Two Factor Worm Detection Based on Signature & Anomaly

Cybersecurity threats constantly evolve, necessitating robust detection mechanisms. This study proposes a two-factor approach combining signature-based and anomaly-based detection systems for identifying packet-based attacks. Leveraging machine learning algorithms—Decision Trees, Random Forest, and GaussianNB—the system aims to enhance detection accuracy and efficiency. Signature-based detection relies on known patterns, while anomaly-based detection identifies deviations from established norms. The study evaluates the performance of these algorithms in detecting attacks on network packets. Through comprehensive experimentation and analysis, this research assesses the efficacy of each algorithm in accurately pinpointing various types of attacks. Results showcase the strengths and limitations of each model in detecting sophisticated attacks. The findings underscore the importance of a hybrid approach, harnessing the strengths of both signature and anomaly detection, and demonstrate the potential of machine learning algorithms in fortifying network security against evolving cyber threats. 

 Keywords: Decision Tree, Random Forest, GaussianNB.