Revolutionizing Authentication: Harnessing Natural Language Understanding for Dynamic Password Generation and Verification (2023)

Abstract: In our interconnected digital ecosystem, API security is paramount. Traditional static password systems once used for API authentication, face vulnerabilities to cyber threats. This paper explores Natural Language Understanding (NLU) as a tool for dynamic password solutions, achieving 49.57% accuracy. It investigates GPT-2 for dynamic password generation and innovative NLU-based verification using a set of specific criteria and threshold adjustments. The study highlights NLU’s benefits, challenges, and prospects in enhancing API security. This approach is a significant stride in safeguarding digital interfaces amidst evolving Cyber Security threats. Keywords: Cyber Security, Authentication, API Security, Generative AI, Dynamic Passwords, Passwords Verification, NLU

## introduction in the modern era of extensive digital interactions and interconnected systems, the security of application programming interfaces (apis) stands as a linchpin. apis serve as conduits for the exchange of data and services between software applications, making their protection pivotal for ensuring the privacy and integrity of transmitted information. the cornerstone of api security is its authentication mechanism, ensuring access solely to authorized entities. while historically reliant on static passwords, this approach, though effective to a degree, has exhibited vulnerabilities in the face of evolving cyber threats. static passwords are prone to various vulnerabilities including brute force attacks, breaches, and human negligence. as the digital landscape continues to evolve, the imperative for a more adaptive approach to api security is propelling the conception of dynamic passwords, fundamentally integrating the essence of natural language understanding (nlu). dynamic passwords represent a paradigm shift from fixed char-acter combinations to a more adaptable, contextaware authentication model. nlu, a subfield of artificial intelligence, empowers systems to interpret, understand, and respond to natural language inputs, heralding a promising avenue for intuitive and secure authentication. this paper will delve into the realms of dynamic password generation and verification through nlu, underpinning its critical role in the broader api security landscape. the paper is structured into several sections, each serving a specific purpose. in section 2, we talk about the api authentication landscape, covering traditional and dynamic password methods. section 3 focuses on nlu and its authentication role. section 4 contains a literature review, while sections 5 and 6 elaborate on dynamic password generation and verification using nlu. section 7 outlines the methodology, and section 8 presents our findings. we explore the security and user experience implications in section 9, followed by challenges in section 10. lastly, section 11 concludes the paper, summarizing findings and future directions. ## related work this study @xcite explores the influence of native languages on password composition and security, applying socioculture theory principles. users generated passwords influenced by both english and native languages, with native language-oriented passwords demonstrating higher security. 107 namibian and south african university students participated, with levenshtein's edit distance, language experts, and a passwordguessing algorithm used for analysis. the study highlights the importance of contextual factors in password design but doesn't delve into the effectiveness of different security measures. the study is limited to a specific demographic and may not generalize to other populations. the paper (hong and lee, 2022) introduces a deep learning model for password security evaluation, emphasizing password leakage prediction. the deep learning model achieved a high accuracy of 95.74% in predicting password leakage. the model was trained on a diverse password dataset and evaluated for its prediction accuracy. the study doesn't explore the practical implementation challenges or user adoption of such a model. the model's real-world performance may differ from its experimental accuracy. the research @xcite addresses password security from a human factors perspective, offering guidelines to motivate users to create secure and memorable passwords. users provided with persuasive messages and creation methods generated stronger and more memorable passwords. the study involved dividing participants into experimental and control groups and assessing password strength. the paper doesn't extensively explore the scalability of persuasive messages in large-scale applications. the study primarily focuses on the user's initial password creation and does not consider password management or changes over time. the paper @xcite introduces the "dynamic password protocol" for user authentication, involving static and dynamic passwords. the proposed protocol aims to enhance internet security by using dynamic passwords and protecting against various cyber threats. the protocol is described conceptually, emphasizing the dynamic aspect of passwords. the paper lacks empirical evidence or detailed implementation procedures. the real-world feasibility and scalability of this protocol are not discussed. the research @xcite focuses on enhancing security in wireless body area networks (wbans) by adopting dynamic password. the proposed scheme offers comprehensive security features, including dynamic password computation and low energy consumption. the paper uses the ieee 802.15.6 standard to evaluate the scheme's energy consumption and security performance. the study doesn't address potential challenges in deploying this scheme in real-world wbans. the scheme's performance may vary in diverse wban scenarios. this paper @xcite introduces a pseudo-dynamic password scheme for user authentication, enhancing security. the scheme provides varying passwords for each session, even when the actual user password remains the same. the paper analyzes the factors influencing password computation time and susceptibility to common attacks. the study does not delve into the scalability of the proposed scheme or its real-world performance. the scheme's effectiveness may depend on the specific implementation and threat landscape. the paper @xcite explores the use of large language models (llms) for password generation, introducing passgpt. passgpt outperforms existing methods in guessing previously unseen passwords and allows for guided password generation. passgpt is trained on password leaks and its password generation capabilities are assessed. the paper doesn't discuss potential limitations or ethical concerns related to using llms for password generation. the practical applicability and real-world security of passgpt remain unaddressed. ## dynamic password generation with nlu our methodology for dynamic password generation using the natural language understanding (nlu) model involved several key steps. firstly, we preprocessed a large dataset containing text samples, ensuring it was tokenized and well-structured. following that, we selected a pre-trained language model, gpt-2, as our nlu model for text generation @xcite . with this model in place, we proceeded to generate dynamic passwords by utilizing the inherent text-generation capabilities. generated passwords underwent a final fine-tuning phase to align them with the specific security and complexity criteria. the dataset used to train our model is from the publicly available password leaked for ethical considerations as done by @xcite @xcite algorithm 1 dynamic password generation using nlu model-gpt-2 here is a breakdown of algorithm 1. preprocess the input dataset: in this step, we prepare our input dataset for dynamic password generation. this involves breaking down the dataset into smaller components (tokens) and structuring it to make it compatible with our nlu model. for example, if our input dataset contains phrases like "make my password secure," we preprocess it into separate words and phrases, making it suitable for the model to understand. select the nlu model: we choose the nlu (natural language understanding) model for generating dynamic passwords. one popular choice is gpt-2, a language model known for its textgeneration capabilities. this model will be used to create dynamic passwords based on the input data. generate dynamic passwords using the nlu model: with the nlu model selected, we feed it the preprocessed input dataset. the model analyzes the input and generates dynamic passwords as output. for instance, if the input dataset con-tains phrases related to security, the model might generate a dynamic password like "secur!ty2023" based on the context and patterns it learned. fine-tune generated passwords for security and complexity: while the nlu model produces passwords, they may need further fine-tuning to ensure they meet security and complexity requirements. we evaluate the generated passwords based on criteria such as length, character diversity, and adherence to security policies. for example, we might adjust the passwords to meet a minimum length of 12 characters and include a mix of uppercase, lowercase, numbers, and special characters. our approach to dynamic password verification using the nlu model began with data preparation. we collected the user's input for password verification, which included the dynamic password, behavioral biometrics data, and session data. next, we preprocessed the verification input, ensuring its readiness for analysis. semantic matching was performed to assess the similarity between the preprocessed input and the dynamic password. we then delved into behavioral biometrics analysis, which involved scrutinizing the user's behavioral data. continuous authentication, integrating session data and behavioral data, played a pivotal role in the verification process. finally, we determined the verification result by considering semantic matching, behavioral biometrics, and continuous authentication. here is a breakdown of algorithm 2. collect verification input: for dynamic password verification, we collect various pieces of input. these include the dynamic password itself, the user's behavioral biometrics data (e.g., typing speed, keystroke dynamics), and session data (e.g., login timestamps, device information). preprocess the verification input: just like in the generation process, we preprocess the collected verification input. this step ensures that the data is correctly formatted and ready for analysis. semantic matching: in this step, we perform semantic matching between the preprocessed verification input and the dynamic password. we compare the meaning and context of the input with the generated password. if there is a significant semantic match, it indicates a potential successful verification. for example, if the dynamic password is "secur!ty2023" and the verification input context is related to security, a semantic match is established. analyze user's behavioral biometrics data: we analyze the user's behavioral biometrics data collected during the verification process. this data may include how the user types, the rhythm of keystrokes, and other unique behavioral patterns. by comparing this data to the user's established patterns, we can determine if the input aligns with the user's behavior. implement continuous authentication: continuous authentication ensures that the user remains authenticated throughout the session. it takes into account session data and ongoing behavioral data analysis. for example, it checks if the user's typing speed remains consistent and matches the patterns established during login. determine the verification result: after considering semantic matching, behavioral biometrics, and continuous authentication, we determine the verification result. if all criteria align, the result is "true," indicating successful verification. if any of these criteria do not match or if continuous authentication fails, the result is "false," indicating unsuccessful verification. ## results and findings in the realm of dynamic password generation, we generated a set of passwords using our nlu model. these passwords, which were shaped by the model's linguistic capabilities, were rigorously assessed based on complexity and security criteria. our findings indicate that the generated passwords exhibited a high degree of security and complexity, aligning with the intended standards for robust password generation. upon analyzing our findings, it is evident that our nlu-based model for dynamic password generation and verification presents a robust and in-novative approach to bolstering security in various applications which still has a gigantic scope for improvement. while the verification model demonstrated substantial accuracy, the occurrence of false positives underscores the need for fine-tuning the threshold to optimize performance. as our hypothesis suggested, the nlu-based model appears to outperform traditional methods in certain aspects, supporting the case for its adoption in enhancing security. our nlu model boasts versatile applications in the domain of api security. in the context of healthcare apis, the model can be employed for user authentication, and safeguarding sensitive patient data. e-commerce platforms can harness its capabilities for securing financial transactions and ensuring the integrity of customer accounts. social media apis can benefit from the nlu model to thwart unauthorized access and protect user information. these use cases underscore the model's significance in fortifying api security across diverse sectors. confusion matrix and observations: the confusion matrix provides a clear overview of the nlubased verification model's performance. in in figure 2 , we observe that the receiver operating characteristic (roc) curve provides valuable insights into the performance of our nlu-based dynamic password verification model. in our updated roc curve, we observe the following key points. the roc curve showcases the trade-off between the true positive rate (tpr) and the false positive rate (fpr). a point closer to the top-left corner indicates a high tpr but also a high fpr, while a point closer to the top-right corner suggests a low fpr but at the expense of a lower tpr. in our case, the roc curve area (auc) is 49.57%, indicating that our model performs reasonably well in distinguishing valid passwords from invalid ones based on the selected criteria. the roc curve allows us to adjust the model's threshold to balance sensitivity and specificity according to our specific security and usability requirements. it serves as a valuable tool for evaluating and fine-tuning the model, with the potential for further enhancements by modifying parameters or collecting additional data. ## implementation challenges implementing natural language understanding (nlu) in dynamic password systems for api authentication presents multifaceted challenges that demand meticulous attention and innovative solutions. ## conclusion this study underscores the importance of adapting to the dynamic cyber security landscape, where traditional static password systems have fallen short in ensuring robust api security. the application of natural language understanding (nlu), particularly through the use of gpt-2 for dynamic password generation and verification, offers a promising solution. our achieved accuracy of 49.57% demonstrates the viability of nlu-driven dynamic passwords. in the future, research in this domain can focus on further enhancing the accuracy of nlu-based dynamic password systems. exploration of advanced nlu models and continuous fine-tuning can contribute to more secure and efficient api authentication. additionally, efforts can be directed towards standardizing criteria and thresholds for dynamic password verification across various domains, ultimately advancing api security on a broader scale. moreover, investigating potential vulnerabilities and potential adversarial attacks on nlu-based systems is crucial for comprehensive protection. as technology evolves, embracing nlu in api security will remain a dynamic and essential field of research and development.