Role of AI in Predicting and Mitigating Threats: A Comprehensive Review

Authors

  • Aftab Arif Washington University of Science and Technology
  • Ali Khan Virginia University of Science & Technology
  • Muhammad Ismaeel Khan MSIT at Washington university of science and technology‬ - ‪information technology‬ - ‪database management‬

Keywords:

artificial intelligence, cybersecurity, danger prediction, public health, moral issues, prejudice, data privacy, machine learning, cooperation between humans and AI, and legal frameworks

Abstract

The field of danger prediction and mitigation is changing due to artificial intelligence (AI) in a number of areas, including national security, cybersecurity, public health, and finance. This paper examines how artificial intelligence (AI) might improve threat detection, response, and prevention. It emphasizes AI's capacity to scan large datasets and spot patterns that speed up decision-making. Anomaly detection in cybersecurity, disease outbreak and natural disaster prediction using predictive modeling, and financial system fraud detection are some of the key uses. The application of AI technologies, however, brings up important ethical issues, such as algorithmic bias, data privacy, responsibility, and the requirement for openness. In order to responsibly manage AI implementation, the essay highlights the significance of ethical AI practices and the creation of strong regulatory frameworks. Future trends point to a move toward more sophisticated machine learning methods, the incorporation of AI with cutting-edge platforms like block chain and the Internet of Things (IoT), and an emphasis on human-AI cooperation. The article's conclusion is that, despite AI's enormous potential to improve security and resilience, responsible use of this disruptive technology will need proactive interaction with a variety of stakeholders and ethical considerations. Society can successfully handle the complexities of AI and make sure it works as a positive force to counteract emerging risks by encouraging a collaborative approach.

References

Ali, S. M., Augusto, J. C., & Windridge, D. (2019). A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation. Applied Artificial Intelligence, 33(8), 747-774.

Al-Mansoori, S., & Salem, M. B. (2023). The role of artificial intelligence and machine learning in shaping the future of cybersecurity: trends, applications, and ethical considerations. International Journal of Social Analytics, 8(9), 1-16.

Amarappa, S., & Sathyanarayana, S. V. (2014). Data classification using Support vector Machine (SVM), a simplified approach. Int. J. Electron. Comput. Sci. Eng, 3, 435-445.

Babu, C. S. (2024). Adaptive AI for Dynamic Cybersecurity Systems: Enhancing Protection in a Rapidly Evolving Digital Landscap. In Principles and Applications of Adaptive Artificial Intelligence (pp. 52-72). IGI Global.

Balogun, O.D., Ayo-Farai, O., Ogundairo, O., Maduka, C.P., Okongwu, C.C., Babarinde, A.O. and Sodamade, O.T., 2024. The Role Of Pharmacists In Personalised Medicine: A Review Of Integrating Pharmacogenomics Into Clinical Practice. International Medical Science Research Journal, 4(1), pp.19-36.

Bernstein, D. J. (2009). Introduction to post-quantum cryptography. In Post-quantum cryptography (pp. 1-14). Berlin, Heidelberg: Springer Berlin Heidelberg

Bouchama, F., & Kamal, M. (2021). Enhancing Cyber Threat Detection through Machine Learning-Based Behavioral Modeling of Network Traffic Patterns. International Journal of Business Intelligence and Big Data Analytics, 4(9), 1-9.

Bouchama, F., & Kamal, M. (2021). Enhancing Cyber Threat Detection through Machine Learning-Based Behavioral Modeling of Network Traffic Patterns. International Journal of Business Intelligence and Big Data Analytics, 4(9), 1-9.

Buhrmester, V., Münch, D., & Arens, M. (2021). Analysis of explainers of black box deep neural networks for computer vision: A survey. Machine Learning and Knowledge Extraction, 3(4), 966-989.

Cains, M. G., Flora, L., Taber, D., King, Z., & Henshel, D. S. (2022). Defining cyber security and cyber security risk within a multidisciplinary context using expert elicitation. Risk Analysis, 42(8), 1643-1669.

Chaudhry, M., Shafi, I., Mahnoor, M., Vargas, D. L. R., Thompson, E. B., & Ashraf, I. (2023). A systematic literature review on identifying patterns using unsupervised clustering algorithms: A data mining perspective. Symmetry, 15(9), 1679.

Cheng, L., Varshney, K. R., & Liu, H. (2021). Socially responsible ai algorithms: Issues, purposes, and challenges. Journal of Artificial Intelligence Research, 71, 1137-1181.

Formosa, P., Wilson, M., & Richards, D. (2021). A principlist framework for cybersecurity ethics. Computers & Security, 109, 102382.

George, A. S. (2023). Securing the future of finance: how AI, Blockchain, and machine learning safeguard emerging Neobank technology against evolving cyber threats. Partners Universal Innovative Research Publication, 1(1), 54- 66.

George, A. S., George, A. H., & Baskar, T. (2023). Digitally immune systems: building robust defences in the age of cyber threats. Partners Universal International Innovation Journal, 1(4), 155-172.

Habeeb, R. A. A., Nasaruddin, F., Gani, A., Hashem, I. A. T., Ahmed, E., & Imran, M. (2019). Real-time big data processing for anomaly detection: A survey. International Journal of Information Management, 45, 289-307

Hassan, A.O., Ewuga, S.K., Abdul, A.A., Abrahams, T.O., Oladeinde, M. and Dawodu, S.O., 2024. Cybersecurity in Banking: A Global Perspective with a Focus On Nigerian Practices. Computer Science & IT Research Journal, 5(1), pp.41-59.

Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., & Hussain, A. (2024). Interpreting blackbox models: a review on explainable artificial intelligence. Cognitive Computation, 16(1), 45-74.

Hatzivasilis, G., Ioannidis, S., Smyrlis, M., Spanoudakis, G., Frati, F., Goeke, L., & Koshutanski, H. (2020). Modern aspects of cyber-security training and continuous adaptation of programmes to trainees. Applied Sciences, 10(16), 5702. Magna Scientia Advanced Research and Reviews, 2024, 10(01), 312–320 320

Kak, S. (2022). Zero Trust Evolution & Transforming Enterprise Security (Doctoral dissertation, California State University San Marcos).

Khan, W. Z., Raza, M., & Imran, M. (2023). Quantum Cryptography a Real Threat to Classical Blockchain: Requirements and Challenges. Authorea Preprints.

Kim, A., Park, M., & Lee, D. H. (2020). AI-IDS: Application of deep learning to real-time Web intrusion detection. IEEE Access, 8, 70245-70261.

Kumar, S., Gupta, U., Singh, A. K., & Singh, A. K. (2023). Artificial intelligence: revolutionizing cyber security in the digital era. Journal of Computers, Mechanical and Management, 2(3), 31-42.

Lallie, H. S., Shepherd, L. A., Nurse, J. R., Erola, A., Epiphaniou, G., Maple, C., & Bellekens, X. (2021). Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic. Computers & security, 105, 102248.

Li, J. H. (2018). Cyber security meets artificial intelligence: a survey. Frontiers of Information Technology & Electronic Engineering, 19(12), 1462-1474.

Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied sciences, 9(20), 4396.

Markevych, M., & Dawson, M. (2023). A review of enhancing intrusion detection systems for cybersecurity using artificial intelligence (ai). In International conference Knowledge-based Organization (Vol. 29, No. 3, pp. 30-37).

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6), 1-35.

Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Overfitting, model tuning, and evaluation of prediction performance. In Multivariate statistical machine learning methods for genomic prediction (pp. 109- 139). Cham: Springer International Publishing.

Nassar, A., & Kamal, M. (2021). Machine Learning and Big Data analytics for Cybersecurity Threat Detection: A Holistic review of techniques and case studies. Journal of Artificial Intelligence and Machine Learning in Management, 5(1), 51-63.

Nyre-Yu, M., Morris, E., Moss, B. C., Smutz, C., & Smith, M. (2022). Explainable AI in Cybersecurity Operations: Lessons Learned from xAI Tool Deployment. In Proceedings of the Usable Security and Privacy (USEC) Symposium, San Diego, CA, USA (Vol. 28).

Radanliev, P., & Santos, O. (2023). Adversarial Attacks Can Deceive AI Systems, Leading to Misclassification or Incorrect Decisions.

Reddy, Y. C. A. P., Viswanath, P., & Reddy, B. E. (2018). Semi-supervised learning: A brief review. Int. J. Eng. Technol, 7(1.8), 81. [40] Richards, N., & Hartzog, W. (2016). Privacy's Trust Gap: A Review.

Sadik, S., Ahmed, M., Sikos, L. F., & Islam, A. N. (2020). Toward a sustainable cybersecurity ecosystem. Computers, 9(3), 74.

Schulte, P. A., Streit, J. M., Sheriff, F., Delclos, G., Felknor, S. A., Tamers, S. L., & Sala, R. (2020). Potential scenarios and hazards in the work of the future: A systematic review of the peer-reviewed and gray literatures. Annals of Work Exposures and Health, 64(8), 786-816

Sharma, D. K., Mishra, J., Singh, A., Govil, R., Srivastava, G., & Lin, J. C. W. (2022). Explainable artificial intelligence for cybersecurity. Computers and Electrical Engineering, 103, 108356.

Tounsi, W., & Rais, H. (2018). A survey on technical threat intelligence in the age of sophisticated cyber-attacks. Computers & security, 72, 212-233.

Vincent, A.A., Segun, I.B., Loretta, N.N. and Abiola, A., 2021. Entrepreneurship, agricultural value-chain and exports in Nigeria. United International Journal for Research and Technology, 2(08), pp.1-8.

Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350-361.

Maloy Jyoti Goswami. (2024). Improving Cloud Service Reliability through AI-Driven Predictive Analytics. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(2), 27– 34. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/75

Yuan, X., et al. (2019). "Adversarial Examples: Attacks and Defenses for Deep Learning." IEEE Transactions on Neural Networks and Learning Systems, 30(9), 2805-2824

Yuan, X., et al. (2019). "Adversarial Examples: Attacks and Defenses for Deep Learning." IEEE Transactions on Neural Networks and Learning Systems, 30(9), 2805-2824.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). "Deep Learning." Nature, 521(7553), 436-444. [18]. Kaspersky Lab. (2020). "AI in Cybersecurity: The Key to Identifying and Preventing Threats." Kaspersky Whitepaper.

Egele, M., et al. (2017). "Malware Analysis and Detection Using Deep Learning Models." ACM Transactions on Information and System Security (TISSEC), 20(4), 1-28.

Garofalo, J., et al. (2019). "Using AI for Cyber Threat Intelligence." IEEE Security & Privacy, 17(5), 41-49.

Russakovsky, O., et al. (2015). "ImageNet Large Scale Visual Recognition Challenge." International Journal of Computer Vision, 115(3), 211-252.

Li, K., Zhu, A., Zhou, W., Zhao, P., Song, J., & Liu, J. (2024). Utilizing deep learning to optimize software development processes. arXiv preprint arXiv:2404.13630.

Liu, S., Yan, K., Qin, F., Wang, C., Ge, R., Zhang, K., & Cao, J. (2024). Infrared Image Super-Resolution via Lightweight Information Split Network. arXiv preprint arXiv:2405.10561. 36. Cao, Y., Weng, Y., Li, M., & Yang, X. The Application of Big Data and AI in Risk Control Models: Safeguarding User Security. International Journal of Frontiers in Engineering Technology, 6(3), 154-164.

Wang, J., Hong, S., Dong, Y., Li, Z., & Hu, J. (2024). Predicting Stock Market Trends Using LSTM Networks: Overcoming RNN Limitations for Improved Financial Forecasting. Journal of Computer Science and Software Applications, 4(3), 1-7.

Zhai, H., GU, B., Zhu, K., & Huang, C. (2023). Feasibility analysis of achieving net-zero emissions in China's power sector before 2050 based on ideal available pathways. Environmental Impact Assessment Review, 98, 106948

GU, B., Zhai, H., an, Y., Khanh, N. Q., & Ding, Z. (2023). Low-carbon transition of Southeast Asian power systems–A SWOT analysis. Sustainable Energy Technologies and Assessments, 58, 103361.

Downloads

Published

04-10-2024

How to Cite

Aftab Arif, Ali Khan, & Muhammad Ismaeel Khan. (2024). Role of AI in Predicting and Mitigating Threats: A Comprehensive Review. JURIHUM : Jurnal Inovasi Dan Humaniora, 2(3), 297–311. Retrieved from https://jurnalmahasiswa.com/index.php/Jurihum/article/view/1570