Adware Detection using LSTM and Comparison with other ML Models

In recent years, the internet has become the predominant communication medium, paralleled by a surge in cyber threats where attackers actively exploit vulnerabilities to obtain sensitive information. Among the various malware tactics employed, Adware poses a significant challenge. Addressing this concern, cyber security specialists leverage Machine Learning (ML) techniques, with this paper proposing a novel Long Short-Term Memory (LSTM) algorithm for adware detection. The research employs a holistic methodology involving diverse data pre-processing techniques, feature selection, and ML algorithms to effectively identify adware samples within the dataset. A comparative analysis of ML classifiers, including Random Forest (RF), k - Nearest Neighbors (k-NN), Decision Tree (DT), and Logistic Regression (LR), reveals optimal detection accuracies of 98.66%, 98.10%, and 98.05% for DT and k-NN, respectively. These findings underscore the efficacy of the proposed approach in fortifying cyber security against adware threats.