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 (...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/23820/ http://umpir.ump.edu.my/id/eprint/23820/ http://umpir.ump.edu.my/id/eprint/23820/ http://umpir.ump.edu.my/id/eprint/23820/1/SAIRF%20A%20similarity%20approach%20for%20attack%20intention%20recognition.pdf |
| Summary: | 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 |
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