Cyber Security and People: Human Nature, Psychology, and Training Affect User Awareness, Social Engineering, and Security Professional Education and Preparedness

  • Mohammad Naveed Hossain
  • Tazria Zerin Khan
  • Sheikh Fahim Uz Zaman
  • Mohammad Shaba Sayeed
  • S. M. Wazid Ullah
  • Md Jahid Raihan
Keywords: Predictive Models, Security Detection, Machine Learning, Predictive Models, Threat Detection, Risk Mitigation


This work focuses on how machine learning methods may be used to identify threats and provide countermeasures. Security threats and vulnerabilities pose significant difficulties in today’s digital world. Algorithms trained with machine learning can sift through massive volumes of data, look for trends, and spot possible security breaches as they happen. These algorithms can offer preventative security measures and actions because they use sophisticated analytics and predictive models. This abstract delves into the use of machine learning to bolster security, focusing on its potential to improve threat detection and provide implementable suggestions for shoring up overall security.


[1] Lee, Sang-Woong, et al. ”Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review.” Journal of Network and Computer Applications 187 (2021): 103111.
[2] Bhardwaj, Akashdeep, and Keshav Kaushik. ”Predictive analytics-based cybersecurity framework for cloud infrastructure.” International Journal of Cloud Applications and Computing (IJCAC) 12.1 (2022): 1-20.
[3] Garcia, Anna Baron, Radu F. Babiceanu, and Remzi Seker. ”Artificial Intelligence and Machine Learning Approaches For Aviation Cybersecurity: An Overview.” 2021 Integrated Communications Navigation and Surveillance Conference (ICNS). IEEE, 2021.
[4] Dumbere, Dhananjay M., and Asha Ambhaikar. ”An ML Bio-inspired Model for improving Security and Speed of FHE for Cybersecurity.”2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES). IEEE, 2021.
[5] Nikoloudakis, Yannis, et al. ”Towards a machine learning based situational awareness framework for cybersecurity: an SDN implementation.” Sensors 21.14 (2021): 4939.
[6] Miao, Yuantian, et al. ”Machine learning–based cyber attacks targeting on controlled information: A survey.” ACM Computing Surveys (CSUR) 54.7 (2021): 1-36.
[7] Gumusbas, Dilara, and Tulay Yildirim. ”AI for Cybersecurity: MLBased Techniques for Intrusion Detection Systems.” Advances in Machine Learning/Deep Learning-based Technologies: Selected Papers in Honour of Professor Nikolaos G. Bourbakis–Vol. 2 (2022): 117-140.
[8] Hossain, Mohammad Naveed, Nafim Ahmed, and SM Wazid Ullah. ”Traffic Flow Forecasting in Intelligent Transportation Systems Prediction Using Machine Learning.” 2022 International Conference on Futuristic Technologies (INCOFT). IEEE, 2022.
[9] Singh, Sujay, et al. ”AI and ML in Vehicular Communication: A Cybersecurity Perspective.” 2022 7th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2022.
[10] Watney, M. M. ”Artificial intelligence and its’ legal risk to cybersecurity.” European conference on cyber warfare and security. Academic Conferences International Limited, 2020.
How to Cite
Hossain, M. N., Khan, T. Z., Uz Zaman, S. F., Sayeed, M. S., Ullah, S. M. W., & Raihan, M. J. (2023). Cyber Security and People: Human Nature, Psychology, and Training Affect User Awareness, Social Engineering, and Security Professional Education and Preparedness. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 9(2), 49-53.

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