Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

To protect the network, resources, and sensitive data, the intrusion detection system (IDS) has become a fundamental component of organizations that prevents cybercriminal activities. Several approaches have been introduced and implemented to thwart malicious activities so far. Due to the effectiveness of machine learning (ML) methods, the proposed approach applied several ML models for the intrusion detection system. In order to evaluate the performance of models, UNSW-NB 15 and Network TON_IoT datasets were used for offline analysis. Both datasets are comparatively newer than the NSL-KDD dataset to represent modern-day attacks. However, the performance analysis was carried out by training and testing the Decision Tree (DT), Gradient Boosting Tree (GBT), Multilayer Perceptron (MLP), AdaBoost, Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the binary classification task. As the performance of IDS deteriorates with a high dimensional feature vector, an optimum set of features was selected through a Gini Impurity-based Weighted Random Forest (GIWRF) model as the embedded feature selection technique. This technique employed Gini impurity as the splitting criterion of trees and adjusted the weights for two different classes of the imbalanced data to make the learning algorithm understand the class distribution. Based upon the importance score, 20 features were selected from UNSW-NB 15 and 10 features from the Network TON_IoT dataset. The experimental result revealed that DT performed well with the feature selection technique than other trained models of this experiment. Moreover, the proposed GIWRF-DT outperformed other existing methods surveyed in the literature in terms of the F1 score.

Design of Context Cluster and Load Balanced Video Recommendation System in Cloud Computing

Nowadays, in online social networks, there is an instantaneous extension of multimedia services and there are huge offers of video contents which has hindered users to acquire their interests. To solve these problem different personalized recommendation systems had been suggested. Although, all the personalized recommendation system which have been suggested are not efficient and they have significantly retarded the video recommendation process. So to solve this difficulty, context extractor based video recommendation system on cloud has been proposed in this paper. Further to this the system has server selection technique to handle the overload program and make it balanced. This paper explains the mechanism used to minimize network overhead and recommendation process is done by considering the context details of the users, it also uses rule based process and different algorithms used to achieve the objective. The videos will be stored in the cloud and through application videos will be dumped into cloud storage by reading, coping and storing process.

Study Determination About Receivers of Support for Rehabilitation "The Houses Unhabitable" in Slum Area, Indonesia

Indonesia, a developing country, has launched a program with a target of 0% slum in urban areas by 2020. It has been more than ten years since this program for handling urban slums has made many improvements. One aspect of the parameter that becomes an improvement in handling urban slum areas is the improvement of uninhabitable houses. This study investigates how the quality of urban slum settlements can be improved in the aspect of houses that are not habitable, especially in terms of determining the recipient of rehabilitation assistance for houses that are unhabitable in urban areas. This study used a qualitative methodology and analysis of the literature review in 21 scientific papers on selecting houses that are not habitable assistance and related regulations. The result is that there are two processes in determining Receivers of Support for Rehabilitation "The Houses Unhabitable", first through the aspects of the criteria for houses unhabitable which are technical in the field, and selection techniques that are more academic in nature. Aspects of the criteria for houses unhabitable provide an assessment of the area and buildings. whereas the selection technique requires a selection technique capable of managing data that has many criteria.

The Effect of Leadership Style on Employee Retention at Ayodya Resort Bali with Non-Physical Work Environment as Moderating Variable

This study aims to analyze the effect of leadership style, work environment on employee retention with non-physical work environment as a moderating variable. The research design used is associative. The research was conducted at Ayodya Resort Bali. The population of this study was 501 employees with 84 employees as samples. The sample selection technique is proportional stratified. The data collection method used is Observation, Interview, Questionnaire and analyzed by Moderated Regression Analysis (MRA). The results show that leadership style has a positive and significant effect on employee retention, the better the leadership style applied by the company, the employee's desire to remain in the company will also increase. The non-physical work environment strengthens the influence of leadership style on employee retention, the better the leadership style supported by a good non-physical work environment, the employee's desire to remain in the company will increase. Companies must always pay attention to the relationship between employees and the relationship between employees and superiors so that they continue to run well and harmoniously.

A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

Indications of people’s environmental concern are linked to transport decisions and can provide great support for policymaking on climate change. This study aims to better predict individual climate change stage of change (CC-SoC) based on different features of transport-related behavior, General Ecological Behavior, New Environmental Paradigm, and socio-demographic characteristics. Together these sources result in over 100 possible features that indicate someone’s level of environmental concern. Such a large number of features may create several analytical problems, such as overfitting, accuracy reduction, and high computational costs. To this end, a new feature selection technique, named the Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-QDA), is first proposed to find the optimal features to predict CC-SoC with the highest accuracy. Different conventional feature selection methods (Lasso, Elastic Net, Random Forest Feature Selection, Extra Trees, and Principal Component Analysis Feature Selection) are employed to compare with the COA-QDA. Afterward, eight classification techniques are applied to solve the prediction problem. Finally, a sensitivity analysis is performed to determine the most important features affecting the prediction of CC-SoC. The results indicate that COA-QDA outperforms conventional feature selection methods by increasing average testing data accuracy from 0.7 to 5.6%. Logistic Regression surpasses other classifiers with the highest prediction accuracy.