Open Access

An Integrated Security Analysis Model for Identifying Software and Hardware System Vulnerabilities

https://doi.org/10.55640/ijmcsit-v03i10-02

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Nguyen Minh Khoa ¹ , Tran Ngoc Anh ¹ ,

⁴ Department of Computer Science, Eastern Asia Institute of Technology, Hanoi, Vietnam

⁴ Department of Information Systems, Eastern Asia Institute of Technology, Hanoi, Vietnam

Abstract

The increasing interdependence of software, hardware, cloud infrastructures, embedded platforms, and connected systems has transformed cybersecurity vulnerability assessment from an isolated technical activity into a multidimensional security requirement. Software vulnerabilities can arise from insecure code, configuration weaknesses, vulnerable dependencies, and exploitable application interfaces, whereas hardware vulnerabilities may originate from architectural design limitations, processor-level weaknesses, implementation flaws, and hardware-assisted attack mechanisms. This study develops an integrated security analysis model for identifying vulnerabilities across software and hardware system layers. The proposed approach synthesizes vulnerability taxonomy, attack-surface analysis, risk assessment, exploitability evaluation, hardware security analysis, machine-learning-assisted detection, and adaptive defensive mechanisms. The methodology is conceptually grounded in the supplied literature and uses a layered assessment process to connect vulnerability discovery with exploitability, potential impact, and mitigation priority. Particular attention is given to base-image vulnerabilities, processor-level attacks, artificial-intelligence-supported security analysis, HTTPS deployment weaknesses, embedded-system risks, and cyber-hunting relationships between threats and defensive weaknesses. The analysis indicates that isolated software or hardware assessment can overlook cross-layer attack paths, while an integrated model provides stronger contextual understanding of vulnerabilities and their operational consequences. The study further identifies the need for risk-oriented prioritization, continuous monitoring, and coordinated software-hardware defenses. The proposed model provides a conceptual foundation for systematic vulnerability assessment in heterogeneous computing environments.

Keywords

Software Vulnerability, Hardware Security, Vulnerability Assessment

References

George P. G. and Renjith V. R., (2021) Evolution of safety and security risk assessment methodologies towards the use of bayesian networks in process industries, Process Safety and Environmental Protection, 2021.

Gelernter N., Schulmann H., and Waidner M., (2024) External Attack-Surface of Modern Organizations, in Proceedings of the 19th ACM, 2024.

Ghelani D., Hua T. K., and Koduru S. K. R., (2022) Cyber security threats, vulnerabilities, and security solutions models in banking, Authorea Preprints, 2022.

Giannuzzi S., (2022) Artificial Intelligence for Security Attacks Detection, 2022.

Habbal A., Ali M. K., and Abuzaraida M. A., (2024) Artificial Intelligence Trust, risk and security management (AI trism): Frameworks, applications, challenges, and future research directions, Expert Systems with Applications, 2024.

Hanif H., Nasir M. H. N. M., Ab Razak M. F., and Firdaus A., (2021) The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches, Journal of Network and ...,Elsevier, 2021.

Haque, M. U., & Babar, M. A. (2022, March). Well begun is half done: An empirical study of exploitability & impact of base-image vulnerabilities. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (1066-1077). IEEE.

Hasan S., Ali M., Kurnia S., and Thurasamy R., (2021) Evaluating the cyber security readiness of organizations and its influence on performance, Journal of Information Security, Elsevier, 2021.

Hemberg, E., Kelly, J., Shlapentokh-Rothman, M., Reinstadler, B., Xu,K., Rutar, N., & O'Reilly, U. M. (2020). Linking threat tactics, techniques, and patterns with defensive weaknesses, vulnerabilities, and affected platform configurations for cyber hunting. arXiv preprint arXiv:2010.00533.

Hu Q., Asghar M. R., and Brownlee N., (2021) A large-scale analysis of HTTPS deployments: Challenges, solutions, and recommendations, Journal of Computer Security,2021.

Hu, W., Chang, C. H., Sengupta, A., Bhunia, S., Kastner, R., & Li, H. (2020). An overview of hardware security and trust: Threats, countermeasures, and design tools. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 40(6), 1010-1038.

Hu Z., Chen P., Zhu M., and Liu P., (2021) A co-design adaptive defense scheme with bounded security damages against heartbleed-like attacks, in Forensics and Security, 2021.

Hubbard D. W., (2020) The failure of risk management: Why it's broken and how to fix it, 2020.

Javaid M., Haleem A., Singh R. P., and Suman R., (2023) Towards insighting cybersecurity for healthcare domains: A comprehensive review of recent practices and trends, Cyber Security and Applications, 2023.

Jimmy, F. N. U. (2024). Cyber security Vulnerabilities and Remediation Through Cloud Security Tools. Journal of Artificial Intelligence General science (JAIGS)ISSN: 3006-4023, 2(1), 129-171.

Kalla D., Kuraku S., and Samaah F., (2023) Advantages, disadvantages and risks associated with chatgpt and ai on cybersecurity, Journal of Emerging Technologies, 2023.

Kazemi Z., Fazeli M., and Hely D. (2020) Hardware security vulnerability assessment to identify the potential risks in a critical embedded application, in 2020 IEEE 26th, 2020.

Kitchin R. and Dodge M., (2020) The (in) security of smart cities: Vulnerabilities, risks, mitigation, and prevention, Smart cities and innovative Urban, 2020.

Kocher P., Horn J., Fogh A., Genkin D., Gruss D., (2020) Spectre attacks: Exploiting speculative execution, Communications ofthe, 2020.

Kornaros G., (2022) Hardware-assisted machine learning in resource-constrained IoT environments for security: review and future prospective, IEEE Access, 2022.

Kostromitin K. I., Dokuchaev B. N., (2020) Analysis of the Most Common Software and Hardware Vulnerabilities in Microprocessor Systems, 2020 International ..., 2020.

Kumar E. P., Priyanka S., and Sudhakar T., (2022) A review on vulnerabilities to modern processors and its mitigation for various variants, Procedia Computer Science, 2022.

Li C. and Gaudiot J. L., (2021) Detecting spectre attacks using hardware performance counters, IEEE Transactions on Computers, 2021.

Li H., Wang S. X., Shang F., and Niu K., (2024) Applications of large language models in cloud computing: An empirical study using real-world data, International Journal of ..., 2024.

Liu L, Guo Y., Cheng Y., and Zhang Y. (2022) Generating robust DNN with resistance to bit-flip based adversarial weight attack, IEEE Transactions on, 2022.

Lu G., Liu Y., Chen Y., Zhang C., and Gao Y., (2020) A comprehensive detection approach of wannacry: principles, rules, and experiments, in Conference on Cyber, 2020.

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An Integrated Security Analysis Model for Identifying Software and Hardware System Vulnerabilities

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