Communication
Mohammad Abd Abbas; Bilal Ghazal Ghazal; Ahmad ghandour Ghandour
Abstract
Online payment methods for e-commerce and many websites in various fields have increased significantly. Therefore, credit card frauds are easy targets, and their rate is on the rise which poses a major problem for online payments. The basic concept is to examine consumers' purchasing histories to extrapolate ...
Read More ...
Online payment methods for e-commerce and many websites in various fields have increased significantly. Therefore, credit card frauds are easy targets, and their rate is on the rise which poses a major problem for online payments. The basic concept is to examine consumers' purchasing histories to extrapolate their typical behavior patterns, classify cardholders into different groups, and then attempt to detect credit card fraud. Credit card fraud detection based on a machine learning model uses a combination of supervised and unsupervised learning techniques such as Random Forest, Decision Tree, Logistic Regression, and Extreme Gradient Boost. We used the synthetic minority oversampling (SMOTE) technique to balance the dataset. The model is trained on a large set of data related to credit card transactions and uses features such as transaction amount, transaction location, and time of day to identify patterns and anomalies in the data that indicate fraudulent activity. Our goal is to build a model based on machine learning technology that detects and analyzes online shopping fraud Detecting fraud in credit card systems is crucial to protecting consumers from financial losses and maintaining the integrity of the financial ecosystem after collecting Creditcard.csv data. With the help of several algorithms, including Random Forest (RF) algorithm accuracy reached 99%, Logistic Regression (LR) algorithm accuracy reached 97%, and Decision Tree (DT) algorithm accuracy reached 99% Researchers provide a comprehensive method for identifying fraud in credit card transactions Precision Recall F1-score. The proposed system includes four main steps: pre-processing, classification using the algorithm, and checking whether the transaction is fraudulent or not.
Computer
Adil Yousef Hussein; Ahmed T. Sadiq
Abstract
Hackerscanconductmoredestructivecyber-attacksthankstotherapidspread of Internet of Things (IoT) devices, posing significant security risks for users. Through a malicious process, the attacker intended to exhaust the capital of the target IoT network. Researchers and company owners are concerned about ...
Read More ...
Hackerscanconductmoredestructivecyber-attacksthankstotherapidspread of Internet of Things (IoT) devices, posing significant security risks for users. Through a malicious process, the attacker intended to exhaust the capital of the target IoT network. Researchers and company owners are concerned about the reliability of IoT networks, which is taken into account because it has a significant impact on the delivery of facilities provided by IoT systems and the security of user groups. The intrusion prevention system ensures that the network is protected by detecting malicious activity. In this paper, the focus is on predicting attacks and distinguishing between normal network use and network exploitation for intrusion and network attack and we will use Swarm Intelligence (SI) which is one of the types of artificial intelligence (AI) that we harness to choose features to determine the task of them and specifically we will use an algorithm Meerkat Clan (MCA) for this purpose, as this research suggested a modified IDS in machine learning (ML) based IoT environments to identify features and these features will be input into Random Forest algorithm. The IoTID20 dataset is used where nominal traits are removed, so the final dataset contains 79 traits. The data set contains three categories: the label that identifies whether it is a natural use or exploitation, the category that characterizes the type of exploitation, and the subcategory that describes that exploitation more accurately. The number of trees in a random forest (RF) classifier for binary, class, and subclass will be determined by the experiment. The trained classifier is then tested and the approach achieves 100% accuracy for binary target prediction, 96.5% for category and accuracy ranges of 83.7% for sub-category target prediction. The proposed system is evaluated and compared with previous systems and its performance is shown through the use of confusion matrix and others.
Suha M. Najem; Suhad kadhem
Abstract
With the speedy expansion of e-commerce, credit cards have also become rising vogue, and that makes online transactions sleek and suitable. In conjunction with rising in online transactions, credit card fraud also increasing, which contributes to losses incurred yearly. As a result, many deep and machine ...
Read More ...
With the speedy expansion of e-commerce, credit cards have also become rising vogue, and that makes online transactions sleek and suitable. In conjunction with rising in online transactions, credit card fraud also increasing, which contributes to losses incurred yearly. As a result, many deep and machine learning methods are produced to fix such as problems like Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and other algorithms, but the current models are still not accurate. Moreover, sometimes the used datasets still need further preprocessing, since that has been approved the important role of feature engineering in performance optimization. In this paper, effective feature engineering and feature selection methods have been produced for preprocessing the raw dataset, which was transformed with Exploratory Data Analysis (EDA). Then LightGBM, XGboost, and Random forest classifiers are used for fraud detection. Experiments show that the LightGBM and XGboost models achieved the best accuracy with 100% after applying further preprocessing on the dataset.
Zainab Ali Mohammed; Mohammed Najm Abdullah2; Imad Husain Al Hussaini
Volume 21, Issue 1 , March 2021, , Page 1-15
Abstract
Traffic incidents dont only cause various levels of traffic congestion but often contribute to traffic accidents and secondary accidents, resulting in substantial loss of life, economy, and productivity loss in terms of injuries and deaths, increased travel times and delays, and excessive consumption ...
Read More ...
Traffic incidents dont only cause various levels of traffic congestion but often contribute to traffic accidents and secondary accidents, resulting in substantial loss of life, economy, and productivity loss in terms of injuries and deaths, increased travel times and delays, and excessive consumption of energy and air pollution. Therefore, it is essential to accurately estimate the duration of the incident to mitigate these effects. Traffic management center incident logs and traffic sensors data from Eastbound Interstate 70 (I-70) in Missouri, United States collected during the period from January 2015 to January 2017, with a total of 352 incident records were used to develop incident duration estimation models. This paper investigated different machine learning (ML) methods for traffic incidents duration prediction. The attempted ML techniques include Support Vector Machine (SVM), Random Forest (RF), and Neural Network Multi-Layer Perceptron (MLP). Root mean squared error (RMSE) and Mean absolute error (MAE) were used to evaluate the performance of these models. The results showed that the performance of the models was comparable with SVM models slightly outperforms the RF, and MLP models in terms of MAE index, where MAE was 14.23 min for the best-performing SVM models. Whereas, in terms of the RMSE index, RF models slightly outperformed the other two models given RMSE of 18.91 min for the best-performing RF model.
