No abstract

No abstract

Information management collects data from several online systems. They analyze the information. They issue reports about information for supporting decision-making management. Utilizing current modern innovations try to controlling many obstacles such as, high cost, high battery power, and speed system, safety System without building a full system to solve all these problems together, we created a new internet of things ( IoT) system that provides attention to safety, and Security with low cost, low battery power, and high-speed System. As for the information management system. This paper aims at developing an active system for managing most of the smart farm and home obstacles, such issues to deal with the security system for the farm's and house and animal hanger, raining, irrigation and watering system, food supplement system, Also, a network was established to connect all those systems. Connected database storage was used, infra-red, The system is used for monitoring. They send all the collected information back to be maintained. Arduino will be used for programming this system

No abstract

The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers.