Bit Stream Oriented Enumeration Tree Pruning Algorithm
Packet analysis is very important in our digital life. But what protocol analyzers can do is limited because they can only process data in determined format. This paper puts forward a solution to decode raw data in an unknown format. It is certain that data can be cut into packets because there are...
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| Published in | Shanghai jiao tong da xue xue bao Vol. 16; no. 5; pp. 567 - 570 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
Shanghai Jiaotong University Press
01.10.2011
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1007-1172 1995-8188 |
| DOI | 10.1007/s12204-011-1190-8 |
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| Abstract | Packet analysis is very important in our digital life. But what protocol analyzers can do is limited because they can only process data in determined format. This paper puts forward a solution to decode raw data in an unknown format. It is certain that data can be cut into packets because there are usually characteristic bit sequences in packet headers. The key to solve the problem is how to find out those characteristic sequences. We present an efficient way of bit sequence enumeration. Both Aho-Corasick （AC） algorithm and data mining method are used to reduce the cost of the process. |
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| AbstractList | Packet analysis is very important in our digital life. But what protocol analyzers can do is limited because they can only process data in determined format. This paper puts forward a solution to decode raw data in an unknown format. It is certain that data can be cut into packets because there are usually characteristic bit sequences in packet headers. The key to solve the problem is how to find out those characteristic sequences. We present an efficient way of bit sequence enumeration. Both Aho-Corasick （AC） algorithm and data mining method are used to reduce the cost of the process. Packet analysis is very important in our digital life. But what protocol analyzers can do is limited because they can only process data in determined format. This paper puts forward a solution to decode raw data in an unknown format. It is certain that data can be cut into packets because there are usually characteristic bit sequences in packet headers. The key to solve the problem is how to find out those characteristic sequences. We present an efficient way of bit sequence enumeration. Both Aho-Corasick (AC) algorithm and data mining method are used to reduce the cost of the process. |
| Author | 邱卫东 金凌 杨小牛 杨红娃 |
| AuthorAffiliation | School of Information Security Engineering, Shanghai Jiaotong University;National Science and Technology on Communication Information Security Control Laboratory, No. 36 Institute of China Electronics Technology Group Corporation |
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| Cites_doi | 10.1137/0206024 10.1023/B:DAMI.0000005258.31418.83 10.1145/170035.170072 10.1002/spe.4380211105 10.1145/360825.360855 |
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| Keywords | association rule pattern matching frequent set TP 309 data mining frequent sequence |
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| Notes | Packet analysis is very important in our digital life. But what protocol analyzers can do is limited because they can only process data in determined format. This paper puts forward a solution to decode raw data in an unknown format. It is certain that data can be cut into packets because there are usually characteristic bit sequences in packet headers. The key to solve the problem is how to find out those characteristic sequences. We present an efficient way of bit sequence enumeration. Both Aho-Corasick （AC） algorithm and data mining method are used to reduce the cost of the process. pattern matching, data mining, frequent set, frequent sequence, association rule 31-1943/U ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
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| References | Agrawal, Mielinsk, Swami (CR5) 1993 Han, Pei, Yin (CR4) 2004; 8 Hume, Sunday (CR1) 1991; 21 Knuth, Pratt (CR2) 1977; 6 Aho, Corasick (CR3) 1975; 18 D. E. Knuth (1190_CR2) 1977; 6 J. Han (1190_CR4) 2004; 8 A. Hume (1190_CR1) 1991; 21 R. Agrawal (1190_CR5) 1993 A. V. Aho (1190_CR3) 1975; 18 |
| References_xml | – volume: 6 start-page: 323 issue: 2 year: 1977 end-page: 350 ident: CR2 article-title: Fast pattern matching in strings [J] publication-title: SIAM Journal on Computing doi: 10.1137/0206024 – volume: 8 start-page: 53 issue: 1 year: 2004 end-page: 87 ident: CR4 article-title: Mining frequent patterns without candidate generation [J] publication-title: Data Mining and Knowledge Discovery doi: 10.1023/B:DAMI.0000005258.31418.83 – start-page: 207 year: 1993 end-page: 216 ident: CR5 article-title: Mining association rules between sets of items in large databases [C] publication-title: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data doi: 10.1145/170035.170072 – volume: 21 start-page: 1221 issue: 11 year: 1991 end-page: 1248 ident: CR1 article-title: Fast string searching [J] publication-title: Software: Practice and Experience doi: 10.1002/spe.4380211105 – volume: 18 start-page: 333 issue: 6 year: 1975 end-page: 340 ident: CR3 article-title: Efficient string matching: An aid to bibliographic search [J] publication-title: Communications of the ACM doi: 10.1145/360825.360855 – volume: 21 start-page: 1221 issue: 11 year: 1991 ident: 1190_CR1 publication-title: Software: Practice and Experience doi: 10.1002/spe.4380211105 – volume: 8 start-page: 53 issue: 1 year: 2004 ident: 1190_CR4 publication-title: Data Mining and Knowledge Discovery doi: 10.1023/B:DAMI.0000005258.31418.83 – volume: 18 start-page: 333 issue: 6 year: 1975 ident: 1190_CR3 publication-title: Communications of the ACM doi: 10.1145/360825.360855 – volume: 6 start-page: 323 issue: 2 year: 1977 ident: 1190_CR2 publication-title: SIAM Journal on Computing doi: 10.1137/0206024 – start-page: 207 volume-title: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data year: 1993 ident: 1190_CR5 doi: 10.1145/170035.170072 |
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| SubjectTerms | Algorithms Architecture Computer Science Cost engineering Digital Electrical Engineering Engineering Enumeration Format Life Sciences Materials Science Packets (communication) Pruning Streams |
| Title | Bit Stream Oriented Enumeration Tree Pruning Algorithm |
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