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

Exposure to airborne SARS-CoV-2 in four hospital wards and ICUs of Cyprus. A detailed study accounting for day-to-day operations and aerosol generating procedures.

In any infectious disease, understanding the modes of transmission is key to selecting effective public health measures. In the case of COVID-19 spread, the strictness of the imposed measures outlined the lack of understanding on how SARS-CoV-2 transmits, particularly via airborne pathways. With the aim to characterize the transmission dynamics of airborne SARS-CoV-2, 165 and 62 air and environmental samples, respectively, were collected in four COVID-19 wards and ICUs in Cyprus and analyzed by RT-PCR. An alternative method for SARS-CoV-2 detection in air that provides comparable results but is less cumbersome and time demanding, is also proposed. Considering that all clinics employed 14 regenerations per hour of full fresh air inside patient rooms, it was hypothesized that the viral levels and the frequency of positive samples would be minimum outside of the rooms. However, it is shown that leaving the door opened in patient rooms hinders the efficiency of the ventilation system applied, allowing the virus to escape. As a result, the highest observed viral levels (135 copies m-3) were observed in the corridor of a ward and the frequency of positive samples in the same area was comparable to that inside a two-bed cohort. SARS-CoV-2 in that corridor was found primarily to lie in the coarse mode, at sizes between 1.8 and 10 µm. Similar to previous studies, the frequency of positive samples and viral levels were the lowest inside intensive care units. However, if a patient with sufficient viral load (Ct-value 31) underwent aerosol generating procedures, positive samples with viral levels below 45 copies m-3 were acquired within a 2 m distance of the patient. Our results suggest that a robust ventilation system can prevent unnecessary exposure to SARS-CoV-2 but with limitations related to foot traffic or the operations taking place at the time.

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.

One-pot and rapid detection of SARS-CoV-2 viral particles in environment using SERS aptasensor based on a locking amplifier.

With the frequent detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in dwellings and wastewater, the risk of transmission of environmental contaminants is of great concern. Fast, simple and sensitive sensors are essential for timely detecting infection and controlling transmission through environment fomites. Herein, we developed a Surface Enhanced Raman Scattering (SERS) aptasensor, which can realize ultrasensitive and rapid assay of SARS-CoV-2 viral particles. In this strategy, we designed a novel locking amplifier which is activated only in the presence of virus by aptamer recognition. The reaction process was carried out though one-pot method at 37 °C, which can save time and resources. In addition, magnetic beads used in reaction system can simplify operation, as well as provide ideas for developing biosensing robots via magnetic field. This SERS aptasensor can detect SARS-CoV-2 virus with a LOD of 260 TU/µL within 40 min in the linear range of 625-10,000 TU/µL. Therefore, this convenience, speediness, sensitivity, and selectivity of detection has great prospects in analyzing SARS-CoV-2 viral particles or other viruses in environment as well as monitoring of environmental virus sources.