相关论文: LE3D: A Lightweight Ensemble Framework of Data Dri…

Data in the real world often has an evolving distribution. Thus, machine learning models trained on such data get outdated over time. This phenomenon is called model drift. Knowledge of this drift serves two purposes: (i) Retain an accurate…

With the increasing volume of streaming data in industrial systems, online anomaly detection has become a critical task. The diverse and rapidly evolving data patterns pose significant challenges for online anomaly detection. Many existing…

Deploying robust machine learning models has to account for concept drifts arising due to the dynamically changing and non-stationary nature of data. Addressing drifts is particularly imperative in the security domain due to the…

This paper presents an Internet of Things (IoT) application that utilizes an AI classifier for fast-object detection using the frame difference method. This method, with its shorter duration, is the most efficient and suitable for…

The proliferation of Internet of Things (IoT) devices has expanded the attack surface, necessitating efficient intrusion detection systems (IDSs) for network protection. This paper presents FLARE, a feature-based lightweight aggregation for…

The rapid expansion of the Internet of Things (IoT) ecosystem has transformed various sectors but has also introduced significant cybersecurity challenges. Traditional centralized security methods often struggle to balance privacy…

In the era of big data and Internet of things, massive sensor data are gathered with Internet of things. Quantity of data captured by sensor networks are considered to contain highly useful and valuable information. However, for a variety…

Anomaly detection in complex, high-dimensional data, such as UAV sensor readings, is essential for operational safety but challenging for existing methods due to their limited sensitivity, scalability, and inability to capture intricate…

Although home IoT (Internet of Things) devices are typically plain and task oriented, the context of their daily use may affect their traffic patterns. For this reason, anomaly-based intrusion detection systems tend to suffer from a high…

Deployed machine learning models are confronted with the problem of changing data over time, a phenomenon also called concept drift. While existing approaches of concept drift detection already show convincing results, they require true…

Accurate perception of dynamic obstacles is essential for autonomous robot navigation in indoor environments. Although sophisticated 3D object detection and tracking methods have been investigated and developed thoroughly in the fields of…

IoT devices and sensors have been utilized in a cooperative manner to enable the concept of a smart environment. In these smart settings, abundant data is generated as a result of the interactions between devices and users' day-to-day…

Urban-oriented autonomous vehicles require a reliable perception technology to tackle the high amount of uncertainties. The recently introduced compact 3D LIDAR sensor offers a surround spatial information that can be exploited to enhance…

Detecting anomalies in Internet of Things (IoT) networks is a critical security challenge, often hampered by highly imbalanced and diverse network traffic datasets. Standard classifiers struggle to perform well across all traffic types.…

IoT device identification is the process of recognizing and verifying connected IoT devices to the network. This is an essential process for ensuring that only authorized devices can access the network, and it is necessary for network…

Intrusion Detection Systems (IDSs) are a key component for protecting Internet of Things (IoT) environments. However, in Machine Learning-based (ML-based) IDSs, performance is often degraded by the strong class imbalance between benign and…

The Internet of Things (IoT) is the science of connecting multiple devices that coordinate to provide the service in question. IoT environments are complex, dynamic, rapidly changing and resource constrained. Therefore, proactively adapting…

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…

Artificial intelligence (AI) has become integral to our everyday lives. Computer vision has advanced to the point where it can play the safety critical role of detecting pedestrians at road intersections in intelligent transportation…

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of…