Anomaly Detection Approach Based on Deep Neural Network and Dropout
Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challe...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Baghdad, College of Science for Women
2020-06-01
|
| Series: | مجلة بغداد للعلوم |
| Subjects: | |
| Online Access: | http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4000 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849711261386801152 |
|---|---|
| author | Zaid Hussien et al. |
| author_facet | Zaid Hussien et al. |
| author_sort | Zaid Hussien et al. |
| collection | DOAJ |
| description | Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss function to enforce the proposed model in multiple classification, including five labels, one is normal and four others are attacks (Dos, R2L, U2L and Probe). Accuracy metric was used to evaluate the model performance. The proposed model accuracy achieved to 99.45%. Commonly the recognition time is reduced in the NIDS by using feature selection technique. The proposed DNN classifier implemented with feature selection algorithm, and obtained on accuracy reached to 99.27%. |
| format | Article |
| id | doaj-art-4e26509449624f0e82efb7a7fda5bf9a |
| institution | DOAJ |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2020-06-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-4e26509449624f0e82efb7a7fda5bf9a2025-08-20T03:14:40ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862020-06-01172(SI)10.21123/bsj.2020.17.2(SI).0701Anomaly Detection Approach Based on Deep Neural Network and DropoutZaid Hussien et al.0Al-nahrain universityRegarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss function to enforce the proposed model in multiple classification, including five labels, one is normal and four others are attacks (Dos, R2L, U2L and Probe). Accuracy metric was used to evaluate the model performance. The proposed model accuracy achieved to 99.45%. Commonly the recognition time is reduced in the NIDS by using feature selection technique. The proposed DNN classifier implemented with feature selection algorithm, and obtained on accuracy reached to 99.27%.http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4000Deep Learning, Dropout, Feature Selection, Network Security, NIDS |
| spellingShingle | Zaid Hussien et al. Anomaly Detection Approach Based on Deep Neural Network and Dropout مجلة بغداد للعلوم Deep Learning, Dropout, Feature Selection, Network Security, NIDS |
| title | Anomaly Detection Approach Based on Deep Neural Network and Dropout |
| title_full | Anomaly Detection Approach Based on Deep Neural Network and Dropout |
| title_fullStr | Anomaly Detection Approach Based on Deep Neural Network and Dropout |
| title_full_unstemmed | Anomaly Detection Approach Based on Deep Neural Network and Dropout |
| title_short | Anomaly Detection Approach Based on Deep Neural Network and Dropout |
| title_sort | anomaly detection approach based on deep neural network and dropout |
| topic | Deep Learning, Dropout, Feature Selection, Network Security, NIDS |
| url | http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4000 |
| work_keys_str_mv | AT zaidhussienetal anomalydetectionapproachbasedondeepneuralnetworkanddropout |