Over the years, malware (malicious software) has become a major challenge for computer users, organizations, and even countries. In particular, a compromise of a set of inflamed hosts (aka zombies or bots) is one of the severe threats to Internet security. Botnet is described as some computer systems or devices controlled on the Internet to carry out unintentional and malicious acts without the owner's permission. Due to the continuously progressing behavior of botnets, the conventional methods fail to identify botnets. In other to solve the stated problem, this paper presents a smart system for detecting behavioural bootnet attacks using Random Forest Classifier and Principal Component Analysis (PCA). The system starts with a botnet dataset that was used in building a robust model in detecting Bootnet attacks. The dataset was pre-processed using pandas library for data cleaning. PCA was used in reducing the dimension of the dataset, so as to avoid data imbalance. The result of the PCA was used as input to the random forest classifier. The random forest classifier was trained using the number of estimators as 1000. The result of the model shows a promising accuracy of about 99%.
Building area is a vital consumer of all globally produced energy. Structures of buildings absorb about 40 % of the total energy created which transcription about 30 % of the integral worldwide CO2 radiations. As such, reducing the measure of energy absorbed by the building area would incredibly help the much-crucial depletions in world energy utilization and the related ecological concerns. This paper presents a smart system for thermal comfort prediction on residential buildings using data driven model with Random Forest Classifier. The system starts by acquiring a global thermal comfort data, pre-processed the acquired data, by removing missing values and duplicated values, and also reduced the numbers of features in the dataset by selecting just twelve columns out of 70 columns in total. This process is called feature extraction. After the pre-processing and feature extraction, the dataset was split into a training and testing set. The training set was 70% while the testing set was 30% of the original dataset. The training data was used in training our thermal comfort model with Random Forest Classifier. After training, Random Forest Classifier had an accuracy of 99.99% which is about 100% approximately. We then save our model and deployed to web through python flask, so that users can use it in predicting real time thermal comfort in their various residential buildings.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.