Predicting Software Vulnerability Trends with Multi-Recurrent Neural Networks: A Time Series Forecasting Approach (2024)

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.

## introduction in the digital age, cybersecurity threats have emerged as a formidable challenge, posing significant risks to organizational data and information systems. the rapid evolution of cyber-attack techniques, ranging from malware dissemination to sophisticated phishing campaigns, underscores the urgent need for advanced predictive models capable of preempting these threats @xcite . traditional methods in cybersecurity threat prediction, while effective to a degree, fall short in addressing the complexity and dynamism of modern cyber-attacks. this gap necessitates the exploration of innovative approaches that can adapt to the evolving landscape of cyber threats. recent advancements in artificial intelligence (ai) and machine learning (ml) have opened new avenues for cybersecurity, offering promising tools for enhancing threat prediction and response mechanisms. among these, recurrent neural networks (rnns) have shown potential in processing time-series data, which is pivotal in understanding and predicting cybersecurity incidents. however, rnns are not without limitations, particularly in handling long-term dependencies and the vanishing gradient problem, which significantly hampers their predictive performance @xcite @xcite . this paper introduces the multi-recurrent neural network (mrn) as a novel approach to overcome the limitations of traditional rnns in cybersecurity threat prediction. the mrn model integrates the strengths of various rnn architectures, incorporating enhanced memory mechanisms and a balanced complexity that allows for effective processing of time-series data without the overfitting risks associated with more complex models like lstms and grus. by applying the mrn model to a diverse set of datasets derived from recent cybersecurity incidents, this study aims to demonstrate the superior predictive capabilities of mrns in identifying potential cyber threats @xcite @xcite . ## results and discussion in this section, we present the results from using various predictive models, including arima, feedforward multilayer perceptrons (ffmlp), simple recurrent network (srn), long short-term memory (lstm), and multirecurrent neural network (mrn) for the task of software vulnerability volume prediction. various combinations of parameters and hyperparameters were tested to optimize the performance of each model. ## conclusion this paper has presented a comprehensive analysis of the application of multi-recurrent neural networks (mrn) for the prediction of software vulnerabilities, demonstrating significant advancements over traditional recurrent neural network (rnn) models and other machine learning approaches. through meticulous experiments and evaluations, we have established that mrns not only consistently outperform established models like lstms and srns across various metrics and settings but also offer substantial improvements in handling complex time-series data efficiently. the performance of mrns, particularly in shorter time windows and with fewer data points, underscores their potential in applications requiring quick, accurate forecasts with limited historical data. this is relevant in the rapidly evolving field of cybersecurity, where the ability to predict and respond to threats swiftly can drastically enhance protective measures. furthermore, the ability of mrns to perform with fewer parameters compared to more complex models like lstms implies a lower computational demand, making them suitable for deployment in environments with limited computational resources.