Sayed Abdulhayan,
Varsha M,
Adam Adhil,
Hazil Muhammed,
Mohamed Favas V.P.,
Mohammad Fadil,
- Professor, P A College of Engineering, Mangalore, Karnataka, India
- Student, P A College of Engineering, Mangalore, Karnataka, India
- Student, P A College of Engineering, Mangalore, Karnataka, India
- Student, P A College of Engineering, Mangalore, Karnataka, India
- Student, P A College of Engineering, Mangalore, Karnataka, India
- Student, P A College of Engineering, Mangalore, Karnataka, India
Abstract
Malicious cyberattacks can frequently hide enormous amounts of typical data in unbalanced network traffic. It is very stealthy and obfuscating in cyberspace, which makes it challenging for Network Intrusion Detection Systems (NIDS) to guarantee the precision and promptness of detection. This essay investigates. Machine learning and deep learning are utilized for intrusion detection in imbalanced network traffic. It offers a novel method for addressing the problem of class imbalance termed the Difficult Set Sampling Technique (DSSTE). First, use the Edited Nearest Neighbor (ENN) approach to extract the easy and tough sets from the imbalanced training set. Next, use the KMeans technique to compress the majority samples in the difficult set to decrease the majority. Test both the more conventional NSL-KDD intrusion dataset and the more modern and comprehensive CSE-CIC-IDS2018 intrusion dataset. XGBoost, Support Vector Machine (SVM), Random Forest (RF), Short-and Long-Term Memory (LSTM), Mini-VGGNet, and AlexNet are examples of traditional classification models. The results of the experiment demonstrate that our proposed DSSTE algorithm performs better than the alternative.
Keywords: IDS, imbalanced network traffic, machine learning, deep learning, CNN, RNN, ML, Network Intrusions
[This article belongs to Recent Trends in Electronics Communication Systems(rtecs)]
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Recent Trends in Electronics Communication Systems
| Volume | 11 |
| Issue | 02 |
| Received | May 31, 2024 |
| Accepted | June 6, 2024 |
| Published | August 14, 2024 |