Pattern Analysis and Regressive Linear Measure for Botnet Detection

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets detection.The simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so on.Here,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's reliability.The F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

Growth Performance of Arsi, Borana, Harar and HF-Crossbred Bulls Finished under Similar Feeding Condition

The study was conducted to evaluate the fattening performance of Arsi, Borana, Harar and Holstein Friesian crossbred bulls finished under a similar feeding condition at the beef farm in Haramaya University. The average daily weight gain of the four breeds ranges from 0.49 to 0.71 kg. Feed conversion efficiency also ranges from 0.11 - 0.15. Simple linear regression models were used to explore the relationship between live body weight change and change in body condition score as well as seven linear body measurements for all age groups. An average change for a unite of body condition score was equivalent to 20.3, 20.61, 22.42 and 27.78 kg for Borana, Arsi, Harar and Holstein Friesian crossbred bulls respectively. Body condition score was significantly influenced by breeds. There was a significant breed by age interaction effect on the initial body condition score of the four breeds. There was a significant and positive strong association between change in body weight and body condition score. There was a significant and strong correlation between change in body weight and change in Total topline, neck length, heart girth, flank circumference and rump length having correlation coefficients ranges from 0.57 to 0.97. A higher net profit of 7380.47 ETB per head was recorded by Borana bulls followed by Harar bulls, Arsi and Holstein Friesian crossbred with net profit of 5406.86, 5193.29 and 3384.98 ETB per head respectively. Borana bulls are more superior in weight gain and net profit. Bodyweight change could be predicted based on body condition score change during the fattening period.