Medical Internet of Things: A Survey of the Current Threat and Vulnerability Landscape

The Internet of things (IoT) is a system that utilizes the Internet to facilitate communication between sensors and devices. Given the ubiquitous nature of IoT devices, it is seemingly inevitable that IoT would be used as a conduit to transform healthcare. One such medical IoT (mIoT) device that is revolutionizing healthcare is the medical implant device. These mIoT implant devices which control insulin pumps, cardioverter defibrillators and bone growth stimulators have redefined the way patient data is accessed, and healthcare is delivered. These implant devices are a double-edged sword. While they allow for the effective and efficient noninvasive treatment of patients, this external communication makes the medical implants vulnerable to cyberattacks synonymous with IoT devices. As a result, privacy and security vulnerabilities have surfaced as pronounced challenges for mIoT devices. This work summarizes and synthesizes the inherent vulnerabilities associated with mIoT devices and the implications regarding patient safety

Towards Security and Privacy in Networked Medical Devices and Electronic Healthcare Systems

E-health is a growing eld which utilizes wireless sensor networks to enable access to effective and efficient healthcare services and provide patient monitoring to enable early detection and treatment of health conditions. Due to the proliferation of e-health systems, security and privacy have become critical issues in preventing data falsification, unauthorized access to the system, or eavesdropping on sensitive health data. Furthermore, due to the intrinsic limitations of many wireless medical devices, including low power and limited computational resources, security and device performance can be difficult to balance. Therefore, many current networked medical devices operate without basic security services such as authentication, authorization, and encryption. In this work, we survey recent work on e-health security, including biometric approaches, proximity-based approaches, key management techniques, audit mechanisms, anomaly detection, external device methods, and lightweight encryption and key management protocols. We also survey the state-of-the art in e-health privacy, including techniques such as obfuscation, secret sharing, distributed data mining, authentication, access control, blockchain, anonymization, and cryptography. We then propose a comprehensive system model for e-health applications with consideration of battery capacity and computational ability of medical devices. A case study is presented to show that the proposed system model can support heterogeneous medical devices with varying power and resource constraints. The case study demonstrates that it is possible to signicantly reduce the overhead for security on power-constrained devices based on the proposed system model

Survey on IoT based Cyber Security Issues and Autonomous Solutions for Implantable Medical Devices

In today’s world the technology has got boomed up to the peak. So as a measure of this technology peak we could see that the enhancement of this has raised very large. This technology booming has also impacted health care sector. In our paper we are going to discuss much on implantable medical devices and its uses which plays a major role in patient’s life. This IMD’s are going to be the life changing aspect of each and every patient. These devices are highly controlled IoT devices (i.e.) those devices are connected through internet which will help doctors to track the details of the patients remotely. On the other hand since all these devices are connected to internet, these are easily hacked by the hackers. The factors of how those devices are much vulnerable and what are all the threats that will make these devices to malfunction and lead a problem to the patients is discussed. And also this will lead the health sector to fall in their reputation. IMD’s are of many types which are in existing in the Medical industry. But we are going to consider some IMD’s as example and we have planned to make a detailed study on the problems on those devices. All these devices are vulnerable since it is connected to internet. So our aim is to completely or partially reduce the risks on those devices via communication network. We have also showcased the possible threats and vulnerabilities chances on those devices. The main scenarios of device control issues and possible solutions have been discussed in this article

Applications of Fog Computing in Video Streaming

The purpose of this paper is to show the viability of fog computing in the area of video streaming in vehicles. With the rise of autonomous vehicles, there needs to be a viable entertainment option for users. The cloud fails to address these options due to latency problems experienced during high internet traffic. To improve video streaming speeds, fog computing seems to be the best option. Fog computing brings the cloud closer to the user through the use of intermediary devices known as fog nodes. It does not attempt to replace the cloud but improve the cloud by allowing faster upload and download of information. This paper explores two algorithms that would work well with vehicles and video streaming. This is simulated using a Java application, and then graphically represented. The results showed that the simulation was an accurate model and that the best algorithm for request history maintenance was the variable model

Strengthening Privacy and Data Security in Biomedical Microelectromechanical Systems by IoT Communication Security and Protection in Smart Healthcare.

Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings forth a complex array of security and privacy challenges intrinsic to IoT communications within smart healthcare ecosystems, demanding comprehensive scrutiny. In this manuscript, we embark on an extensive analysis of the intricate security terrain associated with IoT communications in the realm of BioMEMS, addressing a spectrum of vulnerabilities that spans cyber threats, data manipulation, and interception of communications. The integration of real-world case studies serves to illuminate the direct repercussions of security breaches within smart healthcare systems, highlighting the imperative to safeguard both patient safety and the integrity of medical data. We delve into a suite of security solutions, encompassing rigorous authentication processes, data encryption, designs resistant to attacks, and continuous monitoring mechanisms, all tailored to fortify BioMEMS in the face of ever-evolving threats within smart healthcare environments. Furthermore, the paper underscores the vital role of ethical and regulatory considerations, emphasizing the need to uphold patient autonomy, ensure the confidentiality of data, and maintain equitable access to healthcare in the context of IoT communication security. Looking forward, we explore the impending landscape of BioMEMS security as it intertwines with emerging technologies such as AI-driven diagnostics, quantum computing, and genomic integration, anticipating potential challenges and strategizing for the future. In doing so, this paper highlights the paramount importance of adopting an integrated approach that seamlessly blends technological innovation, ethical foresight, and collaborative ingenuity, thereby steering BioMEMS towards a secure and resilient future within smart healthcare systems, in the ambit of IoT communication security and protection

A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants

Deep brain stimulators (DBSs), a widely used and comprehensively acknowledged restorative methodology, are a type of implantable medical device which uses electrical stimulation to treat neurological disorders. These devices are widely used to treat diseases such as Parkinson, movement disorder, epilepsy, and psychiatric disorders. Security in such devices plays a vital role since it can directly affect the mental, emotional, and physical state of human bodies. In worst-case situations, it can even lead to the patient's death. An adversary in such devices, for instance, can inhibit the normal functionality of the brain by introducing fake stimulation inside the human brain. Nonetheless, the adversary can impair the motor functions, alter impulse control, induce pain, or even modify the emotional pattern of the patient by giving fake stimulations through DBSs. This paper presents a deep learning methodology to predict different attack stimulations in DBSs. The proposed work uses long short-term memory, a type of recurrent network for forecasting and predicting rest tremor velocity. (A type of characteristic observed to evaluate the intensity of the neurological diseases) The prediction helps in diagnosing fake versus genuine stimulations. The effect of deep brain stimulation was tested on Parkinson tremor patients. The proposed methodology was able to detect different types of emulated attack patterns efficiently and thereby notifying the patient about the possible attack. - 2013 IEEE.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) through NPRP under Grant 8-408-2-172.Scopu

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Bring Your Own Device (BYOD): Risks to Adopters and Users

Bring your own device (BYOD) policy refers to a set of regulation broadly adopted by organizations that allows employee-owned mobile devices – like as laptops, smartphones, personal digital assistant and tablets – to the office for use and connection to the organizations IT infrastructure. BYOD offers numerous benefits ranging from plummeting organizational logistic cost, access to information at any time and boosting employee’s productivity. On the contrary, this concept presents various safety issues and challenges because of its characteristic security requirements. This study explored diverse literature databases to identify and classify BYOD policy adoption issues, possible control measures and guidelines that could hypothetically inform organizations and users that adopt and implement BYOD policy. The literature domain search yielded 110 articles, 26 of them were deemed to have met the inclusion standards. In this paper, a list of possible threats/vulnerabilities of BYOD adoption were identified. This investigation also identified and classified the impact of the threats/vulnerabilities on BYOD layered components according to security standards of “FIPS Publication 199” for classification. Finally, a checklist of measures that could be applied by organizations & users to mitigate BYOD vulnerabilities using a set layered approach of data, device, applications, and people were recommended