Data-Driven Decision-Making in Health Security Management: Challenges and Opportunities

The incorporation of data-driven decision-making into health security management has altered the method of recognizing, avoiding, and responding to health risks. This study investigates the influence of data-driven tactics on the efficacy of health-care security measures, demonstrating substantial advantages such as faster detection and reaction times. However, it also notes significant hurdles, such as assuring data is accurate and reliable, safeguarding patient privacy, and establishing appropriate technology and training. Ethical factors, such as data security and the possibility of misuse, are crucial to sustaining public confidence. The study indicates that, while data-driven decision-making has many benefits, addressing these difficulties through strong data governance frameworks and constant review is critical for optimizing health security management. Recommendations include improving data security standards and establishing a technology to enable successful data utilization

Preventing Distributed Denial-of-Service Attacks on the IMS Emergency Services Support through Adaptive Firewall Pinholing

Emergency services are vital services that Next Generation Networks (NGNs) have to provide. As the IP Multimedia Subsystem (IMS) is in the heart of NGNs, 3GPP has carried the burden of specifying a standardized IMS-based emergency services framework. Unfortunately, like any other IP-based standards, the IMS-based emergency service framework is prone to Distributed Denial of Service (DDoS) attacks. We propose in this work, a simple but efficient solution that can prevent certain types of such attacks by creating firewall pinholes that regular clients will surely be able to pass in contrast to the attackers clients. Our solution was implemented, tested in an appropriate testbed, and its efficiency was proven.Comment: 17 Pages, IJNGN Journa

Applying Bag of System Calls for Anomalous Behavior Detection of Applications in Linux Containers

In this paper, we present the results of using bags of system calls for learning the behavior of Linux containers for use in anomaly-detection based intrusion detection system. By using system calls of the containers monitored from the host kernel for anomaly detection, the system does not require any prior knowledge of the container nature, neither does it require altering the container or the host kernel.Comment: Published version available on IEEE Xplore (http://ieeexplore.ieee.org/document/7414047/) arXiv admin note: substantial text overlap with arXiv:1611.0305