Related papers: A Framework for Data-Driven Physical Security and …

With the emergence of smart cities, Internet of Things (IoT) devices as well as deep learning technologies have witnessed an increasing adoption. To support the requirements of such paradigm in terms of memory and computation, joint and…

Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. This is an important research problem, due to its broad set of application domains, from data analysis to e-health,…

Anomaly-based intrusion detection promises to detect novel or unknown attacks on industrial control systems by modeling expected system behavior and raising corresponding alarms for any deviations.As manually creating these behavioral…

Many Security Operations Centers (SOCs) today still heavily rely on signature-based Network Intrusion Detection Systems (NIDS) such as Suricata. The specificity of intrusion detection rules and the coverage provided by rulesets are common…

Anomaly detection in real-world scenarios poses challenges due to dynamic and often unknown anomaly distributions, requiring robust methods that operate under an open-world assumption. This challenge is exacerbated in practical settings,…

Graph knowledge models and ontologies are very powerful modeling and re asoning tools. We propose an effective approach to model network attacks and attack prediction which plays important roles in security management. The goals of this…

Machine learning (ML) based indoor localization solutions are critical for many emerging applications, yet their efficacy is often compromised by hardware/software variations across mobile devices (i.e., device heterogeneity) and the threat…

We propose a novel physical layer security scheme that exploits an optimization method as a one-way function. The proposed scheme builds on nonsquare differential multiple-input multiple-output (MIMO), which is capable of noncoherent…

This paper addresses the problem of detecting possible intruders in a group of autonomous robots, which coexist in a shared environment and interact with each other according to a set of "social behaviors", or common rules. Such rules…

In the Internet of Things (IoT) devices are exposed to various kinds of attacks when connected to the Internet. An attack detection mechanism that understands the limitations of these severely resource-constrained devices is necessary. This…

This paper presents a security paradigm for edge devices to defend against various internal and external threats. The first section of the manuscript proposes employing machine learning models to identify MQTT-based (Message Queue Telemetry…

Deep learning-based object detection is a powerful approach for detecting faulty insulators in power lines. This involves training an object detection model from scratch, or fine tuning a model that is pre-trained on benchmark computer…

A growing number of Internet of Things (IoT) devices are used across consumer, medical, and industrial domains. They interact with their environment through sensors and actuators and connect to networks such as the Internet. Because sensors…

Anomaly detection is a branch of data analysis and machine learning which aims at identifying observations that exhibit abnormal behaviour. Be it measurement errors, disease development, severe weather, production quality default(s) (items)…

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…

Detection of emerging attacks on network infrastructure is a critical aspect of security management. To meet the growing scale and complexity of modern threats, machine learning (ML) techniques offer valuable tools for automating the…

The rapid advancement of machine learning technologies raises questions about the security of machine learning models, with respect to both training-time (poisoning) and test-time (evasion, impersonation, and inversion) attacks. Models…

Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robustly operate in the…

Data-driven machine learning models often require extensive datasets, which can be costly or inaccessible, and their predictions may fail to comply with established physical laws. Current approaches for incorporating physical priors…

Cyber-Physical Systems (CPS) are vulnerable to cyber-physical attacks that violate physical laws. While invariant-based anomaly detection is effective, existing methods are limited: data-driven approaches lack semantic context, and…