@inproceedings{orojo-etal-2024-predicting,
title = "Predicting Software Vulnerability Trends with Multi-Recurrent Neural Networks: A Time Series Forecasting Approach",
author = "Orojo, Abanisenioluwa K. and
Elumelu, Webster C. and
Orojo, Oluwatamilore O.",
editor = "Mitkov, Ruslan and
Ezzini, Saad and
Ranasinghe, Tharindu and
Ezeani, Ignatius and
Khallaf, Nouran and
Acarturk, Cengiz and
Bradbury, Matthew and
El-Haj, Mo and
Rayson, Paul",
booktitle = "Proceedings of the First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
month = jul,
year = "2024",
address = "Lancaster, UK",
publisher = "International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
url = "https://aclanthology.org/2024.nlpaics-1.5/",
pages = "42--47",
abstract = "Predicting software vulnerabilities effectively is crucial for enhancing cybersecurity measures in an increasingly digital world. Traditional forecasting models often struggle with the complexity and dynamics of software vulnerability data, necessitating more advanced methodologies. This paper introduces a novel approach using Multi-Recurrent Neural Networks (MRN), which integrates multiple memory mechanisms and offers a balanced complexity suitable for time-series data. We compare MRNs against traditional models like ARIMA, Feedforward Multilayer Perceptrons (FFMLP), Simple Recurrent Networks (SRN), and Long Short-Term Memory (LSTM) networks. Our results demonstrate that MRNs consistently outperform these models, especially in settings with limited data or shorter forecasting horizons. MRNs show a remarkable ability to handle complex patterns and long-term dependencies more efficiently than other models, highlighting their potential for broader applications beyond cybersecurity. The findings suggest that MRNs can significantly improve the accuracy and efficiency of predictive analytics in cybersecurity, paving the way for their adoption in practical applications and further exploration in other predictive tasks."
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<abstract>Predicting software vulnerabilities effectively is crucial for enhancing cybersecurity measures in an increasingly digital world. Traditional forecasting models often struggle with the complexity and dynamics of software vulnerability data, necessitating more advanced methodologies. This paper introduces a novel approach using Multi-Recurrent Neural Networks (MRN), which integrates multiple memory mechanisms and offers a balanced complexity suitable for time-series data. We compare MRNs against traditional models like ARIMA, Feedforward Multilayer Perceptrons (FFMLP), Simple Recurrent Networks (SRN), and Long Short-Term Memory (LSTM) networks. Our results demonstrate that MRNs consistently outperform these models, especially in settings with limited data or shorter forecasting horizons. MRNs show a remarkable ability to handle complex patterns and long-term dependencies more efficiently than other models, highlighting their potential for broader applications beyond cybersecurity. The findings suggest that MRNs can significantly improve the accuracy and efficiency of predictive analytics in cybersecurity, paving the way for their adoption in practical applications and further exploration in other predictive tasks.</abstract>
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%0 Conference Proceedings
%T Predicting Software Vulnerability Trends with Multi-Recurrent Neural Networks: A Time Series Forecasting Approach
%A Orojo, Abanisenioluwa K.
%A Elumelu, Webster C.
%A Orojo, Oluwatamilore O.
%Y Mitkov, Ruslan
%Y Ezzini, Saad
%Y Ranasinghe, Tharindu
%Y Ezeani, Ignatius
%Y Khallaf, Nouran
%Y Acarturk, Cengiz
%Y Bradbury, Matthew
%Y El-Haj, Mo
%Y Rayson, Paul
%S Proceedings of the First International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%D 2024
%8 July
%I International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%C Lancaster, UK
%F orojo-etal-2024-predicting
%X Predicting software vulnerabilities effectively is crucial for enhancing cybersecurity measures in an increasingly digital world. Traditional forecasting models often struggle with the complexity and dynamics of software vulnerability data, necessitating more advanced methodologies. This paper introduces a novel approach using Multi-Recurrent Neural Networks (MRN), which integrates multiple memory mechanisms and offers a balanced complexity suitable for time-series data. We compare MRNs against traditional models like ARIMA, Feedforward Multilayer Perceptrons (FFMLP), Simple Recurrent Networks (SRN), and Long Short-Term Memory (LSTM) networks. Our results demonstrate that MRNs consistently outperform these models, especially in settings with limited data or shorter forecasting horizons. MRNs show a remarkable ability to handle complex patterns and long-term dependencies more efficiently than other models, highlighting their potential for broader applications beyond cybersecurity. The findings suggest that MRNs can significantly improve the accuracy and efficiency of predictive analytics in cybersecurity, paving the way for their adoption in practical applications and further exploration in other predictive tasks.
%U https://aclanthology.org/2024.nlpaics-1.5/
%P 42-47
Markdown (Informal)
[Predicting Software Vulnerability Trends with Multi-Recurrent Neural Networks: A Time Series Forecasting Approach](https://aclanthology.org/2024.nlpaics-1.5/) (Orojo et al., NLPAICS 2024)
ACL