TY  - JOUR
T1  - Effective Cross Layer Intrusion Detection in Mobile Ad Hoc Networks Using Rough Set Theory and Support Vector Machines
AU - Poongothai, T. AU - Duraiswamy, K. 
JO  - Asian Journal of Information Technology
VL  - 12
IS  - 8
SP  - 242
EP  - 249
PY  - 2013
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2013.242.249
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2013.242.249
KW  - Mobile ad hoc networks
KW  -intrusion detection
KW  -machine learning
KW  -rough set theory
KW  -cross-layer design
KW  -support vector machine
AB  - 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.
ER  -