Related papers: Detecting Irregular Patterns in IoT Streaming Data…
Previous research on behavior-based attack detection for networks of IoT devices has resulted in machine learning models whose ability to adapt to unseen data is limited and often not demonstrated. This paper presents IoTGeM, an approach…
Anomaly detection is critical for finding suspicious behavior in innumerable systems. We need to detect anomalies in real-time, i.e. determine if an incoming entity is anomalous or not, as soon as we receive it, to minimize the effects of…
Given that security threats and privacy breaches are com- monplace today, it is an important problem for one to know whether their device(s) are in a "good state of security", or is there a set of high- risk vulnerabilities that need to be…
The detection of anomalies in real time is paramount to maintain performance and efficiency across a wide range of applications including web services and smart manufacturing. This paper presents a novel algorithm to detect anomalies in…
Given the increased growing of Internet of Things networks and their presence in critical aspects of human activities, the security of devices connected to these networks becomes critical. Machine Learning approaches are becoming prominent…
For the elderly population, falls pose a serious and increasing risk of serious injury and loss of independence. In order to overcome this difficulty, we present ElderFallGuard: A Computer Vision Based IoT Solution for Elderly Fall…
Data is rapidly increasing in volume and velocity and the Internet of Things (IoT) is one important source of this data. The IoT is a collection of connected devices (things) which are constantly recording data from their surroundings using…
The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing medical-related human activities (MRHA) is pivotal for healthcare systems,…
Internet of Things (IoT) devices have grown in popularity since they can directly interact with the real world. Home automation systems automate these interactions. IoT events are crucial to these systems' decision-making but are often…
Falls represent one of the most detrimental occurrences for the elderly. Given the continually increasing ageing demographic, there is a pressing demand for advancing fall detection systems. The swift progress in sensor networks and the…
Device identification is one way to secure a network of IoT devices, whereby devices identified as suspicious can subsequently be isolated from a network. In this study, we present a machine learning-based method, IoTDevID, that recognizes…
As the adoption of Internet of Things (IoT) devices continues to rise in enterprise environments, the need for effective and efficient security measures becomes increasingly critical. This paper presents a cost-efficient platform to…
Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular…
Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such, they would greatly benefit from automated management. This requires robustly identifying devices so that appropriate network security…
Real-time monitoring of human behaviours, especially in e-Health applications, has been an active area of research in the past decades. On top of IoT-based sensing environments, anomaly detection algorithms have been proposed for the early…
Given a stream of entries over time in a multi-dimensional data setting where concept drift is present, how can we detect anomalous activities? Most of the existing unsupervised anomaly detection approaches seek to detect anomalous events…
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring…
The automatic detection of gait anomalies can lead to systems that can be used for fall detection and prevention. In this paper, we present a gait anomaly detection system based on the Matrix Profile (MP) algorithm. The MP algorithm is…
Streaming anomaly detection refers to the problem of detecting anomalous data samples in streams of data. This problem poses challenges that classical and deep anomaly detection methods are not designed to cope with, such as conceptual…
The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies. Data can be collected, transferred, and exchanged with other devices over the network without requiring human…