Performance evaluation of a fast and efficient intrusion detection framework for advanced persistent threat-based cyberattacks

After the COVID-19 pandemic, cyberattacks are increasing as non-face-to-face environments such as telecommuting and telemedicine proliferate. Cyberattackers exploit vulnerabilities in remote systems and endpoint devices in major enterprises and infrastructures. To counter these attacks, fast detection and response are essential because advanced persistent threat (APT) attacks intelligently infiltrate endpoint devices for long periods and spread to large-scale environments. However, because conventional security systems are signature-based, fast detection of APT attacks is challenging, and it is difficult to respond flexibly to the environment. In this study, we propose an APT fast detection and response technique using open-source tools that improves the efficiency of existing endpoint information protection systems and swiftly detects the APT attack process. Performance test results based on realistic scenarios using the open-source APT attack library and MITER ATT&CK indicated that fast detection was possible with higher accuracy for the early stages of APT attacks in scenarios where endpoint attack detectors are interworking environments. © 2022 The Authors

Finite State Automata for Real-Time Health Electronic Record Update: A survey

The sudden exodus of healthcare worker has left many healthcare organizations with limited capabilities to provide efficient and continuous care for long-term patients. When the COVID-19 pandemic arose, society has witnessed the lack of sophisticated medical systems. An abundance of acute patients in long term care needing consistent monitoring has left the healthcare workforce to dwindle due to strict monitoring protocols and fatigue. Due to these global events, it has become apparent that more advanced methods of automated e-health processes should be implemented to relieve the distress within the workforce. As Internet of Things (IoT) becomes prominent within major commercial and military sectors, the interoperability between devices is increasing significantly. Intelligent medical devices and network connectivity in major healthcare organizations have been using IoT and cloud computing to maximize productivity and minimize workforce fatigue. Currently, the limitations of health monitoring systems and smart devices are data interoperability and human intervention errors. In this survey, we will review the selected Finite State Automata that are able to be implemented for real-time update to assist the major issues based on human intervention errors and a framework for updating e-health records. © 2022 IEEE.

An Enhanced Approach to Detect Covid-19 Patients and Level of Infection using FSM

The present situation is this world is pathetic and very uncertain. The killer disease Covid-19 dramatically turn down the regular activities of human beings. The normal life procedure of people is impacted. The society is highly stressed. To identifying covid 19 patients and their level of infection is a challenging task. In this paper proposes a Finite State Machine based classification model to identify the covid 19 patients and their level of infections. This model will help the physicians to diagnose and treat the patients in well advance. The experimental results shows the efficiency of the proposed system. © 2021 IEEE.