@article{MAKHILLIJSSCEA20169228737,
    title = {Offering a New Method for Detection of Flood Attacks in
Voice Transmission Networks on the IP},
    journal = {International Journal of System Signal Control and Engineering Application},
    volume = {9},
    number = {2},
    pages = {17-23},
    year = {2016},
    issn = {1997-5422},
    doi = {ijssceapp.2016.17.23},
    url = {https://makhillpublications.co/view-article.php?issn=1997-5422&doi=ijssceapp.2016.17.23},
    author = {Farid},
    keywords = {Intrusion Detection Solution (IDS),the combination of feature extraction methods,combining the,unsupervised neural network,Iran},
    abstract = {Now a days, the voice transmission technologies on the networks based on the IP (VOIP) has become
one of the most widely used technologies in telecommunications due to lower costs and more flexibility. These
networks due to variation of supporting VOIP terminals are vulnerable in terms of security and against several
attacks such as lack of service, worms, DoS and DHCP attacks and etc. After attacking prevention methods,
such as encryption, one of the conventional methods for securing VOIP is using Intrusion Detection Solutions
(IDS) which include misuse detection methods and methods of detecting abnormal behavior. One area of
concern in the designing the intrusion detection systems is machine learning and data mining. On the other
hand, intrusion detection systems work with large volumes of data that include additional features and this,
slow down and thus reduce the efficiency of the training and testing process. For this reason, feature selection,
as one of the key issues of intrusion detection systems, includes finding a subset of more efficient features to
improve the accuracy of prediction. In this study a method is proposed in four phases, to identify the best
available features for intrusion detection and use them to design a compound classifier in order to detect the
attack packets to the network. The results of the proposed simulation method is indicative of a 99.73% accuracy
in detecting attacks.}
    }