With growing healthcare demands, the Internet of Medical Things (IoMT) has grown significantly in recent years and is dominating the healthcare industry. However, these smarter and advanced medical devices are “more” complicated in terms of software and hardware, with several defects and vulnerabilities that have been found and can lead to possible malicious attacks. Healthcare organizations are the new focus of attackers for carrying out IoMT-focused cyberattacks, which are becoming more common. In recent years, ransomware and distributed denial of service (DDoS) attacks are malware-based popular attacks on IoMT devices. Cyberattacks and disruptions in clinical care can have a catastrophic effect on patient safety, which trickles down to the medical staff’s responsiveness. Moreover, because different medical devices have varying vital capabilities, it is critical to enable differentiated network services for these devices with varying critical levels of operation under network dynamics. Hence, it becomes crucial to efficiently detect and identify any malicious network activities to eliminate or minimize the impact of these attacks, as well as to detect and identify network traffics belonging to different medical equipment.

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The threats to the cybersecurity of today’s organizations’ networks are numerous, vastly varied and constantly evolving. According to a very recent report, about 81 percent of US organizations have faced a cyberattack. Operational downtime was the biggest impact for those organizations (55 percent), followed by compromised customer data (37 percent), and compromised end-user safety (36 percent). Most recently, the COVID-19 pandemic has reared its ugly head – cyberattacks have become more common and have large increased very recently. However, the scale of contemporary network traffic volumes makes monitoring and visualizing real-time information considerably more challenging for human ability to process. Although many IDSs utilizes several visualization techniques and notification mechanisms to provide administrators with an overall view and specific information about particular traffic or attacks on the network, they often still fail to represent the events in an understandable way, and it is quite difficult to understand the relevance of aggregate traffic when receiving only the alarms for individual intrusion records. Moreover, these tools are not well suited for continuous monitoring scenarios since network administrators will suffer from loss of concentration, visual fatigue, temporal demand, and frustration increase.

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Loneliness among older adults is a widespread and harmful condition linked to functional decline, cognitive impairment, and increased morbidity and mortality. Despite its reversibility through targeted interventions, loneliness remains underdiagnosed due to social stigma and the lack of routine assessments by healthcare providers. This project proposes the development of HomePal, a smart speaker-based system that leverages digital biomarkers from speech and behavioral data to automatically assess loneliness levels in community-dwelling older adults. Utilizing smart speakers (e.g., Amazon Echo) and IoT devices, the system will collect linguistic, acoustic, and usage patterns to detect loneliness correlates. Novel semi-supervised Generative Adversarial Networks (SGANs) will be developed for multi-class classification, offering fine-grained assessments of loneliness. This approach will enable early detection of at-risk individuals, continuous monitoring of loneliness fluctuations, and evaluation of intervention effectiveness, addressing a critical public health challenge posed by the growing aging population.

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