Open AccessNIDM: Novel Intrusion Detection Model Using CNN-LSTM for Big Data Environment
Author(s) -
Rohan Benhal
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.38503
Subject(s) - computer science , big data , artificial intelligence , intrusion detection system , machine learning , deep learning , safeguarding , component (thermodynamics) , data mining , medicine , physics , nursing , thermodynamics
Abstract: Machine learning-based (IDS) have become a critical component of safeguarding our economic and national security because of the massive quantities of data produced each day and the growing interconnection of the world's Internet infrastructures. The existing machine Learning Model technique may have difficulty comprehending the ever-increasingly complex distribution of data invasion patterns. With a small number of data points, a single deep learning algorithm may be ineffective at capturing different patterns for intrusive attacks. We presented CNN-LSTM Novel Intrusion Detection Model for Big Data to improve the efficiency of IDS-based CNN-LSTM even further (NIDM). NIDM uses behavioural traits and content functions to understand the characteristics when compared to earlier single learning model tactics, this strategy can improve the rate of intrusive attack detection. Keywords: IDS, Machine Learning, LSTM, CNN.