Name
Title | ||
|---|---|---|
A Comprehensive Data Sampling Analysis Applied To The Classification Of Rare Iot Network Intrusion Types |
Abstract | ||
|---|---|---|
With the rapid growth of Internet of Things (IoT) network intrusion attacks, there is a critical need for sophisticated and comprehensive intrusion detection systems (IDSs). Classifying infrequent intrusion types such as root-to-local (R2L) and user-to-root (U2R) attacks is a reoccurring problem for IDSs. In this study, various data sampling and class balancing techniques-Generative Adversarial Network (GAN)-based over-sampling, k-nearest-neighbor (kNN) oversampling, NearMiss-1 undersampling, and class weights-were used to resolve the severe class imbalance affecting U2R and R2L attacks in the NSL-KDD intrusion detection dataset. Artificial Neural Networks (ANNs) were trained on the adjusted datasets, and their performances were evaluated with a multitude of classification metrics. Here, we show that using no data sampling technique (baseline), GAN-based oversampling, and NearMiss-1 undersampling, all with class weights, displayed high performances in identifying R2L and U2R attacks. Of these, the baseline with class weights had the highest overall performance with an F1-score of 0.11 and 0.22 for the identification of U2R and R2L attacks, respectively. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CCNC49032.2021.9369617 | 2021 IEEE 18TH ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC) |
Keywords | DocType | ISSN |
Machine Learning, Internet of Things, NSL-KDD, Intrusion Detection, Generative Adversarial Networks, Data Sampling | Conference | 2331-9852 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suchet Sapre | 1 | 0 | 0.34 |
| Khondkar R. Islam | 2 | 0 | 0.34 |
| Pouyan Ahmadi | 3 | 0 | 0.68 |