Intrusion Detection System Using PCA with Random Forest Approach

IEEE BASE PAPER ABSTRACT:

With the evolution in wireless communication, there are many security threats over the internet. The intrusion detection system (IDS) helps to find the attacks on the system and the intruders are detected. Previously various machine learning (ML) techniques are applied on the IDS and tried to improve the results on the detection of intruders and to increase the accuracy of the IDS. This paper has proposed an approach to develop efficient IDS by using the principal component analysis (PCA) and the random forest classification algorithm. Where the PCA will help to organise the dataset by reducing the dimensionality of the dataset and the random forest will help in classification. Results obtained states that the proposed approach works more efficiently in terms of accuracy as compared to other techniques like SVM, Naïve Bayes, and Decision Tree. The results obtained by proposed method are having the values for performance time (min) is 3.24 minutes, Accuracy rate (%) is 96.78 %, and the Error rate (%) is 0.21 %.

PROJECT OUTPUT VIDEO:

ALGORITHM /MODEL USED:

Random Forest Classifier.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.

Hard Disk : 500 GB.

Monitor : 15’’ LED

Input Devices : Keyboard, Mouse

Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Subhash Waskle, Lokesh Parashar, Upendra Singh, “Intrusion Detection System Using PCA with Random Forest Approach”, IEEE CONFERENCE 2020.

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Intrusion Detection System Using PCA with Random Forest Approach