arXiv subjects

Anna Maria Mandalari

Publications and source records attributed to Anna Maria Mandalari.

At least 19 recordsLinked to original sources

arXiv2026

CERTIoT-6G: Continuous Cybersecurity Certification for IoT Devices in 5G/6G Networks

The massive adoption of Internet of Things (IoT) devices across critical domains such as healthcare, smart cities, industrial automation, and critical infrastructure introduces significant cybersecurity and regulatory challenges. Current and forthcoming European regulations, including the Cyber Resilience Act (CRA) and the NIS2 Directive, require manufacturers, operators, and other organizations to ensure secure-by-design devices, continuous vulnerability management, and resilient operation throughout the device lifecycle. Traditional certification mechanisms remain static, manual, and difficult to scale across heterogeneous IoT ecosystems. This paper presents CERTIoT-6G, a Security-as-a-Service (SECaaS) framework that enables automated cybersecurity certification and continuous compliance monitoring of IoT devices operating in 5G and future 6G networks. The framework integrates automated compliance analysis, real-time traffic monitoring, and adversarial testing capabilities. We validate the CERTIoT-6G framework on different IoT device categories operating in an advanced 5G testbed. Evaluation results reveal critical compliance gaps, particularly in traffic encryption and availability under unstable conditions, and demonstrate that the framework produces actionable verdicts mapped to regulatory requirements across heterogeneous device types. Furthermore, we show that the monitoring pipeline has a negligible impact on live 5G traffic.

cs.NI↗

arXiv2026

Early-Stage IoT Device Identification Using Passive Network Traffic Analysis

The rapid proliferation of Internet of Things (IoT) devices introduces significant security challenges due to limited visibility and weak device-level guarantees. Accurate and timely identification of devices is essential for enforcing network policies and detecting unauthorised hardware, yet existing approaches often rely on long-term traffic observation, payload inspection, or infrastructure-dependent features. In this paper, we investigate whether IoT devices can be reliably identified during the early stages of network attachment using only passive traffic analysis. We propose a lightweight approach based on flow-level features extracted from metadata, avoiding payload inspection and active probing. Through systematic evaluation across multiple observation windows, we show that device-specific signatures emerge within the first few seconds of communication, enabling high-accuracy identification (up to 99%) across 37 IoT devices. Notably, extending the observation window does not consistently improve performance and may slightly degrade accuracy, indicating that the most discriminative behaviour occurs during initial device startup. These findings demonstrate the feasibility of fast, privacy-preserving IoT device identification at the network edge, supporting real-time enforcement, device inventory, and anomaly detection in practical deployments.

cs.NI↗

arXiv2025

Big Help or Big Brother? Auditing Tracking, Profiling, and Personalization in Generative AI Assistants

Generative AI (GenAI) browser assistants integrate powerful capabilities of GenAI in web browsers to provide rich experiences such as question answering, content summarization, and agentic navigation. These assistants, available today as browser extensions, can not only track detailed browsing activity such as search and click data, but can also autonomously perform tasks such as filling forms, raising significant privacy concerns. It is crucial to understand the design and operation of GenAI browser extensions, including how they collect, store, process, and share user data. To this end, we study their ability to profile users and personalize their responses based on explicit or inferred demographic attributes and interests of users. We perform network traffic analysis and use a novel prompting framework to audit tracking, profiling, and personalization by the ten most popular GenAI browser assistant extensions. We find that instead of relying on local in-browser models, these assistants largely depend on server-side APIs, which can be auto-invoked without explicit user interaction. When invoked, they collect and share webpage content, often the full HTML DOM and sometimes even the user's form inputs, with their first-party servers. Some assistants also share identifiers and user prompts with third-party trackers such as Google Analytics. The collection and sharing continues even if a webpage contains sensitive information such as health or personal information such as name or SSN entered in a web form. We find that several GenAI browser assistants infer demographic attributes such as age, gender, income, and interests and use this profile--which carries across browsing contexts--to personalize responses. In summary, our work shows that GenAI browser assistants can and do collect personal and sensitive information for profiling and personalization with little to no safeguards.

cs.HC↗

arXiv2025

Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway

The rapid expansion of Internet of Things (IoT) devices, particularly in smart home environments, has introduced considerable security and privacy concerns due to their persistent connectivity and interaction with cloud services. Despite advancements in IoT security, effective privacy measures remain uncovered, with existing solutions often relying on cloud-based threat detection that exposes sensitive data or outdated allow-lists that inadequately restrict non-essential network traffic. This work presents ML-IoTrim, a system for detecting and mitigating non-essential IoT traffic (i.e., not influencing the device operations) by analyzing network behavior at the edge, leveraging Machine Learning to classify network destinations. Our approach includes building a labeled dataset based on IoT device behavior and employing a feature-extraction pipeline to enable a binary classification of essential vs. non-essential network destinations. We test our framework in a consumer smart home setup with IoT devices from five categories, demonstrating that the model can accurately identify and block non-essential traffic, including previously unseen destinations, without relying on traditional allow-lists. We implement our solution on a home access point, showing the framework has strong potential for scalable deployment, supporting near-real-time traffic classification in large-scale IoT environments with hundreds of devices. This research advances privacy-aware traffic control in smart homes, paving the way for future developments in IoT device privacy.

cs.CR↗

arXiv2024

Watching TV with the Second-Party: A First Look at Automatic Content Recognition Tracking in Smart TVs

Smart TVs implement a unique tracking approach called Automatic Content Recognition (ACR) to profile viewing activity of their users. ACR is a Shazam-like technology that works by periodically capturing the content displayed on a TV's screen and matching it against a content library to detect what content is being displayed at any given point in time. While prior research has investigated third-party tracking in the smart TV ecosystem, it has not looked into second-party ACR tracking that is directly conducted by the smart TV platform. In this work, we conduct a black-box audit of ACR network traffic between ACR clients on the smart TV and ACR servers. We use our auditing approach to systematically investigate whether (1) ACR tracking is agnostic to how a user watches TV (e.g., linear vs. streaming vs. HDMI), (2) privacy controls offered by smart TVs have an impact on ACR tracking, and (3) there are any differences in ACR tracking between the UK and the US. We perform a series of experiments on two major smart TV platforms: Samsung and LG. Our results show that ACR works even when the smart TV is used as a "dumb" external display, opting-out stops network traffic to ACR servers, and there are differences in how ACR works across the UK and the US.

cs.CR↗

arXiv2024

ACCESS-FL: Agile Communication and Computation for Efficient Secure Aggregation in Stable Federated Learning Networks

Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data and generates a global model on a server by aggregating model updates. Traditional FL approaches risk exposing sensitive client data when plain model updates are transmitted to the server, making them vulnerable to security threats such as model inversion attacks where the server can infer the client's original training data from monitoring the changes of the trained model in different rounds. Google's Secure Aggregation (SecAgg) protocol addresses this threat by employing a double-masking technique, secret sharing, and cryptography computations in honest-but-curious and adversarial scenarios with client dropouts. However, in scenarios without the presence of an active adversary, the computational and communication cost of SecAgg significantly increases by growing the number of clients. To address this issue, in this paper, we propose ACCESS-FL, a communication-and-computation-efficient secure aggregation method designed for honest-but-curious scenarios in stable FL networks with a limited rate of client dropout. ACCESS-FL reduces the computation/communication cost to a constant level (independent of the network size) by generating shared secrets between only two clients and eliminating the need for double masking, secret sharing, and cryptography computations. To evaluate the performance of ACCESS-FL, we conduct experiments using the MNIST, FMNIST, and CIFAR datasets to verify the performance of our proposed method. The evaluation results demonstrate that our proposed method significantly reduces computation and communication overhead compared to state-of-the-art methods, SecAgg and SecAgg+.

cs.CR↗

arXiv2024

Analysis of DNS Dependencies and their Security Implications in Australia: A Comparative Study of General and Indigenous Populations

This paper investigates the impact of internet centralization on DNS provisioning, particularly its effects on vulnerable populations such as the indigenous people of Australia. We analyze the DNS dependencies of Australian government domains that serve indigenous communities compared to those serving the general population. Our study categorizes DNS providers into leading (hyperscaler, US-headquartered companies), non-leading (smaller Australian-headquartered or non-Australian companies), and Australian government-hosted providers. Then, we build dependency graphs to demonstrate the direct dependency between Australian government domains and their DNS providers and the indirect dependency involving further layers of providers. Additionally, we conduct an IP location analysis of DNS providers to map out the geographical distribution of DNS servers, revealing the extent of centralization on DNS services within or outside of Australia. Finally, we introduce an attacker model to categorize potential cyber attackers based on their intentions and resources. By considering attacker models and DNS dependency results, we discuss the security vulnerability of each population group against any group of attackers and analyze whether the current setup of the DNS services of Australian government services contributes to a digital divide.

cs.NI↗

arXiv2024

Is Your Kettle Smarter Than a Hacker? A Scalable Tool for Assessing Replay Attack Vulnerabilities on Consumer IoT Devices

Consumer Internet of Things (IoT) devices often leverage the local network to communicate with the corresponding companion app or other devices. This has benefits in terms of efficiency since it offloads the cloud. ENISA and NIST security guidelines underscore the importance of enabling default local communication for safety and reliability. Indeed, an IoT device should continue to function in case the cloud connection is not available. While the security of cloud-device connections is typically strengthened through the usage of standard protocols, local connectivity security is frequently overlooked. Neglecting the security of local communication opens doors to various threats, including replay attacks. In this paper, we investigate this class of attacks by designing a systematic methodology for automatically testing IoT devices vulnerability to replay attacks. Specifically, we propose a tool, named REPLIOT, able to test whether a replay attack is successful or not, without prior knowledge of the target devices. We perform thousands of automated experiments using popular commercial devices spanning various vendors and categories. Notably, our study reveals that among these devices, 51% of them do not support local connectivity, thus they are not compliant with the reliability and safety requirements of the ENISA/NIST guidelines. We find that 75% of the remaining devices are vulnerable to replay attacks with REPLIOT having a detection accuracy of 0.98-1. Finally, we investigate the possible causes of this vulnerability, discussing possible mitigation strategies.

cs.CR↗

arXiv2024

SunBlock: Cloudless Protection for IoT Systems

With an increasing number of Internet of Things (IoT) devices present in homes, there is a rise in the number of potential information leakage channels and their associated security threats and privacy risks. Despite a long history of attacks on IoT devices in unprotected home networks, the problem of accurate, rapid detection and prevention of such attacks remains open. Many existing IoT protection solutions are cloud-based, sometimes ineffective, and might share consumer data with unknown third parties. This paper investigates the potential for effective IoT threat detection locally, on a home router, using AI tools combined with classic rule-based traffic-filtering algorithms. Our results show that with a slight rise of router hardware resources caused by machine learning and traffic filtering logic, a typical home router instrumented with our solution is able to effectively detect risks and protect a typical home IoT network, equaling or outperforming existing popular solutions, without any effects on benign IoT functionality, and without relying on cloud services and third parties.

cs.CR↗

arXiv2023

Who Let the Smart Toaster Hack the House? An Investigation into the Security Vulnerabilities of Consumer IoT Devices

For smart homes to be safe homes, they must be designed with security in mind. Yet, despite the widespread proliferation of connected digital technologies in the home environment, there is a lack of research evaluating the security vulnerabilities and potential risks present within these systems. Our research presents a comprehensive methodology for conducting systematic IoT security attacks, intercepting network traffic and evaluating the security risks of smart home devices. We perform hundreds of automated experiments using 11 popular commercial IoT devices when deployed in a testbed, exposed to a series of real deployed attacks (flooding, port scanning and OS scanning). Our findings indicate that these devices are vulnerable to security attacks and our results are relevant to the security research community, device engineers and the users who rely on these technologies in their daily lives.

cs.CR↗

arXiv2023

PRISM: Privacy Preserving Healthcare Internet of Things Security Management

Consumer healthcare Internet of Things (IoT) devices are gaining popularity in our homes and hospitals. These devices provide continuous monitoring at a low cost and can be used to augment high-precision medical equipment. However, major challenges remain in applying pre-trained global models for anomaly detection on smart health monitoring, for a diverse set of individuals that they provide care for. In this paper, we propose PRISM, an edge-based system for experimenting with in-home smart healthcare devices. We develop a rigorous methodology that relies on automated IoT experimentation. We use a rich real-world dataset from in-home patient monitoring from 44 households of People Living With Dementia (PLWD) over two years. Our results indicate that anomalies can be identified with accuracy up to 99% and mean training times as low as 0.88 seconds. While all models achieve high accuracy when trained on the same patient, their accuracy degrades when evaluated on different patients.

eess.SY↗

arXiv2023

Protected or Porous: A Comparative Analysis of Threat Detection Capability of IoT Safeguards

Consumer Internet of Things (IoT) devices are increasingly common, from smart speakers to security cameras, in homes. Along with their benefits come potential privacy and security threats. To limit these threats a number of commercial services have become available (IoT safeguards). The safeguards claim to provide protection against IoT privacy risks and security threats. However, the effectiveness and the associated privacy risks of these safeguards remains a key open question. In this paper, we investigate the threat detection capabilities of IoT safeguards for the first time. We develop and release an approach for automated safeguards experimentation to reveal their response to common security threats and privacy risks. We perform thousands of automated experiments using popular commercial IoT safeguards when deployed in a large IoT testbed. Our results indicate not only that these devices may be ineffective in preventing risks, but also their cloud interactions and data collection operations may introduce privacy risks for the households that adopt them.

cs.CR↗

arXiv2021

Rapid IoT Device Identification at the Edge

Consumer Internet of Things (IoT) devices are increasingly common in everyday homes, from smart speakers to security cameras. Along with their benefits come potential privacy and security threats. To limit these threats we must implement solutions to filter IoT traffic at the edge. To this end the identification of the IoT device is the first natural step. In this paper we demonstrate a novel method of rapid IoT device identification that uses neural networks trained on device DNS traffic that can be captured from a DNS server on the local network. The method identifies devices by fitting a model to the first seconds of DNS second-level-domain traffic following their first connection. Since security and privacy threat detection often operate at a device specific level, rapid identification allows these strategies to be implemented immediately. Through a total of 51,000 rigorous automated experiments, we classify 30 consumer IoT devices from 27 different manufacturers with 82% and 93% accuracy for product type and device manufacturers respectively.

cs.LG↗

arXiv2021

Revisiting IoT Device Identification

Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such, they would greatly benefit from automated management. This requires robustly identifying devices so that appropriate network security policies can be applied. We address this challenge by exploring how to accurately identify IoT devices based on their network behavior, while leveraging approaches previously proposed by other researchers. We compare the accuracy of four different previously proposed machine learning models (tree-based and neural network-based) for identifying IoT devices. We use packet trace data collected over a period of six months from a large IoT test-bed. We show that, while all models achieve high accuracy when evaluated on the same dataset as they were trained on, their accuracy degrades over time, when evaluated on data collected outside the training set. We show that on average the models' accuracy degrades after a couple of weeks by up to 40 percentage points (on average between 12 and 21 percentage points). We argue that, in order to keep the models' accuracy at a high level, these need to be continuously updated.

cs.CR↗

arXiv2021

Blocking without Breaking: Identification and Mitigation of Non-Essential IoT Traffic

Despite the prevalence of Internet of Things (IoT) devices, there is little information about the purpose and risks of the Internet traffic these devices generate, and consumers have limited options for controlling those risks. A key open question is whether one can mitigate these risks by automatically blocking some of the Internet connections from IoT devices, without rendering the devices inoperable. In this paper, we address this question by developing a rigorous methodology that relies on automated IoT-device experimentation to reveal which network connections (and the information they expose) are essential, and which are not. We further develop strategies to automatically classify network traffic destinations as either required (i.e., their traffic is essential for devices to work properly) or not, hence allowing firewall rules to block traffic sent to non-required destinations without breaking the functionality of the device. We find that indeed 16 among the 31 devices we tested have at least one blockable non-required destination, with the maximum number of blockable destinations for a device being 11. We further analyze the destination of network traffic and find that all third parties observed in our experiments are blockable, while first and support parties are neither uniformly required or non-required. Finally, we demonstrate the limitations of existing blocklists on IoT traffic, propose a set of guidelines for automatically limiting non-essential IoT traffic, and we develop a prototype system that implements these guidelines.

cs.NI↗

arXiv2020

The Case for Retraining of ML Models for IoT Device Identification at the Edge

Internet-of-Things (IoT) devices are known to be the source of many security problems, and as such they would greatly benefit from automated management. This requires robustly identifying devices so that appropriate network security policies can be applied. We address this challenge by exploring how to accurately identify IoT devices based on their network behavior, using resources available at the edge of the network. In this paper, we compare the accuracy of five different machine learning models (tree-based and neural network-based) for identifying IoT devices by using packet trace data from a large IoT test-bed, showing that all models need to be updated over time to avoid significant degradation in accuracy. In order to effectively update the models, we find that it is necessary to use data gathered from the deployment environment, e.g., the household. We therefore evaluate our approach using hardware resources and data sources representative of those that would be available at the edge of the network, such as in an IoT deployment. We show that updating neural network-based models at the edge is feasible, as they require low computational and memory resources and their structure is amenable to being updated. Our results show that it is possible to achieve device identification and categorization with over 80% and 90% accuracy respectively at the edge.

cs.NI↗

arXiv2020

A Haystack Full of Needles: Scalable Detection of IoT Devices in the Wild

Consumer Internet of Things (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. These functionalities often come with significant privacy and security risks, with notable recent large scale coordinated global attacks disrupting large service providers. Thus, an important first step to address these risks is to know what IoT devices are where in a network. While some limited solutions exist, a key question is whether device discovery can be done by Internet service providers that only see sampled flow statistics. In particular, it is challenging for an ISP to efficiently and effectively track and trace activity from IoT devices deployed by its millions of subscribers --all with sampled network data. In this paper, we develop and evaluate a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in-the-wild. Our findings indicate that millions of IoT devices are detectable and identifiable within hours, both at a major ISP as well as an IXP, using passive, sparsely sampled network flow headers. Our methodology is able to detect devices from more than 77% of the studied IoT manufacturers, including popular devices such as smart speakers. While our methodology is effective for providing network analytics, it also highlights significant privacy consequences.

cs.NI↗

arXiv2020

Towards Automatic Identification and Blocking of Non-Critical IoT Traffic Destinations

The consumer Internet of Things (IoT) space has experienced a significant rise in popularity in the recent years. From smart speakers, to baby monitors, and smart kettles and TVs, these devices are increasingly found in households around the world while users may be unaware of the risks associated with owning these devices. Previous work showed that these devices can threaten individuals' privacy and security by exposing information online to a large number of service providers and third party analytics services. Our analysis shows that many of these Internet connections (and the information they expose) are neither critical, nor even essential to the operation of these devices. However, automatically separating out critical from non-critical network traffic for an IoT device is nontrivial, and requires expert analysis based on manual experimentation in a controlled setting. In this paper, we investigate whether it is possible to automatically classify network traffic destinations as either critical (essential for devices to function properly) or not, hence allowing the home gateway to act as a selective firewall to block undesired, non-critical destinations. Our initial results demonstrate that some IoT devices contact destinations that are not critical to their operation, and there is no impact on device functionality if these destinations are blocked. We take the first steps towards designing and evaluating IoTrimmer, a framework for automated testing and analysis of various destinations contacted by devices, and selectively blocking the ones that do not impact device functionality.

cs.NI↗