Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chro...
详细信息
The Border Gateway Protocol (BGP) is a crucial component of the Internet's infrastructure that enables the exchange of routing information among multiple Autonomous systems so data flow from one network to another...
The Border Gateway Protocol (BGP) is a crucial component of the Internet's infrastructure that enables the exchange of routing information among multiple Autonomous systems so data flow from one network to another becomes possible. However, rare anomalies in BGP, such as IP prefix hijacks, misconfigurations, and worm attacks, when they occur, can cause significant disruptions to the network and threaten the stability and reliability of the Internet. Considerable efforts have been made to understand the nature of normal and abnormal BGP updates to identify and mitigate their disruptive consequences. Recent studies in the literature suggest that machine learning (ML) techniques can achieve a high level of accuracy and robustness in anomaly detection. To fully leverage the advantages of ML techniques, it is necessary to pre-process the data and choose a suitable model that helps identify and mitigate against any such BGP anomalies and improve the stability and reliability of the Internet. This paper evaluates multiple machine learning models for detecting BGP anomalies and comprehensively analyzes their effectiveness. Results reveal that AdaBoost achieves an impressive accuracy of 97.22%, making it the optimal choice for BGP anomaly detection.
Compute In Memory (CIM) has gained significant attention in recent years due to its potential to overcome the memory bottleneck in Von-Neumann computing architectures. While most CIM architectures use non-volatile mem...
详细信息
In future demand response scenarios, a multitude of different types of resources are potentially to be used, e.g., electric vehicles, flexible residential loads, and battery storage systems. To solve the problem of re...
详细信息
Remote photoplethysmography (rPPG) is a non-contact technology that can estimate heart rate using facial video and holds significant potential for health monitoring. Despite the latest deep learning-based rPPG approac...
详细信息
ISBN:
(纸本)9798400708343
Remote photoplethysmography (rPPG) is a non-contact technology that can estimate heart rate using facial video and holds significant potential for health monitoring. Despite the latest deep learning-based rPPG approaches can predict high-quality rPPG signal under similar scenarios, these methods often suffer from degraded performance when encountering variations in subjects, environments, or illumination conditions in target domains. To address this challenge, we propose an uncertainty-guided self-training approach that leverages model uncertainty and periodic priors to enhance generalization across different domains without requiring labels in the target domain. We iteratively update the model using pseudo-labels generated from its own predictions on unlabelled data in the target domain, with varying confidence levels informed by the model’s uncertainty estimation. To achieve this, we modify a standard Convolutional Neural Network (CNN) into a Bayesian Neural Network (BNN) for uncertainty estimation, which guides the assignment of pseudo-labels with varying confidence levels. By employing the adversarially learned periodic priors of rPPG signals shared across domains as regularization terms, we further stabilize the model adaptation process. We evaluate the proposed method on two public datasets (PURE and UBFC-rPPG) across five cross-domain tasks. Experimental results demonstrate improved performance over the baselines, with gains ranging from 60.5% to 97.2%, outperforming existing methods in generalization performance for rPPG-based heart rate measurement.
The increasing frequency and sophistication of cyberattacks necessitate a reevaluation of traditional password practices. This paper introduces DyMP-Gen (Dynamic Memorable Password Generation) Algorithm, a novel algor...
详细信息
ISBN:
(数字)9798331520960
ISBN:
(纸本)9798331520977
The increasing frequency and sophistication of cyberattacks necessitate a reevaluation of traditional password practices. This paper introduces DyMP-Gen (Dynamic Memorable Password Generation) Algorithm, a novel algorithm designed to enhance password security while prioritizing usability. DyMP-Gen allows users to generate strong, unique passwords that evolve over time, mitigating the risks associated with static, password reuse, and leaked passwords. By combining a user pre-selected static component with dynamically generated elements, a password based on current time and date is created. DyMP-Gen ensures that passwords are both memorable and resilient to cracking attempts. The algorithm’s simplicity and offline functionality further enhance its usability, eliminating the need for additional devices or software. Through its innovative approach, DyMP-Gen offers a promising solution to the pervasive challenges of password security.
A Distributed Denial of Service (DDoS) is an attack which aim is to stop or tamper with an online service incapacitating a server with a flood of packages or requests, using internet or intranet. The main aim of the D...
详细信息
State-of-the-art safety assurance approaches for autonomous vehicles (AVs) rely on the existence of relevant, high-quality traffic scenarios as test cases. As a key drawback, traffic scenario synthesis approaches are ...
详细信息
ISBN:
(数字)9798331538422
ISBN:
(纸本)9798331538439
State-of-the-art safety assurance approaches for autonomous vehicles (AVs) rely on the existence of relevant, high-quality traffic scenarios as test cases. As a key drawback, traffic scenario synthesis approaches are often expensive to compute and hard to integrate in external AV testing workflows. While different approaches make varying assumptions about the AV-under-test, they rely on similar conceptual baselines for scenario representation, which yields an opportunity for unification and collaboration within the AV testing community. In this paper, we build up on these representation similarities and propose a traffic scenario catalog to support collaborative AV testing. Our proposed model-based architecture unifies common concepts in existing AV testing approaches, thus enabling a seamless integration of their derived scenarios and test execution results. Additionally, our catalog supports abstractions over scenarios through an integrated data aggregation methodology. This allows users to empirically evaluate and compare the behavior of AV controllers, thereby identifying potential anomalies (faults), e.g., through a domain-specific adaptation of metamorphic testing.
暂无评论