Plant beneficial effect of two strains of <i>Proteus vulgaris </i>isolated from tea plantations

919-924Two strains of Proteus isolated from tea plantation soil were tested for their ability to colonise the roots of gram (Cicerarietinum), bean (Phaseolus radiatus) and mung (Phaseolus mungo) using a gnotobiotic system. Seeds bacterized with the two strains grew faster and showed significant increase in root and shoot enlargement of the plants tested. The bioactive fractions obtained from the culture filtrates and separated through HPLC showed that the plant growth promoting fractions were not always fungicidal and that the insecticidal fraction which was found only in RRLJ 16 was not plant growth promoting. These results suggest that the plant growth promotion effect of the plant beneficial bacteria may not always be due to disease suppression

An Efficient Machine Learning Based Classification Scheme for Detecting Distributed Command &amp; Control Traffic of P2P Botnets

Biggest internet security threat is the rise of Botnets having modular and flexible structures. The combined power of thousands of remotely controlled computers increases the speed and severity of attacks. In this paper, we provide a comparative analysis of machine-learning based classification of botnet command & control(C&C) traffic for proactive detection of Peer-to-Peer (P2P) botnets. We combine some of selected botnet C&C traffic flow features with that of carefully selected botnet behavioral characteristic features for better classification using machine learning algorithms. Our simulation results show that our method is very effective having very good test accuracy and very little training time. We compare the performances of Decision Tree (C4.5), Bayesian Network and Linear Support Vector Machines using performance metrics like accuracy, sensitivity, positive predictive value(PPV) and F-Measure. We also provide a comparative analysis of our predictive models using AUC (area under ROC curve). Finally, we propose a rule induction algorithm from original C4.5 algorithm of Quinlan. Our proposed algorithm produces better accuracy than the original decision tree classifier