Preliminary Examination of Emergent Threat and Risk Landscapes in Intelligent Harvesting Robots

Recently, many farmers have started using robots to help with labour-intensive harvesting operations and deal with labour shortage that was also a negative consequence of the recent COVID-19 pandemic. Intelligent harvesting robots make farming more efficient and productive. However, and like any other technology, intelligent harvesting robots come with a security risk, as threats can damage the robotic system and wreak havoc before the farmer/operator realizes it. This paper focuses on analysing the threats against the security of harvesting robots alongside with the safety implications that may rise if the robotic system is compromised. We analysed an actual asparagus harvesting robot and looked at others in the literature. We identified several security threats which we classified into five categories: network, hardware, software, Artificial Intelligence (AI) and cloud security issues. We selected three interesting attack scenarios for a deeper analysis. Our results suggest that these robots have a large attack surface that can lead to exploits with immense financial and operational impacts.

Do the Right Thing: A Privacy Policy Adherence Analysis of over Two Million Apps in Apple iOS App Store.

Mobile app developers are often obliged by regulatory frameworks to provide a privacy policy in natural comprehensible language to describe their apps' privacy practices. However, prior research has revealed that: (1) not all app developers offer links to their privacy policies; and (2) even if they do offer such access, it is difficult to determine if it is a valid link to a (valid) policy. While many prior studies looked at this issue in Google Play Store, Apple App Store, and particularly the iOS store, is much less clear. In this paper, we conduct the first and the largest study to investigate the previous issues in the iOS app store ecosystem. First, we introduce an App Privacy Policy Extractor (APPE), a system that embraces and analyses the metadata of over two million apps to give insightful information about the distribution of the supposed privacy policies, and the content of the provided privacy policy links, store-wide. The result shows that only 58.5% of apps provide links to purported privacy policies, while 39.3% do not provide policy links at all. Our investigation of the provided links shows that only 38.4% of those links were directed to actual privacy policies, while 61.6% failed to lead to a privacy policy. Further, for research purposes we introduce the App Privacy Policy Corpus (APPC-451K); the largest app privacy policy corpus consisting of data relating to more than 451K verified privacy policies.

Methodology for integrating artificial intelligence in healthcare systems: learning from COVID-19 to prepare for Disease X.

Artificial intelligence and edge devices have been used at an increased rate in managing the COVID-19 pandemic. In this article we review the lessons learned from COVID-19 to postulate possible solutions for a Disease X event. The overall purpose of the study and the research problems investigated is the integration of artificial intelligence function in digital healthcare systems. The basic design of the study includes a systematic state-of-the-art review, followed by an evaluation of different approaches to managing global pandemics. The study design then engages with constructing a new methodology for integrating algorithms in healthcare systems, followed by analysis of the new methodology and a discussion. Action research is applied to review existing state of the art, and a qualitative case study method is used to analyse the knowledge acquired from the COVID-19 pandemic. Major trends found as a result of the study derive from the synthesis of COVID-19 knowledge, presenting new insights in the form of a conceptual methodology-that includes six phases for managing a future Disease X event, resulting with a summary map of various problems, solutions and expected results from integrating functional AI in healthcare systems.

Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data.

In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.

Smart delivery and retrieval of swab collection kit for COVID-19 test using autonomous Unmanned Aerial Vehicles

Drones, also known as Unmanned Aerial Vehicles (UAVs), are one of the highly emerging technologies of the modern day. Due to their small size, flying capabilities, and complex machinery, drones can be deployed in diverse fields, including agriculture, sports, entertainment, parcel delivery, disaster management, search and rescue, emergency medicine, and healthcare. In case of medical emergency, timely delivery of the required emergency kit is very important. This is often not possible in many underdeveloped countries due to lack of resources, traffic jams, congestion or challenging routes. Also, in times like today’s when the world is hit with COVID-19 pandemic, the movement is very limited due to lockdowns and emergency. In such case, drones can be deployed to deliver the emergency kits and collect samples for tests. This may save someones life as well as time and financial resources. In third world countries, the COVID-19 has spread chaos because of very limited hospitals, resources and staff. Therefore, it is difficult for the government and health officials to accommodate every patient or give him/her the care that he/she needs. Amidst the fear of pandemic, everyone is trying to undergo tests for COVID-19 which is difficult to handle In our research, we have proposed a solution that comprises smartphone application with the help of a patient sending a call to a healthcare centre for delivering emergency kit. The kit contains equipment with the help of which a person can collect swab. The drone takes the swab samples back to the healthcare centre for tests. We have introduced an optimization factor as a baseline for future studies of this kind. We have further conducted field experiments to test our proposed scheme. The results have shown that drones can be quite efficient in collecting samples and delivering emergency kits.

Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic.

The COVID-19 pandemic was a remarkable, unprecedented event which altered the lives of billions of citizens globally resulting in what became commonly referred to as the new-normal in terms of societal norms and the way we live and work. Aside from the extraordinary impact on society and business as a whole, the pandemic generated a set of unique cyber-crime related circumstances which also affected society and business. The increased anxiety caused by the pandemic heightened the likelihood of cyber-attacks succeeding corresponding with an increase in the number and range of cyber-attacks. This paper analyses the COVID-19 pandemic from a cyber-crime perspective and highlights the range of cyber-attacks experienced globally during the pandemic. Cyber-attacks are analysed and considered within the context of key global events to reveal the modus-operandi of cyber-attack campaigns. The analysis shows how following what appeared to be large gaps between the initial outbreak of the pandemic in China and the first COVID-19 related cyber-attack, attacks steadily became much more prevalent to the point that on some days, three or four unique cyber-attacks were being reported. The analysis proceeds to utilise the UK as a case study to demonstrate how cyber-criminals leveraged salient events and governmental announcements to carefully craft and execute cyber-crime campaigns.