Predictive analytics of Churn Customers Calling Details Records using Classification by Clustering (CBC) dealing with Supervised Machine Learning Algorithms

Telecom companies generate enormous amounts of data regularly. The telecom Decision makers that obtaining new customers is more challenging than sustaining existing ones. Furthermore, data from existing churn customers may be utilized to detect churn clients and their patterns of behavior. This research develops a model of churn prediction for the telecommunication business, which uses NB, SVM, DT, and RDF to detect churn clients. The proposed model churns customers' data using classification techniques, with the Random Forest (RDF) method performing well (95.94 % correctly categorized instances), the Decision Tree (DTs) providing classification accuracy (95.40 %), the Naïve Bayes (NB) provided classification accuracy (89.58 %), and the Support Vector Machine (SVMs) provided classification accuracy (71.08 %). The four different classification algorithms' predictions and observations are compared, with a percentage of 71 percent equality and 29 percent variation

Design of Automated Website Phishing Detection using Sequential Mechanism of RCL Algorithm

The phishing outbreaks in internet has become a major problem in web safety in recent years. The phishers will be stealing crucial economic data regarding the web user to perform economic break-in. In order to predict phishing websites, many blacklist-based phishing website recognition methods are used in this study. Traditional methods of detecting phishing websites rely on static features and rule-based schemes, which can be evaded by attackers. Recently, Deep Learning (DL) and Machine Learning (ML) models are employed for automated website phishing detection. With this motivation, this study develops an automated website phishing detection using the sequential mechanism of RCL algorithm. The proposed model employs Long-Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Random Forest (RF) models for the detection of attacks in the URLs and webpages by the similarity measurement of the decoy contents. The proposed model involves three major components namely, RF for URL phishing detection, CNN based phishing webpage detection, and LSTM based website classification (i.e., legitimate and phishing). The experimental result analysis of the RCL technique is tested on the benchmark dataset of Alexa and PhishTank. A comprehensive comparison study highlighted that the RCL algorithm accomplishes enhanced phishing detection performance over other existing techniques in terms of distinct evaluation metrics

Machine learning approach for identifying suspicious uniform resource locators (URLs) on Reddit social network

The applications and advantages of the Internet for real-time information sharing can never be over-emphasized. These great benefits are too numerous to mention but they are being seriously hampered and made vulnerable due to phishing that is ravaging cyberspace. This development is, undoubtedly, frustrating the efforts of the Global Cyber Alliance – an agency with a singular purpose of reducing cyber risk. Consequently, various researchers have attempted to proffer solutions to phishing. These solutions are considered inefficient and unreliable as evident in the conflicting claims by the authors. Against this backdrop, this work has attempted to find the best approach to solving the challenge of identifying suspicious uniform resource locators (URLs) on Reddit social networks. In an effort to handle this challenge, attempts have been made to address two major problems. The first is how can the suspicious URLs be identified on Reddit social networks with machine learning techniques? And the second is how can internet users be safeguarded from unreliable and fake URLs on the Reddit social network? This work adopted six machine learning algorithms – AdaBoost, Gradient Boost, Random Forest, Linear SVM, Decision Tree, and Naïve Bayes Classifier – for training using features obtained from Reddit social network and for additional processing. A total sum of 532,403 posts were analyzed. At the end of the analysis, only 87,083 posts were considered suitable for training the models. After the experimentation, the best performing algorithm was AdaBoost with an accuracy level of 95.5% and a precision of 97.57%.publishedVersio

Comparative performance of machine learning methods for classification on phishing attack detection

The development of computer networks today has increased rapidly. This can be shown based on the trend of every computer user around the world, whereby they need to connect their computer to the Internet. This indicates that the use of Internet is very important, such as for the access to social media accounts, namely Instagram, Facebook, and Twitter. However, with this extensive use, the Internet does not necessarily have the ability to maintain account security in mobile phones or computers. With a low level of security in a network system, it will be convenient for scammers to hack a victim’s computer system and retrieve all important information of the victim for their benefit There are many methods that used by scammers to get the important information where phishing attack is the simplest and famous method to be used. Therefore, this study was conducted to develop an anti-phishing method to detect the phishing attack. Machine learning method was proposed as suitable to be used in detecting phishing attacks. In this paper, several machine learning methods were studied and applied in detecting phishing attack. Experiments of the machine learning methods were conducted to investigate which method performed better. Two benchmark datasets were used in the interest to access the ability of the methods in detecting the phishing attack. Then the results were obtained to show the performance of each methods on all dataset