@article{MAKHILLIJSC20149321191,
    title = {A Self Learning Algorithm for Anomaly Based Intrusion Detection System using Genetic Neural Network},
    journal = {International Journal of Soft Computing},
    volume = {9},
    number = {3},
    pages = {117-121},
    year = {2014},
    issn = {1816-9503},
    doi = {ijscomp.2014.117.121},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2014.117.121},
    author = {M. and},
    keywords = {Intrusion Detection System,neural network,Genetic algorithm,genetic neural network,network traffic},
    abstract = {An Anomaly based Intrusion Detection System is a one which monitors the system 
  or network traffic looking for anomalous behaviour rather than matching the 
  user behaviour pattern alone. Hence, the Anomaly Based Intrusion Detection algorithms 
  have the capability to extend their detection mechanisms to detect unknown attacks. 
  In this research, a Self Learning algorithm for anomaly based Intrusion Detection 
  Model which is based on genetic neural network is proposed. The genetic neural 
  network combines the good global searching ability of Genetic algorithm with 
  the accurate local searching feature of back propagation neural networks. Here, 
  it is used to optimize the initial weights of the neural network. The scope 
  of the algorithm in this proposed research remains in identifying the malicious 
  packet.}
    }