Artificial Intelligence and Machine Learning in Healthcare Cybersecurity of Current Applications and Future Directions

Abstract

Data communication has increased due to the growth of technology-driven services and application deployment. Because there are always security risks to networks, the increasing throughput of networks has also increased the likelihood of cybercrimes. Additionally, the abrupt end of the COVID crisis, which caused nearly all services to go online, has increased awareness of the threat posed by cyberattacks in general. Thus, in the information and technology-driven world of today, reliable communication is crucial. The network is frequently discovered to be vulnerable to a variety of network attacks, such as spoofing, malware, and DDoS attacks. The difficulties caused by intrusions have also been covered in the current work, along with the methods to prevent and detect these kinds of intrusion attempts that have been under investigation for the past few years. Research needs have been identified by presenting and analysing the body of literature currently available on IDS. A hybrid approach has been suggested based on the information to provide a secure communication mechanism and protect the network from intruders. A cyber security strategy based on ABC and 3D CNN architecture is suggested as a solution to this. The qualities that reflect malware and DDoS attack aspects are chosen and optimised using the ABC. Neural networks are trained and classified using the retrieved data. The accuracy, recall, and f-measure analysis of the suggested cyber security method for malware and DDoS node detection were assessed. Comparative investigation demonstrated that the cyber security method was more effective at identifying network attacks.