A rough set-based effective rule generation method for classification with an application in intrusion detection

Abstract: In this paper, we use Rough Set Theory (RST) to address the important problem of generating decision rules for data mining. In particular, we propose a rough set-based approach to mine rules from inconsistent data. It computes the lower and upper approximations for each concept, and then builds concise classification rules for each concept satisfying required classification accuracy. Estimating lower and upper approximations substantially reduces the computational complexity of the algorithm. We use UCI ML Repository data sets to test and validate the approach. We also use our approach on network intrusion data sets captured using our local network from network flows. The results show that our approach produces effective and minimal rules and provides satisfactory accuracy. Keywords: rough set; LEM2; inconsistency; minimal; redundant; PCS; intrusion detection; network flow data. Reference to this paper should be made as follows: Gogoi, P., Bhattacharyya, D.K. and Kalita, J.K. (2013) 'A rough set-based effective rule generation method for classification with an application in intrusion detection', Int

Molekularna karakterizacija gena za mitohondrijsku 16S rRNA goveda, bivola i jaka.

A combination of polymerase chain reaction (PCR) with restriction fragment length polymorphism (RFLP) and nucleotide sequencing is the most preferred and efficient method for characterization of different species, in terms of detection power and applicability to large scale screening. The present study was carried out with the aim of developing the molecular fingerprint of the mitochondrial 16S rRNA gene of Cattle, Buffalo and Yak. Blood samples were collected randomly from ten different animals of each species for mitochondrial DNA extraction. The extracted DNA was used for the amplification of the 16S rRNA gene using universal primers. The size of the amplified products was 600bp. RFLP studies were carried out by digesting the amplicons using restriction enzymes viz. AluI, HinfI and HaeIII. The resulting RFLP pattern could easily identify and differentiate each of the species. Sequencing of the amplicons in all three species was carried out to confirm the variations at nucleotide level. Sequence analysis of the 16S rRNA gene using MEGA4 software and also PCRRFLP revealed that the 16S rRNA gene can be used as a good candidate for a molecular marker.Kombinacija lančane reakcije polimerazom (PCR) s polimorfizmom duljine restrikcijskih fragmenata (RFLP) i utvrđivanjem slijeda nukleotida (sekvenciranje) najpoželjniji je i učinkovit postupak za otkrivanje širokog raspona varijacija pri karakterizaciji različitih vrsta. Cilj ovog istraživanja bio je razviti molekularni otisak za gen koji određuje mitohondrijske 16S rRNA goveda, bivola i jaka. Za izdvajanje mitohondrijske DNA nasumično je prikupljeno po 10 uzoraka krvi različitih životinja unutar svake vrste. Izdvojena DNA iskorištena je za umnažanje 16S rRNA gena uz uporabu univerzalnih početnica. Veličina umnoženih proizvoda iznosila je 600 bp. RFLP analize provedene su digestijom amplikona pomoću restrikcijskih enzima viz. AluI, HinfI i HaeIII. Dobiveni RFLP uzorak mogao je lako prepoznati i razlikovati svaku od vrsta. Sekvenciranje amplikona u sve tri vrste provedeno je s ciljem da se potvrde varijacija na razini nukleotida. Analiza sekvencija pomoću računalnog programa MEGA4 i metoda PCR-RFLP pokazala je da se gen 16S rRNA može upotrijebiti kao dobar kandidat za molekularni biljeg