http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/104 2026-10-28T17:36:55Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/32017 Title: SECURE IMAGE STEGANOGRAPHY USING LEAST SIGNIFICANT BIT AND EDWARD-CURVE DIGITAL SIGNATURE ALGORITHM Authors: Gungbias, Hosea; Okechukwu, Chukwuemeka; Nwodo, Rosemary; Anyaora, Peter; Waziri, Victor; Ismaila, idris Abstract: Image steganography is the process of incorporating text into photos so that the Human Visual System (HVS) cannot detect it and that only the sender and recipient are aware of its presence. There has been numerous works done on image steganography, however some of the challenges outlined by most of the work include low Peak Signal-to-Noise Ratio (PSNR) and low compression rate. This research proposes a model for image steganography that makes use of Edward-Curve Digital Signature Algorithm (EdDSA), Least Significant Bit (LSB), and Diffie-Hellman algorithm. The information to be hidden in the image is first of all encrypted and signed using EdDSA and then the encrypted message is then encrypted using the Diffie-Hellman algorithm to deal with the issue of key exchange. The encrypted message is then embedded in the image using LSB method which is one of the most used steganographic methods. In order to strengthen the security of the information, the model not only conceals information in a picture but also encrypts it and offers a hash. Both subjective and objective analysis are used to assess the aforementioned technique. The experimental results were evaluated based on PSNR and Space Saving and the proposed method outperforms a number of other methods by having higher Peak Signal-to-Noise Ratio (PSNR) values and a high a compression rate. The result of the study was tremendous having a PSNR values of 96.21415, 99.76348, 88.85138, 98.15750 for Baboon, House, Pepper, Dog stego images respectively. The result also had a compression rate of 90%, 42%, 95% and 83% for Baboon, House, Pepper, and Dog respectively. The proposed image steganography model, integrating EdDSA, LSB, and the Diffie-Hellman algorithm, not only effectively conceals information within images but also enhances security through encryption and hashing. The achieved superior performance in terms of higher PSNR values and substantial compression rates, as validated through comprehensive analyses, underscores the efficacy and robustness of the proposed method in safeguarding sensitive information during transmission. 2024-04-22T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/32016 Title: Machine Learning Approach for the Classification of Short Message Service Spam Authors: Lawal, Safinatu; Adebayo, Olawale; Anyaora, Peter Abstract: The unwanted advertisements sent with Short Message Services (SMS) significantly interferes the normal communication among customers, which should have been guaranteed by the mobile service providers. Such trends have attracted many phishing and spamming attacks using SMS messages. The issue with SMS spam classification lies in its direct impact on user security, privacy, communication experience, technological advancements, and the broader social and economic landscape. It plays a crucial role in fostering a safer and more efficient digital environment for users. They can unknowingly access suspect websites and be at risk of phishing attacks or malware downloads. The study embarked on an extensive exploration of SMS spam classification techniques, aiming to distinguish between legitimate messages (ham) and unsolicited or unwanted messages (spam). Kaggle database provided us the dataset “SMS Spam Collection” which is made up of over 10,000 records of Ham and Spam messages. The “SMS Spam Collection” dataset is a public set of SMS labelled messages that have been collected for mobile phone spam research. The dataset was splited into 70% training set and 30% testing set. The performance was evaluated based on Accuracy, Precision, Recall and F1-Score. Based on the analysis of the experimental results of the NB-SVM hybridized model, the Accuracy of 0.98 (98%), 0.99 and 0.91 Precisions for HAM and SPAM respectively, 0.99 and 0.93 Recall for HAM and SPAM respectively, and 0.99 and 0.92 F1-Score for HAM and SPAM respectively. 2024-04-22T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31990 Title: ARTIFICIAL INTELLIGENCE – POWERED CYBER WARFARE: RESOURCES, METHODS, WEAPONISATION, FORENSICS, THREAT INTELLIGENCE AND DEFENSES: A SYSTEMATIC REVIEW Authors: Joseph, Christiana; Ojeniyi, Joseph; Noel, Moses; Ahmad, Suleiman; Olusanjo, Fasola; Anyaora, Peter Abstract: Artificial Intelligence (AI) is gradually changing how cyber warfare works, creating new opportunities but also posing threats. This systematic literature review analyses how AI is used in different areas, focusing on resources, methods, weaponisation, forensics, threat intelligence, defenses and research directions. This study uses PRISMA-ScR methodology to analyse relevant publications published between 2018 and 2025. The findings indicate that AI technologies are increasingly being utilised for reconnaissance, automated cyberattacks, and enhanced threat detection. It also increases defensive measures by implementing intelligent systems that ensure faster response and mitigation. However, the increased reliance on AI raises serious concerns about ethical concerns, algorithmic bias, data privacy issues, and the high risk of autonomous weaponisation. The study highlights the need to improve AI-based defense systems and forensic methods to keep up with changing cyber threats. The paper ends by recommending more research in AI, stronger cybersecurity systems, and greater international cooperation to ensure AI is used responsibly in cyber warfare. 2025-09-16T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31989 Title: Enhanced Convolutional Neural Network Model Using Bidirectional Encoder Representations from Transformers and Long Short-Term Memory for Hate Speech Detection Authors: Okechukwu, Chukwuemeka; Nwodo, Chinweoke; Anyaora, Peter; Gungbias, Hosea; Subairu, Sikiru; Ojeniyi, Joseph Abstract: Hate speech has become a prevalent and concerning issue in this digital age as a result of the adoption of social media as a means of communication, social interaction, and driving social discussion. This trend has opened a novel front in cybercrime, cyberbullying, privacy violations, and exposure of the people component of information security to violence, subsequently violating the governance and internal security of nations. The detection and mitigation of hate speech on online platforms have gained significant attention due to the growing influence of social media and the internet as communication channels and the adoption of the same as a means of propagating hate and hate crimes. This thesis offers a model for the more accurate detection of hate speech on an online platform using Bidirectional Encoder Representations from Transformers (BERT) to enhance the feature selection layer of Convolutional Neural Networks (CNN) and a hybrid of CNN and Long-Short-Term Memory (LSTM) to detect hate speech. The proposed BERT-CNN-LSTM model achieved an accuracy of 96.1%, which is an increase of over 0.45% from 95.63% in the benchmark paper, thereby contributing a novel and more accurate tool that, when adapted into the security policies of organisations and governments, will help safeguard the rights and privacy of users, thereby protecting them from both physical and psychological harm resulting from hate propagation over the internet. 2024-04-22T00:00:00Z