Adaptive feature selection for denial of services (DoS) attack
Adaptive detection is the learning ability to detect any changes in patterns in intrusion detection systems. In this paper, we propose combining two techniques in feature selection algorithm, namely consistency subset evaluation (CSE) and DDoS characteristic features (DCF) to identify and select the...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/59480/ http://psasir.upm.edu.my/id/eprint/59480/1/Adaptive%20feature%20selection%20for%20denial%20of%20services%20%28DoS%29%20attack.pdf |
| _version_ | 1848853933411270656 |
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| author | Yusof, Ahmad Riza'ain Udzir, Nur Izura Selamat, Ali Hamdan, Hazlina Abdullah @ Selimun, Mohd Taufik |
| author_facet | Yusof, Ahmad Riza'ain Udzir, Nur Izura Selamat, Ali Hamdan, Hazlina Abdullah @ Selimun, Mohd Taufik |
| author_sort | Yusof, Ahmad Riza'ain |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Adaptive detection is the learning ability to detect any changes in patterns in intrusion detection systems. In this paper, we propose combining two techniques in feature selection algorithm, namely consistency subset evaluation (CSE) and DDoS characteristic features (DCF) to identify and select the most important and relevant features related DDoS attacks. The proposed technique is trained and tested using the NSL-KDD 2009 dataset and compared with the traditional features selection method such as Information Gain, Gain Ratio, Chi-squared and Correlated features selection (CFS). The result shows that the combined CSE with DCF model overcomes the drawback of traditional feature selection technique such as avoid over-fitting, long training time and improved efficiency of detections. The adaptive model based on this technique can reduce computational complexity to analyze the data when attack occurs. |
| first_indexed | 2025-11-15T11:01:51Z |
| format | Conference or Workshop Item |
| id | upm-59480 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:01:51Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-594802018-03-07T02:14:34Z http://psasir.upm.edu.my/id/eprint/59480/ Adaptive feature selection for denial of services (DoS) attack Yusof, Ahmad Riza'ain Udzir, Nur Izura Selamat, Ali Hamdan, Hazlina Abdullah @ Selimun, Mohd Taufik Adaptive detection is the learning ability to detect any changes in patterns in intrusion detection systems. In this paper, we propose combining two techniques in feature selection algorithm, namely consistency subset evaluation (CSE) and DDoS characteristic features (DCF) to identify and select the most important and relevant features related DDoS attacks. The proposed technique is trained and tested using the NSL-KDD 2009 dataset and compared with the traditional features selection method such as Information Gain, Gain Ratio, Chi-squared and Correlated features selection (CFS). The result shows that the combined CSE with DCF model overcomes the drawback of traditional feature selection technique such as avoid over-fitting, long training time and improved efficiency of detections. The adaptive model based on this technique can reduce computational complexity to analyze the data when attack occurs. IEEE 2017 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/59480/1/Adaptive%20feature%20selection%20for%20denial%20of%20services%20%28DoS%29%20attack.pdf Yusof, Ahmad Riza'ain and Udzir, Nur Izura and Selamat, Ali and Hamdan, Hazlina and Abdullah @ Selimun, Mohd Taufik (2017) Adaptive feature selection for denial of services (DoS) attack. In: 2017 IEEE Conference on Application, Information and Network Security (AINS), 13-14 Nov. 2017, Miri Marriott Resort & Spa, Miri, Sarawak. (pp. 81-84). 10.1109/AINS.2017.8270429 |
| spellingShingle | Yusof, Ahmad Riza'ain Udzir, Nur Izura Selamat, Ali Hamdan, Hazlina Abdullah @ Selimun, Mohd Taufik Adaptive feature selection for denial of services (DoS) attack |
| title | Adaptive feature selection for denial of services (DoS) attack |
| title_full | Adaptive feature selection for denial of services (DoS) attack |
| title_fullStr | Adaptive feature selection for denial of services (DoS) attack |
| title_full_unstemmed | Adaptive feature selection for denial of services (DoS) attack |
| title_short | Adaptive feature selection for denial of services (DoS) attack |
| title_sort | adaptive feature selection for denial of services (dos) attack |
| url | http://psasir.upm.edu.my/id/eprint/59480/ http://psasir.upm.edu.my/id/eprint/59480/ http://psasir.upm.edu.my/id/eprint/59480/1/Adaptive%20feature%20selection%20for%20denial%20of%20services%20%28DoS%29%20attack.pdf |