Hybrid Honeypot Malware Detection System Based On Machine Learning

Abstract

The latest wireless technology is expanding smart phone technology and burgeoning mobile cloud technology. Future mobile cloud computing will offer a lot of advantages, but it will also make it easy for hackers to take complete control of a lot of other users. Data privacy is essential. The biggest disadvantage for customers is that, even if data protection is supposed to be secure, when a computer is connected to the internet, a thief can quickly steal data from the targeted target. To improve security and mitigate both known and unknown attacks, a combination of Hybrid Intrusion Detection System (HyInt) and Honey pot networks has been incorporated to the Mobile Cloud Environment. Execution of the study work offers a clear viewpoint on the algorithm's security and high-quality outputs that was absent from earlier studies. Intensive statistical analysis was carried out as part of the research to demonstrate the consistency of the suggested algorithm. The results of the implementation and evaluation indicate that there is still much to learn about the cloud-based intrusion detection system. The developed technique can be used to efficiently track network activity in a highly secure cloud environment designed for use by the military and banks.