Papers by Beatrice Akumba
International journal of advanced trends in computer science and engineering, Dec 6, 2023
The Internet of Things (IoT) refers to a network of interconnected smart devices. The growth of I... more The Internet of Things (IoT) refers to a network of interconnected smart devices. The growth of IoT devices has increased the vulnerability of the network to attacks, such as Denial of Service (DoS) and Distributed Denial of Service (DDoS). Denial-of-Service (DoS) attacks are malicious activities aimed at rendering a computer network, system, or online service unavailable to legitimate users. This research addresses the growing vulnerability of IoT networks to DoS/DDoS attacks by developing a hybrid intrusion detection model to detect these attacks. The model integrates Kalman Filter (KF) with Artificial Neural Network (KF-ANN), Random Forest (KF-RF), Support Vector Machine (KF-SVM) and K-Nearest Neighbor (KF-KNN) machine learning models. The Kalman filter is an efficient tool for estimating the state of a system especially in the midst of uncertainty. Kalman filter was used to estimate the state of the system while the machine learning models were used to make predictions based on the estimated state to detect attacks in IoT. The model was tested using the DoS/DDoS Message Queueing Telemetry Protocol (MQTT) IoT dataset. Results shows Receiver Operative Curve Area Under the Curve (ROC-AUC) of 0.99% for KF-ANN and KF-RF, 0.98% and 0.97% for KF-KNN and KF-SVM. Detection accuracy of approximately 0.96%, 0.94% and 93% for KF-RF and KF-ANN, KF-KNN and KF-SVM respectively
International journal of scientific research in computer science, engineering and information technology, Jun 5, 2023
Fake-news refers to a cyber-weapon launched through the social media, as, its consequence can res... more Fake-news refers to a cyber-weapon launched through the social media, as, its consequence can result to the breakdown of law and order in the society both physically and on the cyber-social-space. In Nigeria, there is currently no established law that guides the use of social media. Therefore, the rate at which fake-news propagates is alarming. This paper presents a new dataset, with focus on Nigeria's trending news such as EndSARS and Herdsmen attacks, which was further used to simulate Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) machine learning models to detect fake-news. The data were extracted from twitter using twitter Application Package Interface (API) and from facebook using a scraping tool. The dataset was encoded using Unicode escape function in python to make all characters accessible by the algorithm and tokenised using Global Vectors for Word Representation. The dataset was used to train CNN and RNN models built in python on google colab platform to detect fake-news using accuracy, sensitivity, recall and F1 score as evaluation metrics. Results showed that RNN performed better in terms of accuracy and precision, at 82.34% and 93.19% compared to 81.96% and 79.65% for CNN, F1 scores are approximately the same for both models and CNN performed better than RNN in terms of recall at 98.03% to 50.61% for RNN.
Zenodo (CERN European Organization for Nuclear Research), Jun 8, 2023
This paper introduced the concept of a hybrid machine learning method for estimating software pro... more This paper introduced the concept of a hybrid machine learning method for estimating software project cost. The literature review of some of the models commonly used in the software project cost estimation was carried out. A hybrid method of algorithms comprising Random Forest (RF), Kalman Filter (KF) and Support Vector Machine (SVM) algorithms respectively were proposed to predict the software project cost and its completion time for software projects. The proposed architecture of the model was presented as well as the proposed the model.
International Journal of Advanced Trends in Computer Science and Engineering , 2023
The Internet of Things (IoT) refers to a network of interconnected smart devices. The growth of I... more The Internet of Things (IoT) refers to a network of interconnected smart devices. The growth of IoT devices has increased the vulnerability of the network to attacks, such as Denial of Service (DoS) and Distributed Denial of Service (DDoS). Denial-of-Service (DoS) attacks are malicious activities aimed at rendering a computer network, system, or online service unavailable to legitimate users. This research addresses the growing vulnerability of IoT networks to DoS/DDoS attacks by developing a hybrid intrusion detection model to detect these attacks. The model integrates Kalman Filter (KF) with Artificial Neural Network (KF-ANN), Random Forest (KF-RF), Support Vector Machine (KF-SVM) and K-Nearest Neighbor (KF-KNN) machine learning models. The Kalman filter is an efficient tool for estimating the state of a system especially in the midst of uncertainty. Kalman filter was used to estimate the state of the system while the machine learning models were used to make predictions based on the estimated state to detect attacks in IoT. The model was tested using the DoS/DDoS Message Queueing Telemetry Protocol (MQTT) IoT dataset. Results shows Receiver Operative Curve Area Under the Curve (ROC-AUC) of 0.99% for KF-ANN and KF-RF, 0.98% and 0.97% for KF-KNN and KF-SVM. Detection accuracy of approximately 0.96%, 0.94% and 93% for KF-RF and KF-ANN, KF-KNN and KF-SVM respectively
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Fake-news refers to a cyber-weapon launched through the social media, as, its consequence can res... more Fake-news refers to a cyber-weapon launched through the social media, as, its consequence can result to the breakdown of law and order in the society both physically and on the cyber-social-space. In Nigeria, there is currently no established law that guides the use of social media. Therefore, the rate at which fake-news propagates is alarming. This paper presents a new dataset, with focus on Nigeria’s trending news such as EndSARS and Herdsmen attacks, which was further used to simulate Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) machine learning models to detect fake-news. The data were extracted from twitter using twitter Application Package Interface (API) and from facebook using a scraping tool. The dataset was encoded using Unicode escape function in python to make all characters accessible by the algorithm and tokenised using Global Vectors for Word Representation. The dataset was used to train CNN and RNN models built in python on google colab platf...
International Journal of Advanced Computer Science and Applications
Severe outbreaks of infectious disease occur throughout the world with some reaching the level of... more Severe outbreaks of infectious disease occur throughout the world with some reaching the level of international pandemic: Coronavirus (COVID-19) is the most recent to do so. In this paper, a mechanism is set out using Zipf's law to establish the accuracy of international reporting of COVID-19 cases via a determination of whether an individual country's COVID-19 reporting follows a power-law for confirmed, recovered, and death cases of COVID-19. The probability of Zipf's law (P-values) for COVID-19 confirmed cases show that Uzbekistan has the highest P-value of 0.940, followed by Belize (0.929), and Qatar (0.897). For COVID-19 recovered cases, Iraq had the highest P-value of 0.901, followed by New Zealand (0.888), and Austria (0.884). Furthermore, for COVID-19 death cases, Bosnia and Herzegovina had the highest P-value of 0.874, followed by Lithuania (0.843), and Morocco (0.825). China, where the COVID-19 pandemic began, is a significant outlier in recording P-values lower than 0.1 for the confirmed, recovered, and death cases. This raises important questions, not only for China, but also any country whose data exhibits P-values below this threshold. The main application of this work is to serve as an early warning for World Health Organization (WHO) and other health regulatory bodies to perform more investigations in countries where COVID-19 datasets deviate significantly from Zipf's law. To this end, this paper provide a tool for illustrating Zipf's law P-values on a global map in order to convey the geographic distribution of reporting anomalies.
ArXiv, 2021
Terrorism is one of the most life-challenging threats facing humanity worldwide. The activities o... more Terrorism is one of the most life-challenging threats facing humanity worldwide. The activities of terrorist organizations threaten peace, disrupts progress, and halt the development of any nation. Terrorist activities in Nigeria in the last decades have negatively affected economic growth and have drastically reduced the possibilities of foreign investments in Nigeria. In this paper, statistical and inferential insights are applied to the terrorist activities in Nigeria from 1970 to 2019. Using the Global Terrorism Database (GTD), insights are made on the occurrences of terrorist attacks, the localities of the target, and the successful and unsuccessful rates of such attacks. The Apriori algorithm is also used in this paper to draw hidden patterns from the GTD to aid in generating strong rules through database mining, resulting in relevant insights. This understanding of terrorist activities will provide security agencies with the needed information to be one step ahead of terroris...
ArXiv, 2021
Terrorism is one of the most serious life-challenging threat facing humanity around the world. Th... more Terrorism is one of the most serious life-challenging threat facing humanity around the world. The activities of terrorist organization threatens peace, disrupts progress and halts all-round development of any nation. Terrorist activities in Nigeria in the last decades has negatively affected the economic growth and has drastically reduced the possibilities of foreign investments in Nigeria. In this paper, statistical and inferential insights are applied to the terrorist activities in Nigeria between 1970 to 2019. Using the Global Terrorism Database (GTD), insights are made on the occurrences of terrorist attacks, the localities of target and the successful and unsuccessful rates of such attacks. The Apriori algorithm is also used in this paper to draw hidden patterns from the GTD in order to aid in the generation of strong rules through database mining, resulting in relevant insights. This understanding of terrorist activities will provide security agencies with the needed informat...
Authentication of Video Evidence for Forensic Investigation: A Case of Nigeria
Journal of Information Security, 2021
Video shreds of evidence are usually admissible in the court of law all over the world. However, ... more Video shreds of evidence are usually admissible in the court of law all over the world. However, individuals manipulate these videos to either defame or incriminate innocent people. Others indulge in video tampering to falsely escape the wrath of the law against misconducts. One way impostors can forge these videos is through inter-frame video forgery. Thus, the integrity of such videos is under threat. This is because these digital forgeries seriously debase the credibility of video contents as being definite records of events. This leads to an increasing concern about the trustworthiness of video contents. Hence, it continues to affect the social and legal system, forensic investigations, intelligence services, and security and surveillance systems as the case may be. The problem of inter-frame video forgery is increasingly spontaneous as more video-editing software continues to emerge. These video editing tools can easily manipulate videos without leaving obvious traces and these...
International Journal of Advanced Computer Science and Applications
Severe outbreaks of infectious disease occur throughout the world with some reaching the level of... more Severe outbreaks of infectious disease occur throughout the world with some reaching the level of international pandemic: Coronavirus (COVID-19) is the most recent to do so. In this paper, a mechanism is set out using Zipf's law to establish the accuracy of international reporting of COVID-19 cases via a determination of whether an individual country's COVID-19 reporting follows a power-law for confirmed, recovered, and death cases of COVID-19. The probability of Zipf's law (P-values) for COVID-19 confirmed cases show that Uzbekistan has the highest P-value of 0.940, followed by Belize (0.929), and Qatar (0.897). For COVID-19 recovered cases, Iraq had the highest P-value of 0.901, followed by New Zealand (0.888), and Austria (0.884). Furthermore, for COVID-19 death cases, Bosnia and Herzegovina had the highest P-value of 0.874, followed by Lithuania (0.843), and Morocco (0.825). China, where the COVID-19 pandemic began, is a significant outlier in recording P-values lower than 0.1 for the confirmed, recovered, and death cases. This raises important questions, not only for China, but also any country whose data exhibits P-values below this threshold. The main application of this work is to serve as an early warning for World Health Organization (WHO) and other health regulatory bodies to perform more investigations in countries where COVID-19 datasets deviate significantly from Zipf's law. To this end, this paper provide a tool for illustrating Zipf's law P-values on a global map in order to convey the geographic distribution of reporting anomalies.
A Forensic Investigation of Terrorism in Nigeria: An Apriori Algorithm Approach
Journal of Information Security
A Predictive Risk Model for Software Projects’ Requirement Gathering Phase
International Journal of Innovative Science and Research Technology
The initial stage of the software development lifecycle is the requirement gathering and analysis... more The initial stage of the software development lifecycle is the requirement gathering and analysis phase. Predicting risk at this phase is very crucial because cost and efforts can be saved while improving the quality and efficiency of the software to be developed. The datasets for software requirements risk prediction have been adopted in this paper to predict the risk levels across the software projects and to ascertain the attributes that contribute to the recognized risk in the software projects. A supervised machine learning technique was used to predict the risk across the projects using Naïve Bayes Classifier technique. The model was able to predict the risks across the projects and the performance metrics of the risk attributes were evaluated. The model predicted four (4) as Catastrophic, eleven (11) as High, eighteen (18) as Moderate, thirty-three (33) as Low and seven (7) as insignificant. The overall confusion matrix statistics on the risk levels prediction by the model ha...
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Unstructured Supplementary Services Data (USSD) is a menu driven, real time communication technol... more Unstructured Supplementary Services Data (USSD) is a menu driven, real time communication technology used for value added services. It is adopted by banks for financial transactions due to its ease of operation. However existing USSD are used by fraudster to commit identity theft through Subscriber Identification Module (SIM) swap, phone theft and kidnap, in other to access funds in the bank. One of the reasons this is made possible is because existing USSD platforms use Automated Teller Machine (ATM) Personal Identification Number (PIN) as second level authenticator and this compromises the ATM channel and violets one of the stated guidelines for USSD operation in Nigeria. More so, the PIN is entered bare on the platform and so can easily be stolen by shoulder surfing. Therefore, in this paper we developed and simulated an improved USSD security model for banking operations in Nigeria. The security of existing USSD platform was enhanced using answer to a secret question as another ...
Uploads
Papers by Beatrice Akumba