AI and Blockchain-based source code vulnerability detection and prevention system for multiparty software development

Nath, Prem; Mushahary, Jaya Rani; Roy, Ujjal; Brahma, Maharaj; Singh, Pranav Kumar

doi:10.1016/j.compeleceng.2023.108607

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…Nath et al proposed a score range map that identi es vulnerability to classify the source code of the software. There are encouraging quality reviews and controls of lead developers are also made available for review, with this scoring map, [31]. Bayram et al obtained systematic literature searches published in the eld of agile software development from Web of Science, Science Direct, Scopus and IEEE academic databases.…”

Section: Resultsmentioning

confidence: 99%

Secure Agile Software Development with Scrum Strategy

Kasım

2023

Preprint

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The implementation of software development strategies while developing a software is important for a project's roadmap. In traditional methods, the project is defined before the start of the project, delivery times and costs are determined. After each of the project development stages is completed, another development stage is started. However, communication is important in software projects developed by agile methods and processes are more flexible. It is possible to make innovation or changes at any time of the project. Scrum is an important strategy used for agile software development. Scrum is an important strategy used for agile software development. When the Scrum strategy is examined, it can be seen that agility and safety principles do not overlap at some points. At this point, models that allow security activities to cooperate with agile methods and work within the framework of Scrum come to the fore. In this study, studies to eliminate this incompatibility based on scientific evidence were analyzed. In this analysis, results of the Bibliometric Network Analysis and Co-Occurrence Network Construction for software development of the Scrum model were revealed. These results show that security processes have a place in the Scrum field. The inclusion of security in the process during the software development phase will enable a more robust system design in response to future security vulnerabilities.

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Section: Resultsmentioning

confidence: 99%

Secure Agile Software Development with Scrum Strategy

Kasım

2023

Preprint

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“…As explained in [45] and [46], testing is a critical phase to ensure that the software meets the specified requirements and functions correctly. It involves creating and executing test cases, identifying defects or bugs, and verifying that the software performs as intended.…”

Section: Figure 1 Typical Sdlc Modelmentioning

confidence: 99%

“…Software development is the process of creating, designing, coding, testing, and maintaining software systems [1]- [3]. It involves a combination of technical skills, problem-solving abilities, and creative thinking to translate concepts and requirements into functional software applications.…”

Section: Introductionmentioning

confidence: 99%

Theory and practice in secure software development lifecycle: A comprehensive survey

Otieno¹,

Odera²,

Ounza³

2023

World J. Adv. Res. Rev.

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Software development security refers to the practice of integrating security measures and considerations throughout the software development lifecycle to ensure the confidentiality, integrity, and availability of software systems. It involves identifying, mitigating, and eliminating security vulnerabilities and threats that could be exploited by attackers. The goal of this paper is to survey the various concepts and methodologies directed towards software security, and the identification of any missing gaps. Based on the findings, it is noted that the development of secure software requires a proactive and comprehensive approach. It begins with establishing secure design principles and incorporating security requirements from the initial stages of development. Here, secure coding practices, such as input validation, output encoding, and secure authentication and authorization mechanisms, are employed to prevent common security vulnerabilities. In addition, regular security testing, including penetration testing and vulnerability scanning, helps identify and address potential weaknesses in the software. Normally, code reviews and security audits are conducted to ensure adherence to secure coding practices and identify any security flaws. It is important that security training and awareness programs be provided to developers and other stakeholders to foster a security-conscious culture. To minimize potential vulnerabilities, secure configuration management, which involves properly configuring servers, networks, and dependencies may be utilized. On the other hand, regular updates and patching are essential to address known security vulnerabilities in software components. To guide their software development security practices, organizations may follow established security standards and frameworks such as ISO 27001 or NIST Cybersecurity Framework. By prioritizing software development security, organizations can protect sensitive data, prevent unauthorized access, and mitigate the risk of security breaches and incidents. In the long run, this helps build trust with users and stakeholders, enhances the reputation of the software, and reduces the potential impact of security incidents on the organization.

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“…To alleviate the storage and computational burden in the blockchain, ref. [19] proposes the use of the IPFS as an off-chain auxiliary storage for model files [43], reserving the blockchain solely for recording model-related information. Moreover, ref.…”

Section: Current Research Status Of Model Services For Deep Learningmentioning

confidence: 99%

Toward Sustainable Model Services for Deep Learning: A Sub-Network-Based Solution Integrating Blockchain with IPFS and a Use Case in Intelligent Transportation

Jiang,

Li,

et al. 2023

Sustainability

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In the era of deep learning as a service, ensuring that model services are sustainable is a key challenge. To achieve sustainability, the model services, including but not limited to storage and inference, must maintain model security while preserving system efficiency, and be applicable to all deep models. To address these issues, we propose a sub-network-based model storage and inference solution that integrates blockchain and IPFS, which includes a highly distributed storage method, a tamper-proof checking method, a double-attribute-based permission management method, and an automatic inference method. We also design a smart contract to deploy these methods in the blockchain. The storage method divides a deep model into intra-sub-network and inter-sub-network information. Sub-network files are stored in the IPFS, while their records in the blockchain are designed as a chained structure based on their encrypted address. Connections between sub-networks are represented as attributes of their records. This method enhances model security and improves storage and computational efficiency of the blockchain. The tamper-proof checking method is designed based on the chained structure of sub-network records and includes on-chain checking and IPFS-based checking stages. It efficiently and dynamically monitors model correctness. The permission management method restricts user permission based on the user role and the expiration time, further reducing the risk of model attacks and controlling system efficiency. The automatic inference method is designed based on the idea of preceding sub-network encrypted address lookup. It can distribute trusted off-chain computing resources to perform sub-network inference and use the IPFS to store model inputs and sub-network outputs, further alleviating the on-chain storage burden and computational load. This solution is not restricted to model architectures and division methods, or sub-network recording orders, making it highly applicable. In experiments and analyses, we present a use case in intelligent transportation and analyze the security, applicability, and system efficiency of the proposed solution, particularly focusing on the on-chain efficiency. The experimental results indicate that the proposed solution can balance security and system efficiency by controlling the number of sub-networks, thus it is a step towards sustainable model services for deep learning.

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AI and Blockchain-based source code vulnerability detection and prevention system for multiparty software development

Cited by 12 publications

References 6 publications

Secure Agile Software Development with Scrum Strategy

Secure Agile Software Development with Scrum Strategy

Theory and practice in secure software development lifecycle: A comprehensive survey

Toward Sustainable Model Services for Deep Learning: A Sub-Network-Based Solution Integrating Blockchain with IPFS and a Use Case in Intelligent Transportation

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