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Ravi, Chandrasekar
- An Improved Method for Tracing IP Packet's Source
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Authors
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1 School of Information Technology and Engineering,VIT University, Vellore, Tamil Nadu – 632 014, IN
1 School of Information Technology and Engineering,VIT University, Vellore, Tamil Nadu – 632 014, IN
Source
Indian Journal of Science and Technology, Vol 7, No 5 (2014), Pagination: 577-580Abstract
IP spoofing and Denial of Service (DoS) has become the most important security threat faced by the Internet community today. One of the effective methods of defending against the types of attack is to efficiently locate its source(s). To locate the packet source, a 16-bit hybrid trace back method has been proposed. It uses both the marking and logging methods along with efficient storage method to achieve zero false positive and zero false negative rates. Furthermore, the evaluation of the proposed method on the basis of efficiency of trace back shows that it performs much better than the existing method.Keywords
Denial of Service (dos), Hash Table, IP Spoofing, IP Trace BackFull Text
- NP-FARM: Negative and Positive Fuzzy Association Rule Mining in Transaction Dataset
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Authors
Affiliations
1 School of Information Technology and Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
1 School of Information Technology and Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 7, No 11 (2014), Pagination: 1774-1779Abstract
Association Rule Mining (ARM) is a well recognized and interesting area of research in the field of Data Mining. In ARM, significant amount of research work has been reported. On the contrary, very less work has been reported for Negative Association Rule Mining (NARM). ARM concentrates only on positive rules while NARM explores negative rules. Also, most researchers have worked with discrete transaction dataset. So, in this paper, we propose a technique called Negative and Positive Fuzzy Association Rule Mining (NP-FARM), which mines both negative and positive association rules from a fuzzy transaction dataset. NP-FARM algorithm has been implemented and the experimental results determine the optimal minimum support threshold and optimal minimum confidence threshold for the given dataset. Also, the experimental results demonstrate that, as the size of the dataset increases, a negligible change in execution time is witnessed to mine the growing dataset.Keywords
Association Rule Mining. Fuzzy Transactions, Positive And Negative Item SetsFull Text