Ayei Ibor is a Lecturer, Author, and Cyber Security Researcher. His specialties include machine and deep learning, programming, blockchain technology, data mining and cryptography.
Journal of the Nigerian Society of Physical Sciences
This paper focuses on highlighting the problems that are associated with the absence of privacy a... more This paper focuses on highlighting the problems that are associated with the absence of privacy and security of medical records in a healthcare system. It seeks to bridge the gap between the currently used security protocols in the management of health information, and encryption algorithms that should be used. Extant health information systems have always been developed with conventional databases. With all the privileges to read, write and execute assigned to the administrator, who has centralised control over all medical records, there is the likelihood of the misuse, distortion and loss of such records in the event that the administrator becomes compromised or inadvertent system failure. To solve this problem, the use of decentralised and distributed databases becomes paramount. Blockchain technology has recently received much attention due to its ability to permit a peer-to-peer network with distributed databases that can be stored locally on each node in the network. Subsequen...
2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2021
In recent years, predicting network intrusions has been a serious research concern in the academi... more In recent years, predicting network intrusions has been a serious research concern in the academia and industry due to the expanding attack surfaces. This unending trend of threat escalation implies that more robust approaches are required to accurately predict attacks. Extant prediction models are generally affected by the choice of hyperparameters based on expert knowledge. Consequently, in this paper we present a novel approach to predict cyberattacks using bio-inspired hyperparameter search technique to generate an optimal network structure using core components of a deep neural network as chromosomes. This optimal or bio-inspired network structure is further used to derive a novel prediction model called NetBiiDenns. Furthermore, we evaluated our model on two well-known benchmark datasets, which include the CICIDS2017 and NSL-KDD datasets and achieved a prediction accuracy of 99%. From findings, our model predicts cyberattacks with high accuracy, low error and false positive rates, and significantly outperforms state-of-the-art comparative models.
Journal of the Nigerian Society of Physical Sciences
The application of machine learning algorithms to the detection of fraudulent credit card transac... more The application of machine learning algorithms to the detection of fraudulent credit card transactions is a challenging problem domain due to the high imbalance in the datasets and confidentiality of financial data. This implies that legitimate transactions make up a high majority of the datasets such that a weak model with 99% accuracy and faulty predictions may still be assessed as high-performing. To build optimal models, four techniques were used in this research to sample the datasets including the baseline train test split method, the class weighted hyperparameter approach, and the undersampling and oversampling techniques. Three machine learning algorithms were implemented for the development of the models including the Random Forest, XGBoost and TensorFlow Deep Neural Network (DNN). Our observation is that the DNN is more effcient than the other 2 algorithms in modelling the under-sampled dataset while overall, the three algorithms had a better performance in the oversamplin...
West African Journal of Industrial and Academic Research, 2015
There is a fast pace of growth in the use of Voice over Internet Protocol (VoIP) networks owing t... more There is a fast pace of growth in the use of Voice over Internet Protocol (VoIP) networks owing to the fact that more organisations are deploying IP based voice networks. This invariably has a security concern for the payload as the traffic on IP based voice networks is exposed to threats similar to those found on regular data traffic. Realising end-to-end security has been influenced by numerous exploits targeted at VoIP networks with attendant lower rate of calls than the traditional telephone system. Although the requirements for security as well as accessibility for voice traffic are dissimilar as compared to data traffic, it is equivalently significant to protect this payload from attacks such as spoofing, eavesdropping and man-in the-middle attacks. These security concerns, due to the flexibility of the VoIP system with corresponding convergence of the voice and data networks pose a plethora of threats to the confidentiality, integrity and availability of the services rendered...
The state of the cyberspace portends uncertainty for the future Internet and its accelerated numb... more The state of the cyberspace portends uncertainty for the future Internet and its accelerated number of users. New paradigms add more concerns with big data collected through device sensors divulging large amounts of information, which can be used for targeted attacks. Though a plethora of extant approaches, models and algorithms have provided the basis for cyberattack predictions, there is the need to consider new models and algorithms, which are based on data representations other than task-specific techniques. Deep learning, which is underpinned by representation learning, has found widespread relevance in computer vision, speech recognition, natural language processing, audio recognition, and drug design. However, its non-linear information processing architecture can be adapted towards learning the different data representations of network traffic to classify benign and malicious network packets. In this paper, we model cyberattack prediction as a classification problem. Further...
... ONTOLOGIES AND SEMANTIC WEB SERVICES FOR M-COMMERCE APPLICATIONS EEWilliams 1, AE IBOR 2 AND ... more ... ONTOLOGIES AND SEMANTIC WEB SERVICES FOR M-COMMERCE APPLICATIONS EEWilliams 1, AE IBOR 2 AND UG INYANG 2 1Department ... defines the machine and ports where messages should be sent [Grant, 2001] 4.0 Ontology based Design of online bookstore for ...
In many parts of the world, the military has been very busy in recent times engaging in terror an... more In many parts of the world, the military has been very busy in recent times engaging in terror and other related wars. This requires that men and materials have to be located in different parts of their strategic geographic centres. And in order to ensure a fast communication with these bases, the military often deploys Mobile Ad-hoc Networks (MANETs). MANETs carry such intelligence information as: deployment information, readiness information, and order of battle plans to their various bases. The nature of these information is such that any compromise on them could be disastrous to the courses of action of the bases. This paper identifies user authentication as a key issue in strengthening security concerns in MANETs. The paper further adopts biometrics technologies as the trending options for the purpose of obtaining a truer reflection of the identities of the users of ad-hoc networks. This paper therefore, reviews various biometrics technology implementation strategies available,...
One of the most damaging security threats on the Internet today is cyberattacks. As new paradigms... more One of the most damaging security threats on the Internet today is cyberattacks. As new paradigms emerge, new vulnerabilities and flaws are discovered on a daily basis. These vulnerabilities have been consistently exploited by malicious users to stage cyberattacks, which erode the confidentiality, integrity and availability of critical data, and other computing resources. In recent times, the research focus has been on signature based and anomaly detection approaches. However, the challenges of using known attack signatures and profiles have made the prediction of attacks an elusive and cumbersome activity. The use of task specific algorithms has also created more setbacks in cyberattack prediction, hence the need for new approaches that exploit the learning of data representations. Therefore, this paper presents a combination of Principal Component Analysis (PCA) and Expectation Maximization (EM) for intelligent clustering, and a supervised Deep Neural Network (DNN) for the trainin...
The prediction of cyberattacks has been a major concern in cybersecurity. This is due to the huge... more The prediction of cyberattacks has been a major concern in cybersecurity. This is due to the huge financial and resource losses incurred by organisations after a cyberattack. The emergence of new applications and disruptive technologies has come with new vulnerabilities, most of which are novel-with no immediate remediation available. Recent attacks signatures are becoming evasive, deploying very complex techniques and algorithms to infiltrate a network, leading to unauthorized access and modification of system parameters and classified data. Although there exists several approaches to mitigating attacks, challenges of using known attack signatures and modeled behavioural profiles of network environments still linger. Consequently, this paper discusses the use of unsupervised statistical and supervised deep learning techniques to predict attacks by mapping hyper-alerts to class labels of attacks. This enhances the processes of feature extraction and transformation, as a means of giving structured interpretation of the dynamic profiles of a network.
Journal of the Nigerian Society of Physical Sciences
This paper focuses on highlighting the problems that are associated with the absence of privacy a... more This paper focuses on highlighting the problems that are associated with the absence of privacy and security of medical records in a healthcare system. It seeks to bridge the gap between the currently used security protocols in the management of health information, and encryption algorithms that should be used. Extant health information systems have always been developed with conventional databases. With all the privileges to read, write and execute assigned to the administrator, who has centralised control over all medical records, there is the likelihood of the misuse, distortion and loss of such records in the event that the administrator becomes compromised or inadvertent system failure. To solve this problem, the use of decentralised and distributed databases becomes paramount. Blockchain technology has recently received much attention due to its ability to permit a peer-to-peer network with distributed databases that can be stored locally on each node in the network. Subsequen...
2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2021
In recent years, predicting network intrusions has been a serious research concern in the academi... more In recent years, predicting network intrusions has been a serious research concern in the academia and industry due to the expanding attack surfaces. This unending trend of threat escalation implies that more robust approaches are required to accurately predict attacks. Extant prediction models are generally affected by the choice of hyperparameters based on expert knowledge. Consequently, in this paper we present a novel approach to predict cyberattacks using bio-inspired hyperparameter search technique to generate an optimal network structure using core components of a deep neural network as chromosomes. This optimal or bio-inspired network structure is further used to derive a novel prediction model called NetBiiDenns. Furthermore, we evaluated our model on two well-known benchmark datasets, which include the CICIDS2017 and NSL-KDD datasets and achieved a prediction accuracy of 99%. From findings, our model predicts cyberattacks with high accuracy, low error and false positive rates, and significantly outperforms state-of-the-art comparative models.
Journal of the Nigerian Society of Physical Sciences
The application of machine learning algorithms to the detection of fraudulent credit card transac... more The application of machine learning algorithms to the detection of fraudulent credit card transactions is a challenging problem domain due to the high imbalance in the datasets and confidentiality of financial data. This implies that legitimate transactions make up a high majority of the datasets such that a weak model with 99% accuracy and faulty predictions may still be assessed as high-performing. To build optimal models, four techniques were used in this research to sample the datasets including the baseline train test split method, the class weighted hyperparameter approach, and the undersampling and oversampling techniques. Three machine learning algorithms were implemented for the development of the models including the Random Forest, XGBoost and TensorFlow Deep Neural Network (DNN). Our observation is that the DNN is more effcient than the other 2 algorithms in modelling the under-sampled dataset while overall, the three algorithms had a better performance in the oversamplin...
West African Journal of Industrial and Academic Research, 2015
There is a fast pace of growth in the use of Voice over Internet Protocol (VoIP) networks owing t... more There is a fast pace of growth in the use of Voice over Internet Protocol (VoIP) networks owing to the fact that more organisations are deploying IP based voice networks. This invariably has a security concern for the payload as the traffic on IP based voice networks is exposed to threats similar to those found on regular data traffic. Realising end-to-end security has been influenced by numerous exploits targeted at VoIP networks with attendant lower rate of calls than the traditional telephone system. Although the requirements for security as well as accessibility for voice traffic are dissimilar as compared to data traffic, it is equivalently significant to protect this payload from attacks such as spoofing, eavesdropping and man-in the-middle attacks. These security concerns, due to the flexibility of the VoIP system with corresponding convergence of the voice and data networks pose a plethora of threats to the confidentiality, integrity and availability of the services rendered...
The state of the cyberspace portends uncertainty for the future Internet and its accelerated numb... more The state of the cyberspace portends uncertainty for the future Internet and its accelerated number of users. New paradigms add more concerns with big data collected through device sensors divulging large amounts of information, which can be used for targeted attacks. Though a plethora of extant approaches, models and algorithms have provided the basis for cyberattack predictions, there is the need to consider new models and algorithms, which are based on data representations other than task-specific techniques. Deep learning, which is underpinned by representation learning, has found widespread relevance in computer vision, speech recognition, natural language processing, audio recognition, and drug design. However, its non-linear information processing architecture can be adapted towards learning the different data representations of network traffic to classify benign and malicious network packets. In this paper, we model cyberattack prediction as a classification problem. Further...
... ONTOLOGIES AND SEMANTIC WEB SERVICES FOR M-COMMERCE APPLICATIONS EEWilliams 1, AE IBOR 2 AND ... more ... ONTOLOGIES AND SEMANTIC WEB SERVICES FOR M-COMMERCE APPLICATIONS EEWilliams 1, AE IBOR 2 AND UG INYANG 2 1Department ... defines the machine and ports where messages should be sent [Grant, 2001] 4.0 Ontology based Design of online bookstore for ...
In many parts of the world, the military has been very busy in recent times engaging in terror an... more In many parts of the world, the military has been very busy in recent times engaging in terror and other related wars. This requires that men and materials have to be located in different parts of their strategic geographic centres. And in order to ensure a fast communication with these bases, the military often deploys Mobile Ad-hoc Networks (MANETs). MANETs carry such intelligence information as: deployment information, readiness information, and order of battle plans to their various bases. The nature of these information is such that any compromise on them could be disastrous to the courses of action of the bases. This paper identifies user authentication as a key issue in strengthening security concerns in MANETs. The paper further adopts biometrics technologies as the trending options for the purpose of obtaining a truer reflection of the identities of the users of ad-hoc networks. This paper therefore, reviews various biometrics technology implementation strategies available,...
One of the most damaging security threats on the Internet today is cyberattacks. As new paradigms... more One of the most damaging security threats on the Internet today is cyberattacks. As new paradigms emerge, new vulnerabilities and flaws are discovered on a daily basis. These vulnerabilities have been consistently exploited by malicious users to stage cyberattacks, which erode the confidentiality, integrity and availability of critical data, and other computing resources. In recent times, the research focus has been on signature based and anomaly detection approaches. However, the challenges of using known attack signatures and profiles have made the prediction of attacks an elusive and cumbersome activity. The use of task specific algorithms has also created more setbacks in cyberattack prediction, hence the need for new approaches that exploit the learning of data representations. Therefore, this paper presents a combination of Principal Component Analysis (PCA) and Expectation Maximization (EM) for intelligent clustering, and a supervised Deep Neural Network (DNN) for the trainin...
The prediction of cyberattacks has been a major concern in cybersecurity. This is due to the huge... more The prediction of cyberattacks has been a major concern in cybersecurity. This is due to the huge financial and resource losses incurred by organisations after a cyberattack. The emergence of new applications and disruptive technologies has come with new vulnerabilities, most of which are novel-with no immediate remediation available. Recent attacks signatures are becoming evasive, deploying very complex techniques and algorithms to infiltrate a network, leading to unauthorized access and modification of system parameters and classified data. Although there exists several approaches to mitigating attacks, challenges of using known attack signatures and modeled behavioural profiles of network environments still linger. Consequently, this paper discusses the use of unsupervised statistical and supervised deep learning techniques to predict attacks by mapping hyper-alerts to class labels of attacks. This enhances the processes of feature extraction and transformation, as a means of giving structured interpretation of the dynamic profiles of a network.
Uploads
Papers by Ayei Ibor