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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | INTEGRATING ARTIFICIAL INTELLIGENCE TECHNIQUES INTO CYBERSECURITY: ENHANCING MALWARE BEHAVIOUR VISUALISATION THROUGH ADVANCED DASHBOARD CREATION FOR IMPROVED PERFORMANCE MONITORING |
| Authors Name: | Osamuyimen Odion Amadasun , Charles Chukwudi Ikpeama |
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IJNRD_219036
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| Published Paper Id: | IJNRD2404639 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | The proliferation of mobile devices has brought about a substantial increase in mobile malware threats, presenting critical security challenges. This study focuses on developing an advanced interactive dashboard specifically designed to evaluate machine and deep learning algorithms for mobile malware detection. The specialised interface of this dashboard empowers cybersecurity experts and researchers to dynamically engage with essential data, including crucial performance metrics like accuracy and precision. Utilising a comprehensive dataset, the dashboard provides real-time insights into algorithmic effectiveness, assessing various algorithms such as Random Forest, Logistic Regression, and Neural Networks. The study highlights the importance of advanced deep learning techniques like Neural Networks and Deep Neural Networks in enhancing precision in detecting malware. Moreover, the dashboard is complemented by diverse graphical representations that elucidate complex algorithmic outputs, facilitating strategic decision-making in mobile security. This research represents a significant advancement in mobile malware detection, providing a strategic tool to address evolving threats effectively. |
| Keywords: | Mobile Malware Detection; Cybersecurity; Machine Learning Algorithms; Deep Learning Techniques; Advanced Dashboard; Performance Metrics; Data Visualisation; Real-time Analysis; Algorithm Evaluation; Security Improvement; Ethical Data Handling; Dataset Selection. |
| Cite Article: | "INTEGRATING ARTIFICIAL INTELLIGENCE TECHNIQUES INTO CYBERSECURITY: ENHANCING MALWARE BEHAVIOUR VISUALISATION THROUGH ADVANCED DASHBOARD CREATION FOR IMPROVED PERFORMANCE MONITORING", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.g330-g364, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404639.pdf |
| Downloads: | 00033 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404639 Registration ID: 219036 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: g330-g364 Country: FCT Abuja, Federal Capital Territory, Nigeria Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404639 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404639 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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