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All Journal Asian Journal of Science, Technology, Engineering, and Art Mikailalsys Journal of Advanced Engineering International Mikailalsys Journal of Mathematics and Statistics International Journal of Education, Management, and Technology
Nnamani, Chinenye Cordelia
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Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Transforming Data Analytics with AI for Informed Decision-Making Akintayo, Taiwo Abdulahi; Paul, Chadi; Queenet, Madumere Chiamaka; Nnadiekwe, Oluchi Anthonia; Victoria, Shittu Sarah; David, Fakokunde Babatunde; Joel, Ogundigba Omotunde; Agada, Olowu Innocent; Ngozi, Egenuka Rhoda; Arinze, Ugochukwu Ukeje; Ojemerenvhie, Grace Alele; Oluwadamilola, Adebesin Adedayo; Nnamani, Chinenye Cordelia; Olayinka, Usman Wasiu
International Journal of Education, Management, and Technology Vol 2 No 3 (2024): International Journal of Education, Management, and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ijemt.v2i3.3812

Abstract

This study delves into how advanced data analytics and artificial intelligence (AI) can work together to enhance decision-making processes. As we navigate today’s data-driven environment, discovering the synergy between these fields is crucial, given the growing complexity of datasets. Advanced analytical tools are essential, and AI offers exceptional capabilities in pattern recognition and automation. This research investigates how cosmbining data analytics techniques—such as Predictive Modeling, Clustering, and Trend Analysis—with AI approaches like Machine Learning and Deep Learning can improve decision-making. A key focus of the study is on making AI models more interpretable and transparent. It emphasizes the importance of ensuring that AI-driven decisions are clear and understandable. Additionally, the research addresses ethical considerations and the need for human-centered design, aiming to balance AI’s power with openness. It also strives for responsible AI use by tackling issues such as bias and promoting ethical practices in the application of advanced data analytics and AI. The study demonstrates practical applications in areas like healthcare and finance, showing how these technologies can transform personalized medicine, disease prediction, risk assessment, fraud detection, and market trend analysis. Overall, this research highlights the valuable interaction between advanced data analytics and AI, offering a guide for organizations to enhance their decision-making while adhering to ethical standards and responsible AI use.
Exploiting AI Capabilities: An in-Depth Analysis of Artificial Intelligence Integration in Cybersecurity for Threat Detection and Response Nnamani, Chinenye Cordelia
International Journal of Education, Management, and Technology Vol 2 No 3 (2024): International Journal of Education, Management, and Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ijemt.v2i3.3904

Abstract

This Article thoroughly examines the revolutionary impact of Artificial Intelligence (AI) on improving threat detection and response tactics within the swiftly changing realm of cybersecurity. Conventional security measures, struggling against the complexity of contemporary cyber threats, fail to provide adequate protection. In response, AI, driven by machine learning algorithms and predictive analytics, becomes a dynamic and adaptive entity strengthening digital defenses. The investigation commences with a comprehensive analysis of the methods by which AI enhances danger detection. Behavioral analytics utilizes AI to assess user behaviors and network activity, creating a proactive baseline, while anomaly detection and predictive analysis harness machine learning to recognize deviations from the norm and forecast potential dangers. This comprehensive strategy enables organizations to remain proactive against emerging cyber threats. Moreover, the study explores the crucial function of AI in incident response. AI-driven automated incident analysis expedites reaction times by rapidly analyzing and prioritizing security warnings. The amalgamation of AI with threat intelligence streams guarantees a perpetually updated knowledge repository, enabling organizations to respond adeptly to emerging dangers. The dynamic flexibility of AI allows systems to evolve and learn from each incidence, hence enhancing their defensive capacities over time. The discourse recognizes the significant advantages of AI in cybersecurity while simultaneously addressing the obstacles associated with its application. False positives, a potential drawback, require a measured approach to prevent the perception of typical action as harmful. Ethical factors, including privacy concerns and responsible AI practices, highlight the necessity for a judicious and principled incorporation of AI in cybersecurity. The paper underscores the essential collaborative synergy between human expertise and AI technologies. The essay emphasizes the importance of continuous investment in AI training programs for cybersecurity professionals, acknowledging that AI is best successful when enhanced by human insights. Additionally, it advocates for routine security audits to assess and refine cybersecurity protocols, collaborative research efforts to tackle ethical issues, and user education activities to strengthen collective defenses. As the digital landscape evolves, the incorporation of AI in cybersecurity seems not as a cure-all but as a formidable ally. This partnership guarantees a robust protection against increasingly complex cyber-attacks, reinforcing the basis for a secure digital future.
Machine Learning Algorithm for Enhanced Cybersecurity: Identification and Mitigation of Emerging Threats Nnamani, Chinenye Cordelia
Mikailalsys Journal of Mathematics and Statistics Vol 2 No 3 (2024): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v2i3.3917

Abstract

Machine learning (ML) methodologies have significantly transformed cybersecurity by offering sophisticated instruments that not only identify but also avert and alleviate cyber threats. This research study seeks to examine the convergence of machine learning and cybersecurity, focusing on diverse methodologies and their use in enhancing cybersecurity measures. The study examines several machines learning methods, including Graph Neural Networks, Adversarial Learning, Federated Learning, Explainable AI, and Reinforcement Learning. Every algorithm is essential for enhancing the identification and mitigation of cyber assaults. Graph Neural Networks facilitate the modelling of intricate linkages within cybersecurity data. It aids not just in forecasting future events but also in identifying anomalies and analyzing network traffic. Adversarial Learning assists in training machine learning models to address the difficulty of producing misleading input data that can deceive any model, hence enhancing their efficacy. Federated Learning is examined as a method for training machine learning models across decentralized networks while preserving data privacy and enhancing model accuracy. Explainable AI methodologies primarily offer transparency and interpretability in machine learning-driven cybersecurity decisions, which are crucial for comprehension and confidence in automated security systems. Reinforcement Learning is focused on a trial-and-error methodology, wherein the model acquires new tasks through a system of rewards and penalties. These sophisticated algorithms jointly improve the effectiveness, precision, and clarity of cybersecurity protocols, offering strong protection against emerging cyber threats.
Assessing the Cybersecurity Risks Associated with the Internet of Things (IoT) Devices Akintayo, Taiwo Abdulahi; Asolo, Emmanuel; Nnamani, Chinenye Cordelia; Felix, Omojola Ayogoke; Osaro, Chukwuemeka Chukwuma; Atinuke, Aregbesola Taobat
Mikailalsys Journal of Advanced Engineering International Vol 1 No 3 (2024): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v1i3.3862

Abstract

The rapid rise of the Internet of Things (IoT) in our daily lives has brought significant cybersecurity concerns to the forefront, emphasizing the need for both active and proactive measures. This research provides a comprehensive review of the literature on the cybersecurity challenges and threats faced by various IoT devices. It outlines proposed solutions and structural frameworks while also exploring different methods for detecting and identifying potential threats. Additionally, it highlights research gaps within the industrial and economic sectors of IoT applications. Our findings reveal that the main issues affecting IoT systems include cybercrime and privacy violations. While Artificial Intelligence holds great promise for enhancing cybersecurity, many attacks, particularly those focused on authentication and confidentiality, are still inadequately addressed by existing solutions. This indicates a pressing need for further research and practical testing of the recommended defenses.
The Cloud Security Revolution: Unlocking the Potential of AI and Machine Learning to Stay Ahead of Threats Okereke, Ruth Onyekachi; Ojemerenvhie, Grace Alele; Azeez, Oladimeji Lamina; Oko-odion, Terry Uwagbae; Samson, Iyanu Opeyemi; Anosike, Chijioke Nnaemeka; Owan, Faith Obun; Nnamani, Chinenye Cordelia
Asian Journal of Science, Technology, Engineering, and Art Vol 2 No 5 (2024): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v2i5.3813

Abstract

As we navigate the digital world, cybersecurity has become a top priority. With each technological advancement, new vulnerabilities emerge, making robust defenses essential. The fusion of machine learning and artificial intelligence has become a game-changer in the fight against cyber threats. This paper delves into the latest applications of these technologies in network security, shedding light on their critical roles in addressing pressing concerns and identifying areas for further exploration. We also examine the ethical and legal implications of implementing these technologies. Our research highlights current challenges and open questions, with a focus on recent breakthroughs in network security leveraging AI and ML. The findings are promising, suggesting that further innovation in integrating AI and ML into network security frameworks holds significant potential. Exciting applications include bolstering network security, detecting malware, and responding to intrusions. Interestingly, while 45% of organizations recognize the need to adopt these technologies, half have already done so, while 5% remain hesitant.
Co-Authors Agada, Olowu Innocent Akintayo, Taiwo Abdulahi Anosike, Chijioke Nnaemeka Arinze, Ugochukwu Ukeje Asolo, Emmanuel Atinuke, Aregbesola Taobat Azeez, Oladimeji Lamina David, Fakokunde Babatunde Felix, Omojola Ayogoke Joel, Ogundigba Omotunde Ngozi, Egenuka Rhoda Nnadiekwe, Oluchi Anthonia Ojemerenvhie, Grace Alele Okereke, Ruth Onyekachi Oko-odion, Terry Uwagbae Olayinka, Usman Wasiu Oluwadamilola, Adebesin Adedayo Osaro, Chukwuemeka Chukwuma Owan, Faith Obun Paul, Chadi Queenet, Madumere Chiamaka Samson, Iyanu Opeyemi Victoria, Shittu Sarah