Machine learning-based security active defence model - security active defence technology in the communication network

摘要

Nowadays, there is anticipated exponential growth in the number of internet-enabled devices, which will increase cyber threats across an expanding attack surface area. For guaranteeing network security, this study proposes the Machine Learning-based Security Active Defence Model (MLSADM). Machine Learning (ML) is how artificial intelligence learns patterns that lead to abnormal behaviour. This occurs when an artificial intelligence system or neural network is deceived into imperfectly recognising or intentionally adapting the input. An emerging type of attack and a coordinated attack is measured in the attack and defence situation. Experiments have been carried out with the dynamic (learning) attacker of a static (fixed) defender, static attackers and active attackers. The findings show that the proposed model enhances network security with high accuracy in network abnormal behaviour detection compared to other popular methods.