期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2021
卷号：3
期号：5
页码：890-895
DOI：10.35629/5252-0305763767
语种：English
出版社：IJAEM JOURNAL
摘要：Intrusion Detection System (IDS) is meant to be a software application which monitors the network or system activities and finds if any malicious operations occur. Tremendous growth and usage of internet raises concerns about how to protect and communicate the digital information in a safe manner. Nowadays, hackers use different types of attacks for getting the valuable information. As the internet emerging into the society, new stuffs like viruses and worms are imported. The malignant so, the users use different techniques like cracking of password, detecting unencrypted text are used to cause vulnerabilities to the system. Hence, security is needed for the users to secure their system from the intruders. Firewall technique is one of the popular protection techniques and it is used to protect the private network from the public network. IDS are used in network related activities, medical applications, credit card frauds, Insurance agency. Many intrusion detection techniques, methods and algorithms help to detect these attacks. This main objective of this project is to provide a comparative study about intrusion detection using various machine learning and deep learning techniques. Various machine learning techniques have been used to develop IDs, such as Back Propagation, Feed Forward, Recurrent neural network and Multilayer Perceptron (MLP) in real time network datasets such as Intrusion Detection System (IDS) datasets and UNSW datasets. MLP is widely used neural network classifier based on number of classes (output) and number of hidden layers, MLP uses weights for every node at neural network, most effective attributes will get large weights conversely attributes not affect in predictive class. The proposed system can be analysed in terms of error rate and accuracy values and implement in python tool for performance analysis.
