Radio frequency (RF) fingerprinting is a tool which allows for authentication by utilizing distinct and random distortions in a received signal based on characteristics of the transmitter.
We demonstrate how Wi-Fi deauthentication attack, a notoriously easy-to-perform and effective denial-of-service (DoS) attack, can be utilized to generate adversarial data for video anomaly detection systems.
While video action recognition has been an active area of research for several years, zero-shot action recognition has only recently started gaining traction.
While anomaly detection in time series has been an active area of research for several years, most recent approaches employ an inadequate evaluation criterion leading to an inflated F1 score.
The experimental results show that the existing state-of-the-art methods are not suitable for the considered practical challenges, and the proposed algorithm outperforms them with a large margin in continual learning and few-shot learning tasks
The proposed algorithm is presented in detail for the commonly encountered heterogeneous datasets with real-valued (Gaussian) and categorical (multinomial) features.
We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic.
Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors.
We consider the network slicing problem of allocating the limited resources at the network edge (fog nodes) to vehicular and smart city users with heterogeneous latency and computing demands in dynamic environments.
Motivated by these research gaps, we propose an online anomaly detection method in surveillance videos with asymptotic bounds on the false alarm rate, which in turn provides a clear procedure for selecting a proper decision threshold that satisfies the desired false alarm rate.
In this paper, we propose a fast unsupervised anomaly detection system comprising of three modules: preprocessing module, candidate selection module and backtracking anomaly detection module.
Internet of Things (IoT) networks consist of sensors, actuators, mobile and wearable devices that can connect to the Internet.
Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail.
Anomaly detection in surveillance videos has been recently gaining attention.
Even though the performance of state-of-the-art methods on publicly available data sets has been competitive, they demand a massive amount of training data.
Ranked #18 on Anomaly Detection on CUHK Avenue
We further extend the proposed detection and localization methods to a supervised setup where an additional anomaly dataset is available, and combine the proposed semi-supervised and supervised algorithms to obtain an online learning algorithm under the semi-supervised framework.
In case the observed data have a low intrinsic dimensionality, we learn a submanifold in which the nominal data are embedded and evaluate whether the sequentially acquired data persistently deviate from the nominal submanifold.
We propose a method for simultaneously detecting shared and unshared communities in heterogeneous multilayer weighted and undirected networks.