Determining Accuracy Rate of Artificial Intelligence Models using Python and R-Studio

Gupta, Ashulekha; Parmar, Rishabh; Suri, Pradeep; Kumar, Rajiv

doi:10.1109/icac3n53548.2021.9725687

Cited by 25 publications

(1 citation statement)

References 9 publications

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“…In MLP, the activation function is the hyperbolic tangent activation function for being a good choice in many applications [42]. On the other hand, the solver used for weight optimization is 'lbfgs', an optimizer in the family of quasi-Newton methods which converges faster and performs better than others for not so large datasets [43]. Indeed, other solvers have been tested and 'lbfgs' produces better results.…”

Section: B Ai Processingmentioning

confidence: 99%

Vulnerability detection under poisoning attacks through code and token features

González-Manzano,

Garcia-Alfaro

2024

Preprint

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The complexity of implementations and the interconnection of assorted systems and devices facilitates the emergence of vulnerabilities. Detection systems are developed to fight against this security issue, being the use of Artificial Intelligence (AI) a common practice. However, the use of AI is not without its problems, specially those affecting the training phase. This paper tackles this issue following a two-fold approach. First, an AI-based vulnerability detection system based on code and token metrics, dubbed VulCoT, is developed. It reaches state-of-the-art performance while being suitable for C#, C/C++ and PHP. Second, the impact of poisoning attacks on VulCoT is analysed. Results show that VulCoT is specially affected beyond 20% of false data. Remarkably, detecting some of the most frequent Common Weakness Enumeration is altered even with lower poison rates. Overall, KNN and SVM are more appropriate for system protection in C# and C/C++, while MLP in PHP. Indeed, PHP is the language which is less affected by attacks, while C# and C/C++ present comparable results.

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Section: B Ai Processingmentioning

confidence: 99%