Features selection for ids in encrypted traffic using genetic algorithm
Intrusion Detection System (IDS) is one method to detect unauthorized intrusions into computer systems and networks. On the other hand, encrypted exchanges between users are widely used to ensure data security. Traditional IDSs are not able to reactive efficiently in encrypted and tunneled traffic d...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
UUM College of Arts and Sciences, Universiti Utara Malaysia
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/41307/1/41307.pdf |
| _version_ | 1825949760662536192 |
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| author | Barati, Mehdi Abdullah, Azizol Mahmod, Ramlan Mustapha, Norwati Udzir, Nur Izura |
| author_facet | Barati, Mehdi Abdullah, Azizol Mahmod, Ramlan Mustapha, Norwati Udzir, Nur Izura |
| author_sort | Barati, Mehdi |
| collection | UPM |
| description | Intrusion Detection System (IDS) is one method to detect unauthorized intrusions into computer systems and networks. On the other hand, encrypted exchanges between users are widely used to ensure data security. Traditional IDSs are not able to reactive efficiently in encrypted and tunneled traffic due to inability to analyze packet content. An encrypted malicious traffic is able to evade the detection by IDS. Feature selection for IDS is a fundamental step in detection procedure and aims to eliminate some irrelevant and unneeded features from the dataset. This paper presents a hybrid feature selection using Genetic Algorithm and Bayesian Network to improve Brute Force attack detection in Secure Shell (SSH) traffic. Brute Force attack traffic collected in a client-server model is implemented in proposed method. Our results prove that the most efficient features were selected by proposed method. |
| first_indexed | 2024-03-06T08:49:31Z |
| format | Conference or Workshop Item |
| id | upm.eprints-41307 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:49:31Z |
| publishDate | 2013 |
| publisher | UUM College of Arts and Sciences, Universiti Utara Malaysia |
| record_format | dspace |
| spelling | upm.eprints-413072015-11-03T03:26:30Z http://psasir.upm.edu.my/id/eprint/41307/ Features selection for ids in encrypted traffic using genetic algorithm Barati, Mehdi Abdullah, Azizol Mahmod, Ramlan Mustapha, Norwati Udzir, Nur Izura Intrusion Detection System (IDS) is one method to detect unauthorized intrusions into computer systems and networks. On the other hand, encrypted exchanges between users are widely used to ensure data security. Traditional IDSs are not able to reactive efficiently in encrypted and tunneled traffic due to inability to analyze packet content. An encrypted malicious traffic is able to evade the detection by IDS. Feature selection for IDS is a fundamental step in detection procedure and aims to eliminate some irrelevant and unneeded features from the dataset. This paper presents a hybrid feature selection using Genetic Algorithm and Bayesian Network to improve Brute Force attack detection in Secure Shell (SSH) traffic. Brute Force attack traffic collected in a client-server model is implemented in proposed method. Our results prove that the most efficient features were selected by proposed method. UUM College of Arts and Sciences, Universiti Utara Malaysia 2013 Conference or Workshop Item NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/41307/1/41307.pdf Barati, Mehdi and Abdullah, Azizol and Mahmod, Ramlan and Mustapha, Norwati and Udzir, Nur Izura (2013) Features selection for ids in encrypted traffic using genetic algorithm. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28-30 Aug. 2013, Sarawak, Malaysia. (pp. 279-285). http://www.icoci.cms.net.my/proceedings/2013/PDF/PID38.pdf |
| spellingShingle | Barati, Mehdi Abdullah, Azizol Mahmod, Ramlan Mustapha, Norwati Udzir, Nur Izura Features selection for ids in encrypted traffic using genetic algorithm |
| title | Features selection for ids in encrypted traffic using genetic algorithm |
| title_full | Features selection for ids in encrypted traffic using genetic algorithm |
| title_fullStr | Features selection for ids in encrypted traffic using genetic algorithm |
| title_full_unstemmed | Features selection for ids in encrypted traffic using genetic algorithm |
| title_short | Features selection for ids in encrypted traffic using genetic algorithm |
| title_sort | features selection for ids in encrypted traffic using genetic algorithm |
| url | http://psasir.upm.edu.my/id/eprint/41307/1/41307.pdf |
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