Open-weights large language models and low-cost steering methods are strongly democratising the crafting of custom artificial intelligence-based assistants. This benefit comes with the side effect of expanding the potential risks associated with the harmful, toxic, or other undesired uses of neural language models. Language model immunisation is a quite novel research area that seeks to mitigate these risks. Immunised models are pre-trained models whose weights are hard to fine-tune toward harmful or dual tasks. While existing works on immunisation focus on resistance against full-parameter or parameter-efficient fine-tuning, this paper proposes a candidate strategy to neutralise models against low-cost attacks based on Inference-Time interventions (ITI). The proposed approach is called Ethical Treatment ( E.T. ), 1 1 The term ‘Ethical Treatment’ refers to the technical process of immunising models, not to solving normative ethical questions. and consists of training layer-wise low-rank adaptors to locally neutralise attacks at the decoder-block level of Transformer-based models. Pilot experiments on Llama-3-8B-Instruct demonstrate E.T. ’s effectiveness in reducing ITI-attack success rates while preserving utility on general-purpose tasks. Evaluation across the TinyBenchmarks suite shows that E.T. maintains strong performance on commonsense reasoning, and world knowledge, with primary degradation limited to mathematical reasoning. While not solving the broader immunisation challenge, these results position E.T. as a promising step toward structurally robust open-weight models. 2 2 Code, reproducibility scripts, and compute requirement specification are available at https://github.com/DISTA-HCAI/ET .

The paper presents Open-FARI, an open-source testbed for evaluating federated learning algorithms for anomaly detection in the railway Industrial Internet of Things domain. Open-FARI uses synthetic data generation modules trained from real train sensor data to generate realistic sensor data of a fleet of trains. Generated data encompass normal and anomalous data, enabling the evaluation of federated learning algorithms for anomaly detection. The paper addresses the lack of testbeds and datasets tailored to the railway domain, which represents an obstacle to research on Machine Learning-driven solutions in this domain.

Recent trends in zero-day attack (ZdA) detection use collective anomaly detection to give insights on out-of-distribution anomalies in a zero-shot fashion. Among these, existing frameworks propose the use of specialised labelling strategies to mimic a step-wise abstract anomaly detection algorithm that generalise ZdA-detection over low-dimensional traffic-flow statistics. To enlarge such applicative scenarios, this paper proposes hero , which is compatible with H igh-dimensional raw-network traffic captures when performing z ERO -day attack detection. To reach convergence over such a high-dimensional and noisy input space, hero decouples the representation task and the correspondent gradient updates from the discriminative task, following the neural algorithmic reasoning blueprint. Specifically, a neural processor is first trained on the discriminative task using synthetic data, and the weights are then frozen. A second training phase successfully optimises the encoding and decoding networks using raw-traffic captures and the algorithmically-aligned processor. Experiments with well-known intrusion detection datasets demonstrate the crucial advantage of using a two-stage training framework to achieve convergence. To the best of the authors’ knowledge, hero is the first deep learning-based instrument that performs collective anomaly detection and categorisation over raw network traffic on a zero-shot basis, i.e., without using labels.

Application containerization allows for efficient resource utilization and improved performance when compared to traditional virtualization techniques . However, managing multiple containers and providing services such as load balancing, fault tolerance and security represent challenging tasks in the emerging microservices architectures. In this context, Kubernetes platform allows to build resilient distributed containers. Besides its efficiency in terms of configuration and architectural resiliency, it must also guarantee the access control to the managed resources. In fact, information must be protected throughout the different microservices which compose an application. To cope with such an issue, this paper proposes the definition of attribute-based policies able to regulate data disclosure within a Kubernetes-based microservices network. Simulations are carried out in a local Minikube environment, considering a smart residence scenario. The investigated metrics include response time , required memory, CPU load, and disk usage.

The modeling and simulation of Internet of Things (IoT) and Industrial IoT (IIoT) systems allow practitioners to obtain valuable insights into the system's behavior before their actual deployment in the field. Early designing permits the analysis of the interactions among the involved entities, evaluating the effects of modifications, and understanding the impact of failures on the system. In particular, this is exacerbated in the context of IoT/IIoT, which is characterized by multiple and heterogeneous subsystems, different processing levels, and communication protocols. In such a direction, recent innovations in IT devices have enabled the rail industry to gather information from Train Control and Monitoring Systems (TCMS) to check conditions constantly and prevent issues, thus improving relia-bility and safety and, in some cases, leading to cost-saving by optimizing maintenance resources. In such a scenario, this paper presents RailRED, a framework for simulating and prototyping a TCMS based on the Node-RED tool. In RaiIRED, the main TCMS subsystems are modeled using Node-RED flows, while the subsystem interconnections are performed through a low footprint and encrypted gateway based on the MQTT protocol. The proposal can also generate diagnostic data that mimic the behavior of a real-world TCMS. RailRED communication latency and its ability to generate diagnostic data have been analyzed, with the latter evaluated by using clusters of diagnostic events collected from a real-world TCMS running on a high-speed train.

Zero-day attack detection and categorization is an open-research field where four main context factors need to be taken into account: novel or zero-day attacks (i) are unlabeled by definition, (ii) may correspond to out-of-distribution data, (iii) can arise concurrently, and (iv) distribution shifts in the feature space need online-learning. Given such constraints, the online detection and categorization of new cyber threats can be modeled as a heterogeneous collective anomaly detection problem, for which no online-learning solutions exist purely based on back-propagation. To this respect, this paper presents an online-learning, end-to-end back-propagation strategy for Automatically Synthesizing the potential signatures or Attack Prototypes of novel cyber threats ( asap ). The presented framework incorporates automatic feature engineering, operating over raw data from the OpenFlow monitoring API and raw bytes of traffic captures. In asap , specialized inductive biases enhance the training data efficiency and accommodate the inference machinery to resource-constrained scenarios such as the Internet of Things. Finally, the validity of this framework is demonstrated in a live training experiment comprising IoT traffic emulation 3 3 To foster research in the field, the source-code of asap alongside instructions to reproduce the experiments are made publicly available among the set of SmartVille NID models in [1] . .

Anomaly detection approaches for network intrusion detection learn to identify deviations from normal behavior on a data-driven basis. However, current approaches strive to infer the degree of abnormality of out-of-distribution samples when these appertain to different zero-day attacks. Inspired by the successes of the neural algorithmic reasoning paradigm to leverage the generalization of rule-based behavior, this paper presents a deep learning strategy for solving zero-day network attack detection and categorization. Moreover, focusing on the particular scenario of the Internet of Things (IoT), the privacy preservation requirement may imply a low training data regime for any learning algorithm. To this respect, the presented framework uses metric-based meta-learning to achieve few-shot learning capabilities. The presented pipeline is called NERO, as it imports the encode-process-decode architecture from the NEural algorithmic reasoning blueprint to converge zeRO-day attack detection policies within constrained training data.

Healthcare supply chain domain and medical records’ management face numerous challenges that come with new demands, such as customer dissatisfaction, rising healthcare costs, tracking and traceability of drugs, and security and privacy related to the sensitive information managed in such a domain. Executing processes related to healthcare domain in a trusted, secure, efficient, accessible and traceable manner is challenging due to the fragmented nature of the healthcare supply chain, which is prone to systemic errors and redundant efforts that may compromise patient safety and negatively impact health outcomes. To cope with such issues, blockchain technology, combined with the Internet of Things (IoT), can offers a reliable way to track and trace products and protect medical data though a peer-to-peer distributed, secure, and shared ledger. Hence, this paper proposes an IoT-driven blockchain-based architecture to manage the healthcare supply chain and protect medical records from tampering and access violation. Hyperledger Fabric, which is a permissioned blockchain, has been adopted due to the sensitive and private nature of the collected data. The envisioned network has been implemented and performance has been evaluated in terms of execution time, resources consumption and throughput.

Abstract Security and privacy of information transmitted among the devices involved in an Internet of Things (IoT) network represent relevant issues in IoT contexts. Guaranteeing effective control and supervising access permissions to IoT applications is a complex task, mainly due to resources' heterogeneity and scalability requirements. The design and development of highly customizable access control policies, along with an efficient mechanism for ensuring that the rules applied by the IoT platform are not tampered with or violated, will undoubtedly have a significant impact on the diffusion of IoT‐based solutions. In such a direction, the article proposes the integration of a permissioned blockchain within an honest‐but‐curious (i.e., not trusted) IoT distributed middleware layer, which aims to guarantee the correct management of access to resources by the interested parties. The result is a robust and lightweight system, able to manage the data produced by IoT devices, support relevant security features, such as integrity and confidentiality, and resist different kinds of attacks. The use of blockchain will ensure the tamper‐resistance and synchronization of the distributed system, where various stakeholders own applications and IoT platforms. The methodology and the proposed architecture are validated employing a test‐bed.

Abstract The Internet of Things (IoT) paradigm is characterized by the adoption of different protocols and standards to enable communications among heterogeneous and, often, resource-constrained devices. The risk of violation is high due to the wireless nature of the communication protocols usually involved in the IoT environments (e.g., e-health, smart agriculture, industry 4.0, military scenarios). For such a reason, proper security countermeasures must be undertaken, in order to prevent and react to malicious attacks, which could hinder the data reliability. In particular, the following requirements should be addressed: authentication, confidentiality, integrity, and authorization. This paper aims at investigating such security features, which are often combined with native functionalities, in the most known IoT-related protocols: MQTT, CoAP, LoRaWAN, AMQP, RFID, ZigBee, and Sigfox. The advantages and weaknesses of each one will be revealed, in order to point out open issues and best practices in the design of efficient and robust IoT network infrastructure.

In the digital age, where the Internet connects things across the globe and individuals are constantly online, data security and privacy are becoming key drivers (and barriers) of change for adoption of innovative solutions. Traditional approaches, whereby communication links are secured by means of encryption, and access control is run in a static way by a centralized authority, are showing their limits when applied to massive-scale, interconnected and distributed systems. Regulations, while still fragmented, are moving to adapt to changes in technology and society, with the aim to protect confidential information by governments, businesses, and individual citizens. In this landscape, proper mechanisms should be defined to allow a strict control over the data life-cycle and to guarantee the privacy and the application of specific regulations on personal information's disclosure, usage and access. Sticky policies represent one approach to improve owners' control over their data. In such an approach, machine-readable policies are attached to data. They are called 'sticky' in that they travel together with data, as data travels across multiple administrative domains. In this article we survey the state-of-the-art in sticky policies, discussing limitations, open issues, applications and research challenges, with a specific focus on their applicability to Internet of Things, cloud computing, and Content Centric Networking.

Summary The paper proposes the use of Node‐RED, a flow‐based programming tool targeted to Internet of Things (IoT), along with a series of case studies related to different IoT contexts, which demonstrate Node‐RED's potentialities and outcomings toward the realization of well‐structured IoT environments. The analyzed applications potentially include a wide range of domains, ranging from smart cities, smart buildings, smart homes/offices, smart retailing, to smart transportation, smart logistics, smart agriculture, smart health, military scenarios, and so on. The motivations behind the presented work are related to the fact that IoT application fields usually involve the same technologies and communication protocols, which are frequently adopted for totally different purposes. Issues such as systems' interoperabiliy, scalability, security and privacy naturally emerge, due to the huge amount of heterogeneous devices acting in the IoT environment itself and to the wireless nature of information transmissions. As a consequence, it is fundamental to dispose of adequate tools for supporting developers in design the network architecture and messages' exchange, in order to realize efficient and effective IoT network infrastructures.

Security and privacy represent critical issues for a wide adoption of Internet of Things (IoT) technologies both by industries and people in their every-day life. Besides, the complexity of an IoT system's management resides in the presence of heterogeneous devices, which communicate by means of different protocols and provide information belonging to various application domains. Hence, adequate policies must be correctly distributed and applied to the information made available by the IoT network to secure the data themselves and to regulate the access to the managed resources over the whole IoT system. Policies mainly involve the access to resources and are usually established by system administrators in accordance with the rules of each specific domain. Since IoT concerns multiple application fields and often wide areas, a centralized solution which manages all the required policies would not be neither efficient nor scalable. Therefore, in this paper, a distributed middleware overlying the IoT network is proposed and integrated with a synchronization system for guaranteeing the correct distribution, update, and application of the policies across the entire IoT environment in real-time. Such a distribution and synchronization system has been developed within a policy enforcement framework. The presented solution has been validated by means of a simple yet real prototype; the analyzed metrics regard delay, overhead and robustness of the proposed enforcement and synchronization framework.

Many solutions based on machine learning techniques have been proposed in literature aimed at detecting and promptly counteracting various kinds of malicious attack (data violation, clone, sybil, neglect, greed, and DoS attacks), which frequently affect Wireless Sensor Networks (WSNs). Besides recognizing the corrupted or violated information, also the attackers should be identified, in order to activate the proper countermeasures for preserving network’s resources and to mitigate their malicious effects. To this end, techniques adopting Self-Organizing Maps (SOM) for intrusion detection in WSN were revealed to represent a valuable and effective solution to the problem. In this paper, the mechanism, namely, Good Network (GoNe), which is based on SOM and is able to assess the reliability of the sensor nodes, is compared with another relevant and similar work existing in literature. Extensive performance simulations, in terms of nodes’ classification, attacks’ identification, data accuracy, energy consumption, and signalling overhead, have been carried out in order to demonstrate the better feasibility and efficiency of the proposed solution in WSN field.

The continuous interest in Wireless Sensor Networks (WSN) has led to the development of several applications, from traditional monitoring, to cooperative and distributed control and management systems, to automated industrial machinery and logistics. The design and optimization of specialized WSN platforms and communication protocols typically relies on simulation tools, which have been designed to explore and validate WSN systems before actual implementation and real world deployment. In this paper, we evaluate the performance and the accuracy of mainstream open source simulation tools for WSNs on a realistic multi-hop data passing benchmark which makes use of the Ad-hoc On Demand Distance Vector Routing (AODV) protocol. The simulation results are then compared against measurements on a physical prototype. Our experiments show that the tools produce equivalent and consistent results from a functional point of view. However, their ability to model details of the execution platform and of the communication channel may significantly impact the run-time simulation performance and the accuracy of the simulation results.

Over recent years, the continuous interest in wireless sensor networks (WSNs) has led to the appearance of new modeling methods and simulation environments for WSN applications. A broad variety of different simulation tools have been designed to explore and validate WSN systems before actual implementation and real-world deployment. These tools address different design aspects and offer various simulation abstractions to represent and model real-world behavior. In this article, we present a comprehensive comparative study of mainstream open-source simulation tools for WSNs. Two benchmark applications are designed to evaluate the frameworks with respect to the simulation runtime performance, network throughput, communication medium modeling, packet reception rate, network latency, and power consumption estimation accuracy. Such metrics are also evaluated against measurements on physical prototypes. Our experiments show that the tools produce equivalent results from a functional point of view and capacity to model communication phenomena, while the ability to model details of the execution platform significantly impacts the runtime simulation performance and the power estimation accuracy. The benchmark applications are also made available in the public domain for further studies.

Internet of Things (IoT) involves heterogeneous technologies (i.e., WSN, RFID, actuators) able to exchange data acquired from the environment in order to provide services to the requesting users. In such a scenario the privacy and the quality (i.e., in terms of accuracy, timeliness, completeness) of the handled information represent critical issues. In fact, the provided services must be customized according to the users preferences and habits and have to manage both users personal information and data from different sources, therefore it needs to guarantee privacy and data quality level. This work proposes a UML general conceptual model, which defines the entities involved in the IoT context, their relationships, facing privacy policies definition and data quality assessment. Such a model should represent a starting point for the development of IoT privacy-aware solutions, handling data with a well-defined quality.

Secure data aggregation still represents a very challenging topic in wireless sensor networks' research. In fact, only few solutions exists to face, simultaneously, confidentiality, integrity, adaptive aggregation, and privacy issues. Furthermore, proposals available in literature mainly assume flat network architectures, without leveraging the peculiarities of clustered wireless sensor networks, which are very common in real life deployments. This work tries to bridge the gap by proposing a solution, namely SETA, tailored to a hybrid architecture composed of wireless sensor nodes and wireless mesh routers as cluster heads. In SETA, to lower the processing workload of sensor nodes, only cluster heads are allowed to perform integrity verification checks and message merging operations to face possible network congestions. To prove its effectiveness, it has been compared, using simulations, with respect to DyDAP in several realistic settings. Results have shown that it can provide a slight improvement of the robustness to malicious nodes and of the sensing accuracy, while increasing the overall energy efficiency and decreasing the signaling overhead in the network.