Secure allocation for graph-based virtual machines in cloud environments

Cloud computing systems (CCSs) enable the sharing of physical computing resources through virtualisation, where a group of virtual machines (VMs) can share the same physical resources of a given machine. However, this sharing can lead to a so-called side-channel attack (SCA), widely recognised as a potential threat to CCSs. Specifically, malicious VMs can capture information from (target) VMs, i.e., those with sensitive information, by merely co-located with them on the same physical machine. As such, a VM allocation algorithm needs to be cognizant of this issue and attempts to allocate the malicious and target VMs onto different machines, i.e., the allocation algorithm needs to be security-aware. This paper investigates the allocation patterns of VM allocation algorithms that are more likely to lead to a secure allocation. A driving objective is to reduce the number of VM migrations during allocation. We also propose a graph-based secure VMs allocation algorithm (GbSRS) to minimise SCA threats. Our results show that algorithms following a stacking-based behaviour are more likely to produce secure VMs allocation than those following spreading or random behaviours

Security aware and energy-efficient virtual machine consolidation in cloud computing systems

The increasing number of data centers is consuming significant power with an upward surge. Hence, to preserve such huge energy and operating cost of data centers, the cloud service providers consolidate Virtual Machines (VM) to minimize the number of active physical machines. However, lack of reliable security measurements and policy enforcement during the consolidation process, have increased the security risks to the clients. In this paper, the compartment isolation technique is introduced to improve the system security during the consolidation process. The security-based selection and placement algorithms are also presented. The comparative analysis of this improved security approach shows that utilizing the proposed method will reduce the security risks without impacting the overall power consumption in data centers

Smart aging : utilisation of machine learning and the Internet of Things for independent living

Smart aging utilises innovative approaches and technology to improve older adults’ quality of life, increasing their prospects of living independently. One of the major concerns the older adults to live independently is “serious fall”, as almost a third of people aged over 65 having a fall each year. Dementia, affecting nearly 9% of the same age group, poses another significant issue that needs to be identified as early as possible. Existing fall detection systems from the wearable sensors generate many false alarms; hence, a more accurate and secure system is necessary. Furthermore, there is a considerable gap to identify the onset of cognitive impairment using remote monitoring for self-assisted seniors living in their residences. Applying biometric security improves older adults’ confidence in using IoT and makes it easier for them to benefit from smart aging. Several publicly available datasets are pre-processed to extract distinctive features to address fall detection shortcomings, identify the onset of dementia system, and enable biometric security to wearable sensors. These key features are used with novel machine learning algorithms to train models for the fall detection system, identifying the onset of dementia system, and biometric authentication system. Applying a quantitative approach, these models are tested and analysed from the test dataset. The fall detection approach proposed in this work, in multimodal mode, can achieve an accuracy of 99% to detect a fall. Additionally, using 13 selected features, a system for detecting early signs of dementia is developed. This system has achieved an accuracy rate of 93% to identify a cognitive decline in the older adult, using only some selected aspects of their daily activities. Furthermore, the ML-based biometric authentication system uses physiological signals, such as ECG and Photoplethysmogram, in a fusion mode to identify and authenticate a person, resulting in enhancement of their privacy and security in a smart aging environment. The benefits offered by the fall detection system, early detection and identifying the signs of dementia, and the biometric authentication system, can improve the quality of life for the seniors who prefer to live independently or by themselves