Model

Intrusion detection system (IDS) is one of the implemented solution to act against the harmful attacks. To manage the development of computer based network system with heavy network traffic, hackers and malicious users devising new way of network intrusion.  Using certain machine learning algorithm like (Bayes Net, J48, Random forest and Random tree) to determine the accuracy of algorithm by classifying these attacks through intrusion detection system using machine learning model


In machine learning model the testing phase process is implemented to test randomly extracted. The extracted testing data includes all 21 types of attacks within KDD dataset.Here the confusion metrics used in classification algorithm and. all the machine learning classifiers are implemented for providing a compared results of average evaluating the efficiency and performance base on KDD dataset that the instance record extracted as training data to build training the process experiment has been taken for handling efficient new attacks based on malicious activity detection system using machine learning model

Real Time Network intrusion Detection System Using Machine Learning Model


  • For building the classifier training models the time requirement is one of the important issue to be faced.
  • The currently using dataset KDD99 has many issues and the construction of new dataset needs expert support with high labor cost.
  • To overcome the current challenges of IDS and to provide high performance in detecting the security attacks certain new system design is needed.
  • Facing a set of rules for solving the intrusion detection issues to perform test dataset.
PhD in Intrusion Detection System Using Machine Learning Model


  • By implementing SQL server after the KDD dataset for extract 600000 instances of records and to present it as a training data.
  • Traffic grouping is much helpful to control poor detection effect acquired by strong heterogeneity of flow.
  • Systematic dataset construction and incremental learning is useful to reflect the new attacks and correspond to the available network environment.
  • Clustering algorithm is one of the solution for produce more robust IDS which helps to limit the intrusion attacks.


  • In this proposal in malicious detection system using machine learning that they use Bayes Net and Random forest classifier learning algorithm is utilized.
  • The proposal in intrusion detection system using machine learning model is only based on comparing both learning algorithm which accepts 97% accuracy but classifier present acceptable performance parameter except false negative parameter. In future they need to consider “false negative and false positive rates” along with the result of algorithm.

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