HAVAE – An Advanced Approach for Malware Detection Using Deep Learning

The dynamic situation of cybersecurity necessitates continuous adaptation to the evolving and sophisticated nature of malware. This study proposes an innovative approach to enhancing threat detection methodologies by combining Adversarial Autoencoders (AAEs) and Variational Autoencoders (VAEs) for unsupervised malware detection. AAEs, with their EncoderDecoder structure and adversarial techniques, are integrated with VAEs to discern latent representations which are crucial for discriminating between malware and harmless software. This model, referred to as Hybrid Adversarial-Variational Autoencoder (HAVAE), takes advantage of both of their strengths architectures, capturing nuanced features within a latent space through unsupervised learning. The HAVAE model employs the Reparameterization Technique, crucial for sampling latent variables, ensuring the generation of realistic samples while retaining discriminative attributes essential for accurate malware identification. Through comprehensive evaluations across diverse datasets, the efficiency of HAVAE is assessed using metrics encompassing precision, recall, and F1-score. The evaluation underscores the model's robust ability to detect malicious software effectively, emphasizing its potential as a versatile cybersecurity tool. This innovative approach represents a revolution in cybersecurity, utilizing the strength of unsupervised learning techniques, AAEs, and VAEs. The findings signify a significant advancement in adaptive and resilient malware detection systems, illuminating pathways for improved threat identification and mitigation in the ever-evolving cybersecurity landscape