Related papers: Exploring Robust Intrusion Detection: A Benchmark …
Data quality is a significant issue for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process…
The rapid growth of the Internet of Things (IoT) has transformed industries by enabling seamless data exchange among connected devices. However, IoT networks remain vulnerable to security threats such as denial of service (DoS) attacks,…
Domain adaptation, a pivotal branch of transfer learning, aims to enhance the performance of machine learning models when deployed in target domains with distinct data distributions. This is particularly critical for object detection tasks,…
Internet of Things (IoT) is a disruptive technology with applications across diverse domains such as transportation and logistics systems, smart grids, smart homes, connected vehicles, and smart cities. Alongside the growth of these…
IoT systems have been facing increasingly sophisticated technical problems due to the growing complexity of these systems and their fast deployment practices. Consequently, IoT managers have to judiciously detect failures (anomalies) in…
We investigate the detection of botnet command and control (C2) hosts in massive IP traffic using machine learning methods. To this end, we use NetFlow data -- the industry standard for monitoring of IP traffic -- and ML models using two…
Due to the rapid growth in the number of Internet of Things (IoT) networks, the cyber risk has increased exponentially, and therefore, we have to develop effective IDS that can work well with highly imbalanced datasets. A high rate of…
The rapid expansion of Internet of Things (IoT) networks has introduced new security challenges, necessitating efficient and reliable methods for intrusion detection. In this study, a detection framework based on hyperdimensional computing…
The rapid growth of the Internet of Things (IoT) has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability…
The Internet of Things (IoT), with its high degree of interconnectivity and limited computational resources, is particularly vulnerable to a wide range of cyber threats. Intrusion detection systems (IDS) have been extensively studied to…
The rapid expansion of the industrial Internet of things (IIoT) has introduced new challenges in securing critical infrastructures against sophisticated cyberthreats. This study presents the development and evaluation of an advanced…
The Internet as we know it Today, comprises several fundamental interrelated networks, among which is the Internet of Things (IoT). Despite their versatility, several IoT devices are vulnerable from a security perspective, which renders…
This paper proposes a resource-aware allocation model for layered intrusion detection in het erogeneous networks. Monitoring traffic at higher protocol layers improves the ability to detect sophisticated attacks, but it also increases…
The rapid expansion of Internet of Things (IoT) devices has transformed industries and daily life by enabling widespread connectivity and data exchange. However, this increased interconnection has introduced serious security…
Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our…
Machine learning (ML) models are increasingly deployed in cybersecurity applications such as phishing detection and network intrusion prevention. However, these models remain vulnerable to adversarial perturbations small, deliberate input…
High-quality datasets are critical for training machine learning models, as inconsistencies in feature generation can hinder the accuracy and reliability of threat detection. For this reason, ensuring the quality of the data in network…
Fall detection is a serious healthcare issue that needs to be solved. Falling without quick medical intervention would lower the chances of survival for the elderly, especially if living alone. Hence, the need is there for developing fall…
Graph Neural Network (GNN)-based network intrusion detection systems (NIDS) are often evaluated on single datasets, limiting their ability to generalize under distribution drift. Furthermore, their adversarial robustness is typically…
Transferability scores aim to quantify how well a model trained on one domain generalizes to a target domain. Despite numerous methods proposed for measuring transferability, their reliability and practical usefulness remain inconclusive,…