The purpose of this study is to develop a decision tree model that can predict student’s performa... more The purpose of this study is to develop a decision tree model that can predict student’s performance based on the mechanisms of metacognitive scaffolding prompted by the instructor in Facebook discussion. Prior to the development of the decision tree model, the study identified the pattern of dominant mechanism of metacognitive scaffolding (MS) prompted by the instructor in Facebook discussion. Additionally, students’ academic performance was also investigated. 37 postgraduate’s students from the Authoring System course was participated in a pre-experimental, one group pre and post-test research design. The data were mined using WEKA software and calculated based on the frequency of metacognitive scaffolding posted by the instructor in online learning setting which is Facebook group discussion and also students’ scores in the performance test. The decision tree model predicts that students who achieved grade A in their study were prone to receive a combination of guidance that focus...
Phishers and other cybercriminals are making the cyberspace unsafe by posing serious risks to use... more Phishers and other cybercriminals are making the cyberspace unsafe by posing serious risks to users and businesses as well as threating global security and economy. Nowadays, phishers are constantly evolving the techniques using luring user to revealing their sensitive information. Many techniques have been proposed in past for phishing detection, but due to static nature of some of the current and challenging nature of the problem, the quest for better solution is still on. In this paper, we developed phishing website model using XGBOOST algorithm to investigate the effect of dataset size using publicly available dataset composed of phishing and benign websites as in [1]. Experimental results demonstrated that as the number of instances of the dataset increases, the XGBOOST performance improve simultaneously, which shows that the XGBOOST has the highest performance than PNN algorithm.
The increase use of cloud computing infrastructure has led to a security consciousness by users a... more The increase use of cloud computing infrastructure has led to a security consciousness by users and cloud service providers to protect data available in the cloud from illegal users. Even though, different techniques have been proposed by different researchers, yet, more techniques are still proposed all in the quest to achieve better and flexible security techniques that will utilize less resource and minimize cost, because user pay as they go in the cloud. In this paper we proposed an improved multi-channel security technique in the cloud using image steganography and RSA algorithm. The proposed method was used to conceal the existence of data communication using the image steganography and protect the concealed data using RSA encryption algorithm in order to prevent illegal access. Different data set such as text, image, audio and video were used to evaluate the system. It is shown in the analysis that text data utilizes less processing time, memory storage and processing power f...
As most of human activities are being moved to cyberspace, phishers and other cybercriminals are ... more As most of human activities are being moved to cyberspace, phishers and other cybercriminals are making the cyberspace unsafe by causing serious risks to users and businesses as well as threatening global security and economy. Nowadays, phishers are constantly evolving new methods for luring user to reveal their sensitive information. To avoid falling victim to cybercriminals, a phishing detection algorithms is very necessary to be developed. Machine learning or data mining algorithms are used for phishing detection such as classification that categorized cyber users in to either malicious or safe users or regression that predicts the chance of being attacked by some cybercriminals in a given period of time. Many techniques have been proposed in the past for phishing detection but due to dynamic nature of some of the many phishing strategies employed by the cybercriminals, the quest for better solution is still on. In this paper, we propose a new phishing detection model based on Ex...
International Journal of Artificial Intelligence & Applications, 2019
Reducing the risk pose by phishers and other cybercriminals in the cyber space requires a robust ... more Reducing the risk pose by phishers and other cybercriminals in the cyber space requires a robust and automatic means of detecting phishing websites, since the culprits are constantly coming up with new techniques of achieving their goals almost on daily basis. Phishers are constantly evolving the methods they used for luring user to revealing their sensitive information. Many methods have been proposed in past for phishing detection. But the quest for better solution is still on. This research covers the development of phishing website model based on different algorithms with different set of features in order to investigate the most significant features in the dataset.
Boosting the accuracy of phishing detection with less features using XGBOOST
Phishing has been for a long time a difficult threat in every society as it changes form with tim... more Phishing has been for a long time a difficult threat in every society as it changes form with time and it has taken billions of dollars from governments, companies and individuals alike. It is an identity theft which employs a kind of social engineering attack to get vital information from individuals or group of individuals. In this paper we focus on studying various features employed in different phishing attacks. So many studies have been conducted on single feature to have high accuracy for attack detection while others advanced on the use of many features to detect different attack behaviors with high accuracy. Researchers have advanced the study to the adoption and standardization of thirty (30) features to be examined in phishing attack in order to achieve high accuracy of detection. We examined all the features used so far and used XGBOOST classification model to categories the features into different kinds to detect important features. The analysis revealed that some features hampers on the accuracy and are unfruitful which also contributes in slowing the whole detection process. The model helps us to select useful features and weeds out the useless features. This yields higher accuracy and less time in detection process
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Papers by Hajara Musa