K-means Clustering

Todays, Intrusion Detection Systems (IDS) are considered as key components of security networks. However, high false positive and false negative rates are the important problems of these systems. On the other hand, many of the existing solutions in the articles are restricted to class datasets due to the use of a specific technique, but in real applications they may have multi-variant datasets. With the impetus of the facts, this paper presents a new anomaly based intrusion detection system using J48 Decision Tree, Support Vector Classifier (SVC) and k-means clustering algorithm in order to reduce false alarm rates and enhance the system performance. J48 decision tree algorithm is used to select the best features and optimize the dataset. Also, an SVM classifier and a modified k-means clustering algorithm are used to build a profile of normal and anomalous behaviors of dataset. Simulation results on benchmark NSL-KDD, CICIDS2017 and synthetic datasets confirm that the proposed method has significant performance in comparison with previous approaches.

The Internet has become an important part of many people’s daily activities. Therefore, numerous attacks threaten Internet users. IDS is a network intrusion detection tool used to quickly identify and categorize intrusions, attacks, or security issues in network-level and host-level infrastructure. Although much research has been done to improve IDS performance, many key issues remain. IDSs need to be able to more accurately detect different types of intrusions with fewer false alarms and other challenges. In this paper, we attempt to improve the performance of IDS using Whale Optimization Algorithm (WOA). The results are compared with other algorithms. NSL-KDD dataset is used to evaluate and compare the results. K-means clustering was chosen for pre-processing after a comparison between some of the existing classifier algorithms. The proposed method has proven to be a competitive method in terms of detection rate and false alarm rate base on a comparison with some of the other existing methods.

Microfinance institutions in India provide a set of financial services to the economically weaker sections. Recently, a large number of microfinance institutions have emerged in India and they have favorable impact for poverty reduction. The impact of these institutions on entrepreneurship and society, needs to be explored in greater depth. The objective of this study is to apply machine learning techniques to explore this impact. The research uses a MIX dataset for three successive years, namely 2017, 2018, and 2019. This dataset comprises eight variables centered on gross loan portfolio. Principal Component Analysis (PCM) has been applied on the sample dataset for dimensionality reduction, resulting in two main components and each component consist of fraction from eight variables. Then, the sample dataset has been labelled with the help of clustering using K-means clustering technique. Further, classification models based on K-Nearest Neighbors (KNN) algorithm and Support Vector Machine (SVM) are applied to predict the appropriate category of entrepreneurship. The experiment result shows that the machine learning techniques have been found effective and useful tools for estimating the impact of microfinance on entrepreneurship in India.

In order to resolve the issues with Intrusion Detection Systems (IDS), a preprocessing step known as feature selection is utilized. The main objectives of this step are to enhance the accuracy of classification, improve the clustering operation on imbalance dataset and reduce the storage space required. During feature selection, a subset of pertinent and non-duplicative features is chosen from the original set. In this paper, a novel approach for feature selection in intrusion detection is introduced, leveraging an enhanced k-means clustering algorithm. The clustering operation is further improved using the combination of Gravity Search Algorithm (GSA) and Particle Swarm Optimization (PSO) techniques. Additionally, Biogeography Based Optimization (BBO) technique known for its successful performance in addressing classification problems is also employed. To evaluate the proposed approach, it is tested on the UNSW-NB15 intrusion detection dataset. Finally, a comparative analysis is conducted, and the results demonstrate the effectiveness of the proposed approach, in such a way that the value of the detection accuracy parameter in the proposed method was 99.8% and in other methods it was a maximum of 99.2%.

During the last decade, tourism has always had an ascending approach in appealing new audiences. One of the types of tourism, which is considered the most popular type of tourism in this century, is war tourism. In Iran, war tourism is organized in the form of war tourism camps and is based on the memories and stories of the eight years of holy defense. Preserving the memory and sacrifices of the past generation is one of Iran's cultural priorities, and war tourism camp is one of the most important related cultural events in the country, which transmits these concepts to the new generation. Students and professors are among the influential groups in the society's culture and are among the main audiences of tourism, knowing more about their attitudes and views towards the war tourism can contribute to increase the productivity of this event and the satisfaction of tourists. In this regard, the aim of this study is to identify and categorize the views of the academic community towards war tourism camps. The method was to use Q approach to survey experts and categorize their views. Then, with k-means clustering algorithms, students' opinions have been analyzed with a larger sample size. According to the findings of Q analysis, 3 categories of views have been identified, and according to the outputs of the clustering algorithm, 5 clusters of views have been identified, and the generalities of these cases are very similar.