Unmanned aerial vehicles (UAVs), also known as drones, provides unique functionalities, which allows area surveillance, inspection, surveying, unarmed cargo, armed attack machines, and aerial photography. Drones are susceptible to GPS spoofing attacks, integrity attacks and de-authentication attacks, which can allow criminals to access data, intercept the drone and use it commit a crime. Thus, this paper is presented to report on potential attacks against the Parrot Bebop 2 drone, and the ability for an investigator to collect evidence about the attacks on the drone. This paper aims at examining the possibility of establishing ownership and collecting data to reconstruct events, linking the drone controller with the drone to prove ownership, flight origins and other potentially useful information necessary to identify the proprietor of a crime. In addition, we have also proposed small-scale drone ontology for modelling drone context data, and simple forensic processing framework for small-scale drones.

With increasing technical safeguards to protect information systems, Human error continues to be a critical factor contributing to the rise in information systems attacks and data breaches. Inadequate or unenforceable Cybersecurity policies or training can open doors for adversaries to circumvent technical safeguards and paint a picture of a growing cybersecurity problem. The problem investigated in this work assesses if organizations adequately invest in resources to provide industry-aligned cybersecurity education, training, and awareness that can minimize human error leading to cyber-attacks. This work aims to investigate breaches attributed to human errors and compare cybersecurity policies, education, training, and awareness programs in three different schools in New York State. The work focused on user awareness and vulnerable behaviours, effective training for users, and investigating start-of-the-art approaches to gauge or evaluate the organization’s cybersecurity stance when compared to industry frameworks like the NIST framework. A Triangulation research approach including quantitative, qualitative, and descriptive methods are adopted for this work. Instruments for data collection include a survey, literature review, qualitative analysis to identify research gaps, and assessments of the questionnaires. This work demonstrates that formulated enforced cybersecurity policies coupled with targeted security education, training, and awareness are instrumental to decreasing user errors, thereby reducing the probability of a cyber-attack.

The Internet of things (IoT) has revolutionized many aspects of the world, including industrial systems, automobiles, home automation, and surveillance, to name a few. IoT has offered a multitude of conveniences for our daily lives, such as being able to control our thermostats remotely, view our home surveillance cameras while away, or have a smart television that can surf the web. However, the widespread adoption of IoT devices combined with their vast device vulnerabilities results in significant security risks reinforcing the need for more robust default security controls and public awareness. As such, this paper aims to discover and document security vulnerabilities in the Asus Zenbo Junior IoT robot, along with providing a few best practices when securing smart home devices. This work presents an experiment using several security vulnerability assessment tools such as Nmap and OpenVAS scans to assess cybersecurity vulnerability currently present on Zenbo based on the 4P forensic investigative framework. The result of the experiment shows multiple open ports were discovered, along with miscellaneous information that an attacker could use to their advantage to attack the Zenbo robot. Based on the result, this work presents various security precautions that can help users protect against cyber-attack.

Electronic health record (EHR) data breaches create severe concerns for patients’ privacy, safety, and risk of loss for healthcare entities responsible for managing patient health records. EHR systems collect a vast amount of user-sensitive data, requiring integration, implementation, and the application of essential security principles, controls, and strategies to safeguard against persistent adversary attacks. This research is an exploratory study into current integrated EHR cybersecurity attacks using United States Health Insurance Portability and Accountability Act (HIPAA) privacy and security breach reported data. This work investigates if current EHR implementation lacks the requisite security control to prevent a cyber breach and protect user privacy. We conduct descriptive and trend analysis to describe, demonstrate, summarize data points, and predict direction based on current and historical data by covered entity, type of breaches, and point of breaches (examine, attack methods, patterns, and location of breach information). An Autoregressive Integrated Moving Average (ARIMA) model is used to provide a detailed analysis of the data demonstrating breaches caused by hacking and IT incidents show a significant trend (coefficient 0.84, p-value < 2.2 × 10⁻¹⁶ ***). The findings reveal a consistent rise in breaches—particularly from hacking and IT incidents—disproportionately affecting healthcare providers. The study highlights that EHR data breaches often follow recurring patterns, indicating common vulnerabilities, and underlines the need for prioritized, data-driven security investments. These findings validate the hypothesis that most EHR cybersecurity attacks are concentrated using similar attack methodologies and face common vulnerabilities and demonstrate the value of targeted mitigation strategies to strengthen healthcare cybersecurity. The findings highlight the urgent need for healthcare organizations and policymakers to prioritize targeted, data-driven security investments and enforce stricter controls to protect EHR systems from increasingly frequent and predictable cyberattacks.