Abstract-Wireless Body Area Network (WBAN) is a wireless network that can be attached or implanted onto the human body by using wireless sensor. Since WBAN developed for medical devices, the system should be design for a wide range of end user with different professional skill groups. This require WBAN system to be open, accurate and efficient. As from our previous experienced, any open system is vulnerable, similar to any other current available wireless systems such as Wireless Local Area Network (WLAN). However, currently there were not many discussions on the WBAN security vulnerability and security threats and if there is any, the issues were discussed through theoretical, concept and simulation data. In this paper, we discuss potential WBAN security vulnerability and threats using Practical Impact Assessment (PIA) conducted in real environment so that we are able to identify the problem area in details and develop potential solutions to produce a forensics readiness secure network architecture for WBAN system.
This article attempts to investigate the various types of threats that exist in healthcare information systems (HIS). A study has been carried out in one of the government-supported hospitals in Malaysia.The hospital has been equipped with a Total Hospital Information System (THIS). The data collected were from three different departments, namely the Information Technology Department (ITD), the Medical Record Department (MRD), and the X-Ray Department, using in-depth structured interviews. The study identified 22 types of threats according to major threat categories based on ISO/IEC 27002 (ISO 27799:2008). The results show that the most critical threat for the THIS is power failure followed by acts of human error or failure and other technological factors. This research holds significant value in terms of providing a complete taxonomy of threat categories in HIS and also an important component in the risk analysis stage.
<p>Insider threat is a significant challenge in cybersecurity. In comparison with outside attackers, inside attackers have more privileges and legitimate access to information and facilities that can cause considerable damage to an organization. Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns. However, a sophisticated method is necessary for an in-depth understanding of insider activities that the insider performs in the organization. In this study, we propose a new conceptual method for insider threat detection on the basis of the behaviors of an insider. In addition, gated recurrent unit neural network will be explored further to enhance the insider threat detector. This method will identify the optimal behavioral pattern of insider actions.</p>
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