Leveraging AI for Automated Malware Classification and Detection in Large-Scale Networks
Authors: Rabie Ahmed  
Published: February 2025

Malware has been increasing exponentially, while cybersecurity threats, in general, are becoming more complex at the same time securing large networks becomes a challenge. Traditional techniques for detecting malware are not bad, but they often do not keep pace with the changing nature of malware. The paper investigates using Artificial Intelligence (AI) as a new classification/detection paradigm for malware and aims to automate some of the processes involved in improving security performance over large network infrastructures. The main aim however is to create an AI-based approach that enhances detection accuracy minimizes false positives and provides scalable solutions appropriate for high-volume real-time network environments.
The proposed study uses a static and dynamic malware analysis based on which important features are extracted to train machine learning as well as deep learning models. Signature CNNs may help detect layouts & GUI features and the RNN sequential data and temporal patterns are associated with malware behavior. The entire process includes a high-quality dataset curation from various trusted data sources, preprocessing, feature extraction, and splitting the data into train and test datasets to train respective models followed by validation. The proposed model was further tested by evaluating its performance metrics such as accuracy, precision, recall, and F1 score.
We achieved high accuracy and real-time capability in malware classification and detection using an AI-driven model. The study concluded that the use of deep learning architectures allows us to adapt to the ever-changing and evolving nature of malware, including those that are new and unknown with high precision. Simplicity also underpins the model's scalability, allowing for strong deployment in large networks, and contributing to a stronger cybersecurity framework.
The AI model suggested has great practical value for network administrators and cybersecurity professionals. By embedding this technology into actual security systems, we can balance out manual time-consuming work with an automated real-time responsive capability against malware attacks and thus improve the efficiency of response to possible incidents. In the model design, we allow for easy deployment in any architecture and the scalability can be attained through multiple nodes as network size/traffic volume increases.
The novelty of this paper lies in our introduction of a hybrid approach to malware detection integrated automation combining static and dynamic analysis in an AI framework designed for high-scale network applications. This integration and implementation of machine learning and deep learning models in this domain highlights a novel solution to the urgent trends of current malware detection. This work offers a new model that supports scalability and adaptability, extending existing studies into utilizing AI in network security while paving the ground for advanced automated solutions for cyber defense.
Introduction: Malware has become more sophisticated, threatening network infrastructures in finance, healthcare, and government. Traditional methods like signature-based and heuristic approaches struggle with new and complex variants. AI technologies, including machine learning and deep learning, offer adaptive solutions by processing large datasets and detecting unknown threats in real-time, enhancing network security and reducing human intervention. This study aims to develop a scalable AI-driven malware detection model for large-scale networks, focusing on improving accuracy, minimizing false positives, and evaluating performance in high-traffic environments.
Objectives: This study aims to develop a scalable AI-driven malware detection model tailored for large-scale networks. Key objectives include:

Enhancing classification accuracy and minimizing false positives through advanced AI techniques.
We are evaluating the model’s scalability and performance in high-traffic, real-time network environments.
It is identifying effective AI-based approaches for detecting novel and evolving malware threats. This research focuses on applying machine learning and deep learning techniques to create a dynamic malware detection framework that is both efficient and scalable.

Methods: To build a robust AI model for malware detection, a diverse and comprehensive dataset from sources like MalwareBazaar and VirusShare is used, focusing on diversity, data integrity, and scalability. Data preprocessing includes normalization, feature extraction, and data augmentation to enhance robustness. The framework combines CNNs for visual pattern analysis, RNNs for sequential data, and transformers for managing dependencies. The model architecture includes three CNN layers, two LSTM layers, and two self-attention layers, with parameters tuned through grid search. Feature engineering extracts static (metadata, opcode frequency, API calls) and dynamic (network activity, system calls, process creation) features. Training and validation use k-fold cross-validation, and performance is assessed using accuracy, precision, recall, and F1 score.
Results: The AI model achieved high detection accuracy and precision, outperforming traditional signature-based and heuristic methods. The model demonstrated high recall and F1 scores, effectively identifying a wide range of malware with minimal false negatives. Scalability was evaluated by deploying the model in simulated large-scale network environments, showing high detection rates and low latency under various loads.
Conclusions: The AI-based model outperforms traditional methods in malware detection, achieving high accuracy, precision, and recall for novel and polymorphic threats. Its low latency and resource consumption make it scalable for high-traffic networks. The model reduces false positives and negatives, enhancing network security and incident response efficiency. Contributions include an innovative hybrid architecture, enhanced feature engineering, and real-time adaptability.