Deep Learning for Malware Detection: Enhancing cybersecurity with Advanced Classification Models

Abstract

The rapid propagation of malware presents a important test to global cybersecurity, demanding cultured detection methods talented of growing with initial threats. Traditional signature based methods are stressed to keep step with increasingly advanced malware alternatives, nessitating innvoative solutions that can classify both known and unknown therats with high accuarcy. This study discovers the use of deep learning procedures for enhancing malware detection capabilites, paying preprocessing approaches such as normalization and Term Frequency-Inverse Document Frequency (TF-IDF) for active feature engineering specifically directing on API calls as serious pointers of an submission’s behaviour. A Long Short Term Memory (LSTM) network is used to model the time-based dependencies inhert in malware behaviors, enabling the identification of strange sequences telling of malicious movement. By integrating advanced preprocessing, feature engineering, and deep learning, the proposed systems enhances detection accuracy, reduces false positive, and improves flexibility against complication technique used by cyber criminals. The findings suggest that incorporating LSTM networks, combined with effective feature engineering, significantly boosts the capability of malware detection systems conducive to a robust protection against developing cyber threats and a safer digital environment.