SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network
The ability of cybercriminals tohide their intentionto attack obstructs existingprotectionsystems causing the system to be unable to prevent any possible sabotage in network systems. In this paper, we propose a Similarity approach for Attack Intention Recognition using Fuzzy Min-Max Neural Network (...
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| Format: | Article |
| Language: | English |
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Elsevier
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/23820/1/SAIRF%20A%20similarity%20approach%20for%20attack%20intention%20recognition.pdf |
| _version_ | 1825812510982275072 |
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| author | Ahmed, Abdulghani Ali Mohammed, Mohammed Falah |
| author_facet | Ahmed, Abdulghani Ali Mohammed, Mohammed Falah |
| author_sort | Ahmed, Abdulghani Ali |
| collection | UMP |
| description | The ability of cybercriminals tohide their intentionto attack obstructs existingprotectionsystems causing the system to be unable to prevent any possible sabotage in network systems. In this paper, we propose a Similarity approach for Attack Intention Recognition using Fuzzy Min-Max Neural Network (SAIRF). In particular, the proposed SAIRF approach aims to recognize attack intention in real time. This approach classifies attacks according to their characteristics and uses similar metric method to identify motives of attacks and predict their intentions. In this study, network attack intentions are categorized into specific and general intentions. General intentions are recognized by investigating violations against the security metrics of confidentiality, integrity, availability, and authenticity. Specific intentions are recognized by investigating the network attacks used to achieve a violation. The obtained results demonstrate the
capability of the proposed approach to investigate similarity of network attack evidence and recognize the intentions of the attack being investigated |
| first_indexed | 2024-03-06T12:30:15Z |
| format | Article |
| id | UMPir23820 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:30:15Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | UMPir238202019-01-17T01:21:53Z http://umpir.ump.edu.my/id/eprint/23820/ SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network Ahmed, Abdulghani Ali Mohammed, Mohammed Falah QA75 Electronic computers. Computer science T Technology (General) The ability of cybercriminals tohide their intentionto attack obstructs existingprotectionsystems causing the system to be unable to prevent any possible sabotage in network systems. In this paper, we propose a Similarity approach for Attack Intention Recognition using Fuzzy Min-Max Neural Network (SAIRF). In particular, the proposed SAIRF approach aims to recognize attack intention in real time. This approach classifies attacks according to their characteristics and uses similar metric method to identify motives of attacks and predict their intentions. In this study, network attack intentions are categorized into specific and general intentions. General intentions are recognized by investigating violations against the security metrics of confidentiality, integrity, availability, and authenticity. Specific intentions are recognized by investigating the network attacks used to achieve a violation. The obtained results demonstrate the capability of the proposed approach to investigate similarity of network attack evidence and recognize the intentions of the attack being investigated Elsevier 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23820/1/SAIRF%20A%20similarity%20approach%20for%20attack%20intention%20recognition.pdf Ahmed, Abdulghani Ali and Mohammed, Mohammed Falah (2018) SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network. Journal of Computational Science, 25. pp. 467-473. ISSN 1877-7503. (Published) http: www.elsevier.com/locate/jocs https://doi.org/10.1016/j.jocs.2017.09.007 |
| spellingShingle | QA75 Electronic computers. Computer science T Technology (General) Ahmed, Abdulghani Ali Mohammed, Mohammed Falah SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network |
| title | SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network |
| title_full | SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network |
| title_fullStr | SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network |
| title_full_unstemmed | SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network |
| title_short | SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network |
| title_sort | sairf a similarity approach for attack intention recognition using fuzzy min max neural network |
| topic | QA75 Electronic computers. Computer science T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/23820/1/SAIRF%20A%20similarity%20approach%20for%20attack%20intention%20recognition.pdf |
| work_keys_str_mv | AT ahmedabdulghaniali sairfasimilarityapproachforattackintentionrecognitionusingfuzzyminmaxneuralnetwork AT mohammedmohammedfalah sairfasimilarityapproachforattackintentionrecognitionusingfuzzyminmaxneuralnetwork |