Jamilu Waziri - Academia.edu (original) (raw)
Papers by Jamilu Waziri
International Journal of Entertainment Technology and Management
International Journal of Multidisciplinary Research and Growth Evaluation
The aim of this research is to develop an enhanced reference monitor algorithm for Software Defin... more The aim of this research is to develop an enhanced reference monitor algorithm for Software Defined Networking (SDN) controller for 5G security. It is anticipated that by 2025, the network infrastructure should be able to provide connectivity for almost everything. This is expected to bring over 50 billion connections which cannot be handled by the current 4G. While 4G networks main focus is ubiquitous mobile broadband, 5G technology characteristics will have to increase immensely. The flexibility provided by software is key to meeting the unforeseen future service requirement. In this regard, Software Defined Networking (SDN) has recently gathered momentum in the networking industry and specific standard is yet to be adopted on how to check security challenges on SDN for 5G. This work proposes to adopt 5-ENSURE framework of integrating Reference Monitor (RM) to SDN controllers in order to impose access control policy. The Study will also isolate and handle malicious packets in a di...
International Journal Of Science for Global Sustainability, 2020
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.
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...
Journal of Theoretical and Applied Information Technology , 2019
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 Extreme Gradient Boosted Tree (XGBOOST) algorithm. Experimental results demonstrated that XGBOOST-based phishing detection model is promising by returning an accuracy of 97.27% which outperformed both probabilistic Neural Network (PNN) and Random forest (RF) that returned accuracies of 96.79% and 95.66% respectively. Keyword: Machine Learning, Feature Selection, Classification, XGBOOST, Phishing.
International Journal of Entertainment Technology and Management
International Journal of Multidisciplinary Research and Growth Evaluation
The aim of this research is to develop an enhanced reference monitor algorithm for Software Defin... more The aim of this research is to develop an enhanced reference monitor algorithm for Software Defined Networking (SDN) controller for 5G security. It is anticipated that by 2025, the network infrastructure should be able to provide connectivity for almost everything. This is expected to bring over 50 billion connections which cannot be handled by the current 4G. While 4G networks main focus is ubiquitous mobile broadband, 5G technology characteristics will have to increase immensely. The flexibility provided by software is key to meeting the unforeseen future service requirement. In this regard, Software Defined Networking (SDN) has recently gathered momentum in the networking industry and specific standard is yet to be adopted on how to check security challenges on SDN for 5G. This work proposes to adopt 5-ENSURE framework of integrating Reference Monitor (RM) to SDN controllers in order to impose access control policy. The Study will also isolate and handle malicious packets in a di...
International Journal Of Science for Global Sustainability, 2020
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.
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...
Journal of Theoretical and Applied Information Technology , 2019
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 Extreme Gradient Boosted Tree (XGBOOST) algorithm. Experimental results demonstrated that XGBOOST-based phishing detection model is promising by returning an accuracy of 97.27% which outperformed both probabilistic Neural Network (PNN) and Random forest (RF) that returned accuracies of 96.79% and 95.66% respectively. Keyword: Machine Learning, Feature Selection, Classification, XGBOOST, Phishing.