@article{MAKHILLAJIT20131285770,
    title = {Effective Cross Layer Intrusion Detection in Mobile Ad Hoc Networks Using Rough Set Theory and Support Vector Machines},
    journal = {Asian Journal of Information Technology},
    volume = {12},
    number = {8},
    pages = {242-249},
    year = {2013},
    issn = {1682-3915},
    doi = {ajit.2013.242.249},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2013.242.249},
    author = {T. and},
    keywords = {Mobile ad hoc networks,intrusion detection,machine learning,rough set theory,cross-layer design,support vector machine},
    abstract = {Intrusion detection on Mobile Ad Hoc Networks (MANET) is a 
  challenging task due to its unique characteristics such as open medium, dynamic 
  topology, lack of centralized management and highly resource constrained nodes. 
  Conventional Intrusion Detection System developed for wired networks cannot 
  be directly applied to MANET. It needs to be redesigned to suit the ad hoc technology. 
  Proposed IDS uses cross layer features instead of using single layer features 
  to improve the performance. Also, the proposed system maximizes the detection 
  accuracy by using two machine learning techniques. Support Vector Machines (SVM) 
  and rough set theory are used together to take the advantage of better accuracy 
  of SVM and faster speed of rough set. The performance of the system is validated 
  using Network Simulator (NS2). The simulation results demonstrate that the proposed 
  IDS effectively detect the anomalies with high detection accuracy.}
    }