Abstract
In the age of advancing technology, artificial intelligence, and big data, Remote to Local (R2L) attacks are increasingly threatening cloud computing environments, heightening concerns about security and privacy. Intrusion detection systems (IDS) using Artificial Intelligence play a role in safeguarding data integrity within databases by swiftly identifying and isolating suspicious records. Furthermore, machine learning techniques enhance the effectiveness of these IDS by continuously adapting to new attack patterns and improving accuracy. This research investigates the use of Decision Tree, a Machine Learning Algorithm for enhancing Remote to Local (R2L) intrusion detection capabilities, utilizing the KDD Cup 1999 dataset and the NSL-KDD dataset. We applied the algorithm to the KDD Cup 1999 dataset, and to the NSL-KDD dataset individually. Later, we combined the two datasets and applied the algorithm to it. The results indicate that the use of combined dataset enhances intrusion detection accuracy. The IDS achieves an accuracy, detection rate, and false alarm rate of 99.93%, 91% and 0.08%, surpassing the individual accuracies of 99.90%, 83%, 1.08%, for KDD Cup 1999 and 99.83%, 83%, 0.08% for NSL-KDD.
College
College of Science & Engineering
Department
Computer Science
Campus
Winona
First Advisor/Mentor
Collin Engstrom
Second Advisor/Mentor
Sudharsan Iyengar
Third Advisor/Mentor
Mingrui Zhang
Location
Ballroom - Kryzsko Commons
Start Date
4-18-2024 9:00 AM
End Date
4-18-2024 10:00 AM
Presentation Type
Poster Session
Format of Presentation or Performance
In-Person
Session
1a=9am-10am
Poster Number
55
Included in
Enhancing R2L Intrusion Detection Using Decision Trees
Ballroom - Kryzsko Commons
In the age of advancing technology, artificial intelligence, and big data, Remote to Local (R2L) attacks are increasingly threatening cloud computing environments, heightening concerns about security and privacy. Intrusion detection systems (IDS) using Artificial Intelligence play a role in safeguarding data integrity within databases by swiftly identifying and isolating suspicious records. Furthermore, machine learning techniques enhance the effectiveness of these IDS by continuously adapting to new attack patterns and improving accuracy. This research investigates the use of Decision Tree, a Machine Learning Algorithm for enhancing Remote to Local (R2L) intrusion detection capabilities, utilizing the KDD Cup 1999 dataset and the NSL-KDD dataset. We applied the algorithm to the KDD Cup 1999 dataset, and to the NSL-KDD dataset individually. Later, we combined the two datasets and applied the algorithm to it. The results indicate that the use of combined dataset enhances intrusion detection accuracy. The IDS achieves an accuracy, detection rate, and false alarm rate of 99.93%, 91% and 0.08%, surpassing the individual accuracies of 99.90%, 83%, 1.08%, for KDD Cup 1999 and 99.83%, 83%, 0.08% for NSL-KDD.
