Tiger hash based AdaBoost machine learning classifier for secured multicasting in mobile healthcare system

Abstract

Secure multicast routing is an important in mobile healthcare system. Few research works have been developed to prevent malicious behaviors from disclosing integrity of data in mobile healthcare systems using machine learning technique. But, the performance of conventional machine learning technique was not effectual. In order to overcome this limitation, Tiger hashing based AdaBoost with SVM classifier (TH-ASVMC) technique is proposed. The TH-ASVMC technique is designed to improve the security of multicast routing in MANETs with higher data integrity rate and therefore reducing the time taken. The TH-ASVMC technique initially used Tiger hash function which converts the patient data to be transmitted over a wireless network into a hash value for maintaining the data integrity during the process of multicasting in mobile healthcare system. After that, TH-ASVMC technique used AdaBoost with SVM classifier to classify the nodes in mobile healthcare system as authentic or unauthentic based on measurement of trust value for securing multicast routing with minimum communication overhead. Thus, TH-ASVMC technique choose the only an authentic node for routing the hash value of patient data to multiple destination nodes in mobile healthcare system. This process results in enhanced reliability and scalability of secured multicast routing. The TH-ASVMC technique conducts the simulations works on metrics such as data integrity rate, scalability, reliability and communication overhead. The simulation results shows that the TH-ASVMC technique is able to improve the reliability and data integrity rate of multicast routing as compared to state-of-the-art works.