Template-Type: ReDIF-Article 1.0
Author-Name:Urwa Bibi, Hafiz GulfamAhmadUmer, Rimsha Jamil Ghilzai,and Muskan Maryam
Author-Email:urwatariqkhosa@gmail.com
Author-Workplace-Name:Ghazi University D.GKhan
Title:NeuroSecure-IoMT: Deep Learning Meets Cyber Defense in the Internet of Medical Things
Abstract:Intrusion deduction systems (IDS) are crucial to preservingsensitive medical information fromcyber  threats.  However,  issueswith  multi-class  intrusion  detection  include an imbalanceddata set, poor accuracy for minority classes,and a lack of flexibility in handling complex real-world situations. To address these issues, we provide a hybrid framework that combines machine learning and deep learning methodsto address these problems. The model uses a random forest classifier for anomaly detection after reducing dimensionality using an autoencoder.  The  Synthetic  Minority  Oversampling  Technique  (SMOTE)  was  used  during processing to ensure equitable class representation and reduce class imbalance. A multi-class intrusion detection dataset tailored to healthcare applications was used to thoroughly test the suggested  framework,  which  provides  an  impressive  99%  accuracy  rate.  In  addition  to  its excellent accuracy, the model addresses important issuesin multi-class Intrusion detection by exhibiting  remarkable  precision  for  minority  classes  and  consistent  performance  across  all categories. These results highlight the framework'seffectiveness in providing dependable and effective normal detection solutions, which makesit ideal for implementation incrucial sectors like healthcare, their accuracy and data security are crucial.
Keywords:Intrusion Detection System (IDS), Machine Learning (ML), Deep Learning (DL), Autoencoder, Random Forest, Dimensionality Reduction
Journal:International Journal of Innovations in Science and Technology
Pages:1179-1199
Volume:7
Issue:2
Year:2025
Month:June
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1418/1956
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1418
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:2:p:1179-1199