BibTeX:

@article{IJIRSTV1I11121,
     title={A HOST BASED INTRUSION DETECTION SYSTEM USING IMPROVED EXTREME LEARNING MACHINE},
     author={MEGHA RAJ, Shijoe Jose and Ambikadevi Amma T},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={11},
     pages={327--332},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I11121.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

HIDS is very challenging due to high false alarm rate. Host based systems are based on building some reference models from execution traces to characterize the system behavior. These models are then used to classify the normal as well as abnormal behavior. Most of the popular techniques are based on using Extreme Learning Machine (ELM).First analyze the discontiguous patterns of system calls and extract the important feature using ELM. This method provides powerful solution to IDS problems. For dynamic nature interpret the semantic structure between system calls and programming languages. However the use of ELM requires long training time due to the large size of typical system call traces which makes ELM computationally infeasible. So in order to overcome this problem this paper introduces a new host based intrusion detection system using Improved Extreme Learning Machine (I-ELM), in an attempt to reduce the training overhead problem while increasing the detection rate. The key concept is to apply N-gram extraction algorithm. This method mainly focuses on mining the frequent common patterns (N-grams) in the system call traces instead of considering each trace. Thus the length of training sequence is reduced significantly compare to traditional ELM while keeping the accuracy rate.

Intrusion detection system (IDS), Host-based IDS (HIDS); Extreme Learning Machine (ELM), Improved ELM, N-gram extraction algorithm