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This article gives the framework of extensive experimentation of various machine learning models to detect distributed denial of service attacks (DDoS). We use six-tier feature ranking methods that use statistical techniques as well as machine learning based classifiers to obtain the significant features. The measurable statistical based feature selection involves Chi-Square (Chi2), information gain (IG), merged Chi-Square (Chi2)-IG ranking and machine learning classifiers involve ensemble classifiers, that is, decision tree, random forest and eXtreme gradient boosting (XGBoost). Different supervised machine learning models (logistic regression, decision tree classifier, linear support vector machine, k-nearest neighbors, Gaussian Naive Bayes, random forest classifier, XGBoost) are trained on a feature-engineered datasets. To further our research, we use neural networks (ANN and CNN) using both feature-selected and auto-feature selection training setup. We check the validation and adaptability of these models with the optimal tuning of various parameters using GridSearchCV and the effectiveness of random sampling in overcoming the class imbalance problem. Based on various feature selection methods, the models are evaluated for their best performance. The experimental results show that our trained machine learning models and neural networks outperformed the ones in the state of art. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate, sensitivity, specificity, false-positive rate, F1 score, area under curve analysis and loss functions on well-known KDD Cup 99 and UNSW-NB15 datasets. This study is significant for furthering the research in DDoS detection with machine learning and deep neural networks.

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Cotton is an important cash crop, white gold and mainstay in the economy of Pakistan. Sucking insect pests of cotton especially Bemesia tabaci is the major threat. The present study was planned to evaluate the toxic effects of three synthetic insecticides i.e. Acetamiprid 20 SP, Buprofezin 25WP and Endosulfon 35EC against Bemisia tabaci against cotton white fly under optimum laboratory circumstances. All the tested insecticides were applied at dose rates of 50, 100, 200, 400 and 800 ppm. Each treatment and control were replicated thrice. Findings of the bioassays displayed that Buprofezin 25WP exhibited high mortality (82.13%) of white fly after exposure of 24 hours, among the three tested insecticides. Whilst Endosulfon 35EC and Acetamiprid 20 SP exhibited relatively low mortality (77.46 and 69.52%, respectively). LC50 values reflected that Endosulfon 35EC showed the highest toxicity against Bemisia tabaci with LC50 of 241.76 ppm followed by Buprofezin 25WP and Acetamiprid 20 SP with LC50s of 340.50 ppm and 395.12 ppm, respectively. Overall results disclosed that all three tested insecticides showed enhanced control under controlled experimental conditions. Hence, Buprofezin and Endosulfon can be used in integrated pest management (IPM) of B. tabaci.