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.
The rapid increase in the use of information technology has made cyber-attacks a major concern in the use of internet by users globally. These attacks are carried out in different forms, some are carried out as phishing, man in the middle, malicious applications and so on. In this study we will focus on malware attack. Malicious applications have been a major challenge in the use of applications on windows operating system. These malicious attacks are being carried out in different forms. Some of these attacks are trojan, ransom, keylogger etc. The need to detect and classifier these malicious attacks in windows operating system is an important task. So therefore, this paper presents a smart system for detecting and classifying eight categories of malware attack on windows operating system using random forest classifier. The system starts by collecting signatures of malware attack on windows from Virus Share, Virus Sign and Github respiratory. The collected malware signatures went through the following stages of preprocessing (First stage, Second Stage, and Third Stage). The first stage has to do with creating a pandas. Dataframe using the malware signatures. The second stage has to with data cleaning and the third stage has to do with data transformation. The result of the Random Forest Classifier shows a promising performance in terms of accuracy, precision, f1-score, and recall. The result shows that the Random Forest Classifier has an accuracy of about 100% for each of the matrix evaluation. Keywords- Malware signatures, Random Forest Classifier, Windows operating System, Matrix Evaluation
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