SMRD: A Novel Cyber Warfare Modeling Framework for Social Engineering, Malware, Ransomware, and Distributed Denial-of-Service Based on a System of Nonlinear Differential Equations

https://doi.org/10.48185/jaai.v5i1.972

Authors

  • Mohamed Aly Bouke University Putra Malaysia
  • Azizol Abdullah University Putra Malaysia

Keywords:

Cyber warfare, modeling's framework, Cyber defense strategies, Cyber Security, Interdependencies and dynamics

Abstract

Cyber warfare has emerged as a critical aspect of modern conflict, as state and non-state actors increasingly leverage cyber capabilities to achieve strategic objectives. The rapidly evolving cyber threat landscape demands robust and adaptive approaches to protect against advanced cyberattacks and mitigate their impact on national security. Traditional cyber defense strategies often struggle to keep pace with the rapidly changing threat landscape, resulting in the need for more robust and adaptive approaches to protect against advanced cyberattacks. This paper presents a novel cyber warfare modeling framework, Social Engineering, Malware, Ransomware, and Distributed Denial-of-Service (SMRD), capturing the interactions and interdependencies between these core components. The SMRD framework offers insights for enhancing cyber defense, threat prediction, and proactive measures. A mathematical model consisting of a system of nonlinear differential equations is proposed to quantify the relationships and dynamics between the components.

Downloads

Download data is not yet available.

References

P. Mali, J.S. Sodhi, T. Singh, S. Bansal, Analysing the awareness of cyber crime and designing a relevant framework with respect to cyber warfare: an empirical study, Int. J. Mech. Eng. Technol. 9 (2018) 110–124.

M.A. Bouke, A. Abdullah, S.H. ALshatebi, S.A. Zaid, H. El Atigh, The intersection of targeted advertising and security: Unraveling the mystery of overheard conversations, Telemat. Informatics Reports. 11 (2023) 100092. https://doi.org/10.1016/j.teler.2023.100092.

A.P. Liff, Cyberwar: a new ‘absolute weapon’? The proliferation of cyberwarfare capabilities and interstate war, J. Strateg. Stud. 35 (2012) 401–428.

J.A. Lewis, Assessing the risks of cyber terrorism, cyber war and other cyber threats, Center for Strategic & International Studies Washington, DC, 2002.

J. Jang-Jaccard, S. Nepal, A survey of emerging threats in cybersecurity, J. Comput. Syst. Sci. 80 (2014) 973–993.

Command History, (n.d.). https://www.cybercom.mil/About/History/ (accessed April 10, 2023).

M. Bouke, A. Abdullah, Turnkey Technology: A Powerful Tool for Cyber Warfare, ArXiv Prepr. ArXiv2308.14576. (2023) 1–11. http://arxiv.org/abs/2308.14576.

National Cyber Strategy 2022 (HTML) - GOV.UK, (n.d.). https://www.gov.uk/government/publications/national-cyber-strategy-2022/national-cyber-security-strategy-2022#the-national-cyber-force (accessed April 10, 2023).

M.N. Schmitt, Tallinn manual on the international law applicable to cyber warfare, Cambridge University Press, 2013.

Mounting Cyber Threats Mean Financial Firms Urgently Need Better Safeguards, (n.d.). https://www.imf.org/en/Blogs/Articles/2023/03/02/mounting-cyber-threats-mean-financial-firms-urgently-need-better-safeguards (accessed April 6, 2023).

J. Jang-Jaccard, S. Nepal, A survey of emerging threats in cybersecurity, J. Comput. Syst. Sci. 80 (2014) 973–993. https://doi.org/10.1016/j.jcss.2014.02.005.

S.G. Coulson, Lanchester modelling of intelligence in combat, IMA J. Manag. Math. 30 (2019) 149–164. https://doi.org/10.1093/imaman/dpx014.

M. Tatam, B. Shanmugam, S. Azam, K. Kannoorpatti, A review of threat modelling approaches for APT-style attacks, Heliyon. 7 (2021) e05969. https://doi.org/10.1016/j.heliyon.2021.e05969.

I. Apostol, A Survey on Epidemiological Propagation Models of Botnets, J. Mil. Technol. 3 (2020) 29–36. https://doi.org/10.32754/JMT.2020.1.05.

F.A. Aboaoja, A. Zainal, F.A. Ghaleb, B.A.S. Al-rimy, T.A.E. Eisa, A.A.H. Elnour, Malware Detection Issues, Challenges, and Future Directions: A Survey, Appl. Sci. 12 (2022). https://doi.org/10.3390/app12178482.

A.M. del Rey, Mathematical modeling of the propagation of malware: a review, Secur. Commun. Networks. 8 (2015) 2561–2579.

Z. Sengul, C. Acarturk, Cyber Warfare Integration to Conventional Combat Modeling: A Bayesian Framework, 14th Int. Conf. Inf. Secur. Cryptology, ISCTURKEY 2021 - Proc. (2021) 1–6. https://doi.org/10.1109/ISCTURKEY53027.2021.9654297.

K.J. Huang, K.H. Chiang, Toward a Self-Adaptive Cyberdefense Framework in Organization, SAGE Open. 11 (2021). https://doi.org/10.1177/2158244020988855.

U. Urooj, B.A.S. Al-Rimy, A. Zainal, F.A. Ghaleb, M.A. Rassam, Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research Directions, Appl. Sci. 12 (2022). https://doi.org/10.3390/app12010172.

H. Oz, A. Aris, A. Levi, A.S. Uluagac, A survey on ransomware: Evolution, taxonomy, and defense solutions, ACM Comput. Surv. 54 (2022) 1–37.

A. Alqahtani, F.T. Sheldon, A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook, Sensors. 22 (2022) 1–19. https://doi.org/10.3390/s22051837.

S. Uebelacker, S. Quiel, The social engineering personality framework, Proc. - 4th Work. Socio-Technical Asp. Secur. Trust. STAST 2014 - Co-Located with 27th IEEE Comput. Secur. Found. Symp. CSF 2014 Vienna Summer Log. 2014. (2014) 24–30. https://doi.org/10.1109/STAST.2014.12.

M. Mittal, K. Kumar, S. Behal, Deep learning approaches for detecting DDoS attacks: a systematic review, Soft Comput. (2022). https://doi.org/10.1007/s00500-021-06608-1.

A. Pollini, T.C. Callari, A. Tedeschi, D. Ruscio, L. Save, F. Chiarugi, D. Guerri, Leveraging human factors in cybersecurity: an integrated methodological approach, Cogn. Technol. & Work. 24 (2022) 371–390.

M.A. Siddiqi, W. Pak, M.A. Siddiqi, A study on the psychology of social engineering-based cyberattacks and existing countermeasures, Appl. Sci. 12 (2022) 6042.

N. Yathiraju, G. Jakka, S.K. Parisa, O. Oni, Cybersecurity Capabilities in Developing Nations and Its Impact on Global Security: A Survey of Social Engineering Attacks and Steps for Mitigation of These Attacks, in: Cybersecurity Capab. Dev. Nations Its Impact Glob. Secur., IGI global, 2022: pp. 110–132.

J. Singh, J. Singh, A survey on machine learning-based malware detection in executable files, J. Syst. Archit. 112 (2021) 101861. https://doi.org/10.1016/j.sysarc.2020.101861.

X. Ling, L. Wu, J. Zhang, Z. Qu, W. Deng, X. Chen, Y. Qian, C. Wu, S. Ji, T. Luo, others, Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art, Comput. & Secur. (2023) 103134.

J. Singh, J. Singh, A survey on machine learning-based malware detection in executable files, J. Syst. Archit. 112 (2021) 101861. https://doi.org/10.1016/j.sysarc.2020.101861.

M.S. Abbasi, H. Al-Sahaf, M. Mansoori, I. Welch, Behavior-based ransomware classification: A particle swarm optimization wrapper-based approach for feature selection, Appl. Soft Comput. 121 (2022) 108744. https://doi.org/10.1016/j.asoc.2022.108744.

X. Yang, D. Yang, Y. Li, A Hybrid Attention Network for Malware Detection Based on Multi-Feature Aligned and Fusion, Electronics. 12 (2023) 713.

M.A. Bouke, A. Abdullah, S.H. ALshatebi, M.T. Abdullah, E2IDS: An Enhanced Intelligent Intrusion Detection System Based On Decision Tree Algorithm, J. Appl. Artif. Intell. (2022).

M.A. Bouke, A. Abdullah, S.H. ALshatebi, M.T. Abdullah, H. El Atigh, An intelligent DDoS attack detection tree-based model using Gini index feature selection method, Microprocess. Microsyst. 98 (2023) 104823. https://doi.org/10.1016/j.micpro.2023.104823.

G. Baldini, I. Amerini, Online Distributed Denial of Service (DDoS) intrusion detection based on adaptive sliding window and morphological fractal dimension, Comput. Networks. 210 (2022) 108923. https://doi.org/10.1016/j.comnet.2022.108923.

D.C. Can, H.Q. Le, Q.T. Ha, Detection of Distributed Denial of Service Attacks Using Automatic Feature Selection with Enhancement for Imbalance Dataset, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 2021: pp. 386–398. https://doi.org/10.1007/978-3-030-73280-6_31.

H. Lin, C. Wu, M. Masdari, A comprehensive survey of network traffic anomalies and DDoS attacks detection schemes using fuzzy techniques, Comput. Electr. Eng. 104 (2022) 108466. https://doi.org/10.1016/j.compeleceng.2022.108466.

A. Bhardwaj, V. Mangat, R. Vig, S. Halder, M. Conti, Distributed denial of service attacks in cloud: State-of-the-art of scientific and commercial solutions, Comput. Sci. Rev. 39 (2021) 100332. https://doi.org/10.1016/j.cosrev.2020.100332.

Published

2024-03-20

How to Cite

Bouke, M. A., & Abdullah, A. (2024). SMRD: A Novel Cyber Warfare Modeling Framework for Social Engineering, Malware, Ransomware, and Distributed Denial-of-Service Based on a System of Nonlinear Differential Equations. Journal of Applied Artificial Intelligence, 5(1), 54–68. https://doi.org/10.48185/jaai.v5i1.972

Most read articles by the same author(s)