• Corpus ID: 15890016

An Enhanced Data Mining Based Intrusion Detection System (IDS) using Selective Feedback

@inproceedings{Adebowale2013AnED,
  title={An Enhanced Data Mining Based Intrusion Detection System (IDS) using Selective Feedback},
  author={Ajayi Adebowale and A IdowuS. and Babcock},
  year={2013}
}

• A. Adebowale, A. IdowuS., Babcock
• Published 2013
• Computer Science

Intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. Data mining helps in identifying implicit and sometimes long patterns in network traffic data and consequently stating valid bounds for network traffic. Classification-based data mining models for intrusion detection are often ineffective in dealing with dynamic changes in intrusion patterns and characteristics, making it imperative for them to become adaptive to the flow of traffic going through the network. There must be a continuous learning on the part of the IDS so it can train itself to identify false negatives that were overlooked before in a certain period of time. This study explored the use of selective feedback to improve the efficiency of C4.5 (a data mining based research IDS) by using some algorithms based on machine learning paradigms namely, “smart learners”, “incremental learners” and “meta learners”. Using C4.5 as the IDS in the framework for Intrusion detection and the NSL-KDD dataset to represent real streaming network traffic, several experiments were performed and an evaluation of the classifier’s performance was done using the confusion matrix and classification errors as the evaluation metric. Key Words— Data mining, Intrusion detection systems, Smart learners, Meta learners, C4.5, NSL-KDD.