Classification of malware using multinomial linked latent modular double q learning

In recent times, malware has progressed by utilizing distinct advanced machine learning techniques for detection. However, the model becomes complicated and singular value decomposition depth-based detectors failed to detect significantly with minimum time overhead. This paper proposes a multinomial linked latent dirichlet modular double q (MLLD-MDQL) efficiently based on network behavior patterns. First, extraction (ML-LDNBE) is applied input anomaly detection that extracts pattern. The extracted which are grouped perform path protocol analyzing repeated behaviors. Finally, classification behaviors significant effectiveness of proposed MLLD-M DQL method compared existing models. results obtained show combined (ML) determined also reduced false positive rate (FPR). showed 42% better when 62% FPR.