Journal of Cybersecurity and Information Management
  JCIM
  2690-6775
  2769-7851
  
   10.54216/JCIM
   https://www.americaspg.com/journals/show/1811
  
 
 
  
   2019
  
  
   2019
  
 
 
  
   An Encrypted Rules and Extreme Learning Machine Approach for Enhancement of Data Security
  
  
   Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, India
   
    Amit
    Amit
   
  
  
   Among the many uses for WSN, which is an ad hoc wireless system, are conveyance, calamity administration, industrialized observing, health observing, and so on. Intrusion Detection System (IDS) is a top-tier network security measure. In order to prevent cross-layer attacks, IDS detection rates must be high. Using a technique known as the "Rule of Thumb" or ELM (Extreme Learning Machine) algorithm, WSN is able to predict the future with a great grade of accurateness. The projected RELM provides a comprehensive overview of both the attacks and the rules for detecting them. The rules can identify threats at the different layers. If the rule-founded IDS were deployed at the sensor nodes, less data would need to be transmitted over the network, saving power. Relative to the SVM (Support Vector Machine) and BPN (Back Propagation Neural Network) on the NSL-KDD dataset, RELM evaluates ELM's detection rate. Because of its superior detection rate, ELM has been used as the foundation of the IDS deployed at the BS to protect it against intrusion. If the criteria were combined with the ELM algorithm, the resulting system would have a higher detection rate than any currently available alternative.
  
  
   2023
  
  
   2023
  
  
   47
   56
  
  
   10.54216/JCIM.110205
   https://www.americaspg.com/articleinfo/2/show/1811