Risk Concentration for Context Assessment (RiCCA) of SMS Messages using Danger Theory
Unified with the booming of mobile and Internet technology, spam is one of the serious issues that have been emerged tremendously. The never ending records of loss that caused by spam have initiated and motivated this study. With the purpose to produce a complementary solution for the current safegu...
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my-usim-ddms-13009 |
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uketd_dc |
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Universiti Sains Islam Malaysia |
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USIM Institutional Repository |
| language |
en_US |
| topic |
Danger Theory Spam filtering (Electronic mail) Artificial Immune Systems |
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Danger Theory Spam filtering (Electronic mail) Artificial Immune Systems Kamahazira Binti Zainal Risk Concentration for Context Assessment (RiCCA) of SMS Messages using Danger Theory |
| description |
Unified with the booming of mobile and Internet technology, spam is one of the serious issues that have been emerged tremendously. The never ending records of loss that caused by spam have initiated and motivated this study. With the purpose to produce a complementary solution for the current safeguards, this study aims to assist users in identifying potential harmful messages. The focus of the study is deploying an English text spam in Short Messages Services (SMS) format that usually consists of fraud intention in disguise. Due to the convincing but deceptive contents, users easily get enticed and their lack of awareness about implicit spam's impact loss is one of the most vulnerable factors that lead to numerous cases of fraud, scam and identity theft. This study applied one of the prominent theories from Artificial Immune Systems (AIS), Danger Theory. The behaviour of dendritic cells to sniff danger that caused by a harmful substance in the human body has become the fundamental idea of this theory. Danger Theory is employed in deciphering the risky content of span1 message via mapping its' biological properties in spam environment. Consolidation of this theory with other procedures such as risk assessment and text mining has produced a model namely as Risk Concentration for Context Assessment or RiCCA. This RiCCA prototype is developed from Danger Theory algorithms that is Dendritic Cell Algorithm (DCA) and Deterministic Dendritic Cell Algorithm (dDCA). Through a series of simulation with the deployment of merged dataset from UCI Machine Learning and self-collected spam messages, has indicated RiCCA as a reliable medium for measuring the risk concentration. Both algorithms demonstrated that Danger Theory is feasible in measuring risk of SMS content with more than 90% of accuracy rate, which dDCA outperformed DCA with distinctive result of 94.16% accuracy rate. For future work, the prototype can be further enhanced as one of the mobile application. Moreover, this study can be further applied for a larger size of message context (instead of SMS that is limited to 160 characters) and also tested in other languages. |
| format |
Thesis |
| author |
Kamahazira Binti Zainal |
| author_facet |
Kamahazira Binti Zainal |
| author_sort |
Kamahazira Binti Zainal |
| title |
Risk Concentration for Context Assessment (RiCCA) of SMS Messages using Danger Theory |
| title_short |
Risk Concentration for Context Assessment (RiCCA) of SMS Messages using Danger Theory |
| title_full |
Risk Concentration for Context Assessment (RiCCA) of SMS Messages using Danger Theory |
| title_fullStr |
Risk Concentration for Context Assessment (RiCCA) of SMS Messages using Danger Theory |
| title_full_unstemmed |
Risk Concentration for Context Assessment (RiCCA) of SMS Messages using Danger Theory |
| title_sort |
risk concentration for context assessment (ricca) of sms messages using danger theory |
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1812444737813086208 |
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my-usim-ddms-130092024-05-29T18:49:45Z Risk Concentration for Context Assessment (RiCCA) of SMS Messages using Danger Theory Kamahazira Binti Zainal Unified with the booming of mobile and Internet technology, spam is one of the serious issues that have been emerged tremendously. The never ending records of loss that caused by spam have initiated and motivated this study. With the purpose to produce a complementary solution for the current safeguards, this study aims to assist users in identifying potential harmful messages. The focus of the study is deploying an English text spam in Short Messages Services (SMS) format that usually consists of fraud intention in disguise. Due to the convincing but deceptive contents, users easily get enticed and their lack of awareness about implicit spam's impact loss is one of the most vulnerable factors that lead to numerous cases of fraud, scam and identity theft. This study applied one of the prominent theories from Artificial Immune Systems (AIS), Danger Theory. The behaviour of dendritic cells to sniff danger that caused by a harmful substance in the human body has become the fundamental idea of this theory. Danger Theory is employed in deciphering the risky content of span1 message via mapping its' biological properties in spam environment. Consolidation of this theory with other procedures such as risk assessment and text mining has produced a model namely as Risk Concentration for Context Assessment or RiCCA. This RiCCA prototype is developed from Danger Theory algorithms that is Dendritic Cell Algorithm (DCA) and Deterministic Dendritic Cell Algorithm (dDCA). Through a series of simulation with the deployment of merged dataset from UCI Machine Learning and self-collected spam messages, has indicated RiCCA as a reliable medium for measuring the risk concentration. Both algorithms demonstrated that Danger Theory is feasible in measuring risk of SMS content with more than 90% of accuracy rate, which dDCA outperformed DCA with distinctive result of 94.16% accuracy rate. For future work, the prototype can be further enhanced as one of the mobile application. Moreover, this study can be further applied for a larger size of message context (instead of SMS that is limited to 160 characters) and also tested in other languages. 2018-08 Thesis en_US https://oarep.usim.edu.my/handle/123456789/13009 https://oarep.usim.edu.my/bitstreams/67576432-b1a8-446a-bcf4-fad6c1b629be/download 8a4605be74aa9ea9d79846c1fba20a33 https://oarep.usim.edu.my/bitstreams/21e6e578-0482-4aec-bd18-1a2d752a5f6e/download cef8d55defed687d13943fd8d74c466e https://oarep.usim.edu.my/bitstreams/f1614aea-7a35-4b2d-b92f-5b9e52b50bef/download 50a9605e5d6ed73e9088b4a6e4506714 https://oarep.usim.edu.my/bitstreams/d259ed80-2a15-430c-b4be-f1d6acf37031/download 1d0c223f6b6c1aca23f4abaa879afb69 https://oarep.usim.edu.my/bitstreams/b152a9d7-ca50-4be0-a91c-307742a8a9ab/download 0a21b02860b1f1867b256d9867fa284a https://oarep.usim.edu.my/bitstreams/e986b882-a832-4b69-ad97-6a712801daa8/download d522dcf838f14975bac28c5049b6764c https://oarep.usim.edu.my/bitstreams/22751bfb-93c2-4aa7-8afa-70997098f9dd/download 82f9de78dd8bfd5ac6a9cf10657f9f7b https://oarep.usim.edu.my/bitstreams/2c713c25-7a59-4ee2-b0b5-ff3c91f1c8a7/download dbae66d6ca417590be0bceac6616428f https://oarep.usim.edu.my/bitstreams/4c13ca06-953c-4b3b-a09c-6126c154bfac/download 1ab20c75bbe16c8ba98467bb192e60ff https://oarep.usim.edu.my/bitstreams/5d55389a-45c6-428d-9b2a-081220954248/download d9cb4a9a4498d8a0e2c10c4297223f3c https://oarep.usim.edu.my/bitstreams/2783af18-01b0-4e83-86da-055d7794b1f9/download 68b329da9893e34099c7d8ad5cb9c940 https://oarep.usim.edu.my/bitstreams/3be8ebd2-3aee-4da2-9b5f-589d799f4ece/download 5b269190081bde1e92f4bc607939673a https://oarep.usim.edu.my/bitstreams/0b5f80fe-6156-4f10-9db0-b2fc1831be8d/download b308b7fe5c1c2bbdc0cb686d451b84aa https://oarep.usim.edu.my/bitstreams/5f7e88e7-cc49-4bc4-957a-b0b7f4467c09/download e7e403acd248afe9eff5396f0fad9807 https://oarep.usim.edu.my/bitstreams/5d62ea1f-4c93-4d5f-b5c5-9c18ee02f4da/download 85feea28f98c2c0579d46de97c4adc99 https://oarep.usim.edu.my/bitstreams/61ca0bcd-fc15-440b-842e-e744f112afd8/download 392b92377d3166969ac79cc37a0ba5fb https://oarep.usim.edu.my/bitstreams/c91693c1-0a5e-42c1-ab8e-b5115119956d/download 79ea1886757ca90ee7643ee5d045e0fc https://oarep.usim.edu.my/bitstreams/0cf869dc-f89f-40d4-8e32-bc78b479e6df/download 212b0306580d4f0044d18f9a3edcc832 https://oarep.usim.edu.my/bitstreams/4c8ecf2e-3651-40ca-87d6-4e97fa1168c7/download 79ea1886757ca90ee7643ee5d045e0fc Danger Theory Spam filtering (Electronic mail) Artificial Immune Systems |