Anomaly Detection via Improved Rain Optimization Algorithm and Stacked Autoencoder Hoeffding Tree

Hsaini, Maryam, Alhelli, Firas Abdul Kadhim Mohamed, Mokheef, Dalia Abdulrahim

Innovative Multidisciplinary Journal of Applied Technology · 2024-yil

Annotatsiya

Professionals of Cybersecurity suggest that cyber-attack cost of damage would widely increase. Huge web usage increases stress over the way of securely passing on electronic info. Diagnosing faults and anomalies in real-time industrial systems is a concern because of enough covering an industrial system’s complexity and difficulty. For security improvement, intrusion detection systems (IDSs) are applied for anomaly detection in network traffic. Now, IDS technology has concerns about performance based on false alarm notifications, unknown attack diagnoses, times, and accuracy detection. Machine learning (ML) methods have been increasingly applied in IDS for many years. However, such methods yet suffer from a lack of a labeled set of data, low accuracy, and heavy overhead. Reducing dimensionality plays an important role in IDS, as anomaly diagnosis from high dimensional network traffic attributes is a time-consuming process. Selection of Feature selection affects analysis speed. This paper concentrates on developing IDS effectiveness by applying the proposed Stacked Autoencoder Hoeffding Tree approach (SAE-HT) applying an Improved Rain Optimization Algorithm (IROA) for the selection of features. Experiments on the dataset NSL-KDD illustrate that our model multi-classification possesses great performance. In comparison to the other mechanisms of ML in the accuracy case, our model performs better than such mechanisms. The presented technique obtained an accuracy of 98.82 on a dataset of NSLKDD. Such outcomes of tests show that the presented strategy could properly and efficiently diagnose bad data.

Maqola ma’lumotlari
MualliflarHsaini, Maryam, Alhelli, Firas Abdul Kadhim Mohamed, Mokheef, Dalia Abdulrahim
JurnalInnovative Multidisciplinary Journal of Applied Technology
NashriyotТДТУ Қўқон филиали
ISSN2995-486X, 2995-486x
Nashr sanasi2024-10-21
Jild2
Son10
Betlar170-180
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

Anomaly Detection, feature selection, Rain Optimization Algorithm, Autoencoder, Hoeffding Tree

Ilmiy soha

Texnika

Iqtibos keltirish

APAHsaini, Maryam, Alhelli, Firas Abdul Kadhim Mohamed, & Mokheef, Dalia Abdulrahim (2024). Anomaly Detection via Improved Rain Optimization Algorithm and Stacked Autoencoder Hoeffding Tree. Innovative Multidisciplinary Journal of Applied Technology, 2(10), 170-180. https://www.multijournals.org/index.php/innovative/article/view/2509
GOST (OAK)Hsaini, Maryam, Alhelli, Firas Abdul Kadhim Mohamed, Mokheef, Dalia Abdulrahim Anomaly Detection via Improved Rain Optimization Algorithm and Stacked Autoencoder Hoeffding Tree // Innovative Multidisciplinary Journal of Applied Technology. – 2024. – T. 2, № 10. – P. 170-180.
MLAHsaini, Maryam, Alhelli, Firas Abdul Kadhim Mohamed, and Mokheef, Dalia Abdulrahim. “Anomaly Detection via Improved Rain Optimization Algorithm and Stacked Autoencoder Hoeffding Tree.” Innovative Multidisciplinary Journal of Applied Technology, 2(10), 170-180, 2024. https://www.multijournals.org/index.php/innovative/article/view/2509
ChicagoHsaini, Maryam, Alhelli, Firas Abdul Kadhim Mohamed, and Mokheef, Dalia Abdulrahim. “Anomaly Detection via Improved Rain Optimization Algorithm and Stacked Autoencoder Hoeffding Tree.” Innovative Multidisciplinary Journal of Applied Technology, 2(10), 170-180 (2024). https://www.multijournals.org/index.php/innovative/article/view/2509
HarvardHsaini, Maryam, Alhelli, Firas Abdul Kadhim Mohamed, and Mokheef, Dalia Abdulrahim (2024) ‘Anomaly Detection via Improved Rain Optimization Algorithm and Stacked Autoencoder Hoeffding Tree’, Innovative Multidisciplinary Journal of Applied Technology, 2(10), 170-180. https://www.multijournals.org/index.php/innovative/article/view/2509
BibTeX
@article{hsainimaryam2024,
  title = {Anomaly Detection via Improved Rain Optimization Algorithm and Stacked Autoencoder Hoeffding Tree},
  author = {Hsaini, Maryam and Alhelli, Firas Abdul Kadhim Mohamed and Mokheef, Dalia Abdulrahim},
  journal = {Innovative Multidisciplinary Journal of Applied Technology},
  year = {2024},
  volume = {2},
  number = {10},
  pages = {170-180},
  url = {https://www.multijournals.org/index.php/innovative/article/view/2509}
}

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