Most machine learning-based Intrusion Detection Systems (IDSs) are designed for stationary environments, where data distributions is assumed to remain constant over time. However, modern network environments are dynamic, and this can lead to significant changes in the observed environment since the training phase, causing degradation in IDS performance. Consequently, increasing attention has been given to online learning techniques designed to address such phenomenon, known as concept drift. Designing such adaptive systems is a far from trivial task, due to a multitude of factors, such as experimental biases as well as the lack of real-world labeled datasets with precise drift annotations. Moreover, the evaluation of such systems still lacks a standardized methodology, and critical aspects are often inconsistently addressed, making comparisons between approaches particularly difficult. To address these challenges, this work proposes REFINE, a Robust Evaluation Framework for IDS under concept drift in dynamic environments. REFINE combines a Concept Drift Stream Generator (CDSG), which produces realistic datasets from real network traffic with controlled drift characteristics, and a robust online evaluation pipeline that mitigates experimental biases. Results demonstrate that REFINE enables accurate, unbiased evaluation and comparison of online IDSs, providing critical insights into their adaptation and detection capabilities across various drift scenarios.

Recent advances in distributed vehicle-to-vehicle communication promise to transform the user’s driving ex- perience, providing new services capable of improving safety, efficiency and quality of travelling. Due to the large amount of information exchanged, a major challenge of Vehicular Networks is the adoption of appropri- ate data dissemination protocols that ensure good performance in real-time event detection, while guarantee- ing low communication overhead. To this aim, this paper proposes an adaptive event dissemination algorithm which exploits Population Protocols (PPs) for modelling vehicle interactions as coordinated behaviors of au- tonomous agents in a distributed system. The experimental evaluation performed on realistic vehicle tracks over real-world maps demonstrates the system’s ability to efficiently disseminate information in the network in order to support reliable and distributed event detection services.

In offline retail settings, there are two major challenges: improving the customer experience through personalized product recommendations and optimizing inventory management through accurate sales forecasting. Conventional recommendation systems assist customers in selecting goods based on personal preferences, similar user behavior, and popularity trends, while forecasting systems help managers predict future sales and optimize inventory levels. However, existing approaches face limitations in offline retail environments due to the scarcity of explicit feedback and the complexity of in-store interactions. To address these limitations, this paper introduces a hybrid intelligent system that combines multiple recommendation paradigms with predictive modeling techniques. By leveraging Recurrent Neural Networks and data-driven statistical models, the system improves both recommendation accuracy and demand forecasting reliability compared to traditional approaches. The effectiveness of the proposed system has been thoroughly evaluated using standard metrics such as Mean Reciprocal Rank at K (MRR@K) and Hit Rate at K (HR@K). Experimental results confirm the effectiveness of the proposed approach in balancing personalization and accuracy, offering significant benefits in offline retail environments.

The Smart Connected Communities paradigm, which synergistically integrates smart technologies with the surrounding environment, has paved the way for a new generation of applications that provide increasingly intelligent services by leveraging information coming from users, and the IoT. While user collaboration is essential to improve the quality of information (QoI), the interest of providers in data can jeopardize the right to privacy by revealing details that users are not willing to share (e.g., habits, health status). In addition, not all involved users consistently exhibit cooperative behavior, and the presence of attackers often undermines the quality of the collected information. In this paper, we propose a system for aggregating and analyzing user data without ever compromising their privacy, whilst improving QoI. The system uses Privacy Preserving Computation techniques, clustering, and an outlier removal step to improve the quality of information. Utilizing a real-world dataset, we tested our system, demonstrating its resilience in a scenario with potential attackers and its superior performance compared to other state-of-the-art systems.

The dynamic characteristics of Internet of Things (IoT) systems create major challenges for threat detection systems that rely on machine learning models. Over time, shifts in the statistical distribution of data can lead to drastic performance degradation. This phenomenon is known as concept drift. When this problem occurs, traditional static systems require human intervention to manually retrain, leaving the network vulnerable in the meantime. In this paper, we propose an unsupervised system for online detection of anomalous traffic generated by malware-infected IoT devices. The proposed multi-tier system explicitly accounts for concept drift, automatically retraining only when necessary. We thoroughly tested the system by performing an extensive experimental evaluation using the real-world IoT-23 dataset, which includes network traffic generated by IoT devices as well as malicious network traffic generated by devices infected with different types of malware. We also compared our approach with other state-of-the-art work, and the results showed the remarkable performance achieved by the system using key metrics such as F1 score, accuracy, false positive rate and false negative rate.

Many crowdsensing applications today rely on learning algorithms applied to data streams to accurately classify information and events of interest in smart environments. Unfor-tunately, the statistical properties of the input data may change in unexpected ways. As a result, the definition of anomalous and normal data can vary over time and machine learning models may need to be re-trained incrementally. This problem is known as concept drift, and it has often been ignored by anomaly detection systems, resulting in significant performance degradation. In addition, the statistical distribution of past data often tends to repeat itself, and thus old learning models could be reused, avoiding costly retraining phases on new data, which would waste computational and energy resources. In this paper, we propose a hybrid anomaly detection system for streaming data in smart environments that accounts for concept drift and minimize the number of machine learning models that need to be retrained when shifts in incoming data distribution are detected. The system is multi-tier and relies on two different concept drift detection modules and an ensemble of anomaly detection models. An extensive experimental evaluation has been carried out, using two real datasets and a synthetic one; results show the high performance achieved by the system using common metrics such as F1-score and accuracy.

Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible with traditional sensor networks. Given the participatory nature of mobile crowdsensing, it is imperative to incentivize mobile users to provide sensing services in a timely and reliable manner. Most importantly, given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility pattern, which is often uncertain. For this reason, in this paper, we propose IncentMe, a framework that solves this core issue by leveraging game-theoretical reverse auction mechanism design. After demonstrating that the proposed problem is NP-hard, we derive two mechanisms that are parallelizable and achieve higher approximation ratio than existing work. IncentMe has been extensively evaluated on a road traffic monitoring application implemented using mobility traces of taxi cabs in San Francisco, Rome, and Beijing. Results demonstrate that the mechanisms in IncentMe outperform the state of the art work by improving the efficiency in recruiting participants by 30 percent.

Thanks to the collective action of participating smartphone users, mobile crowdsensing allows data collection at a scale and pace that was once impossible. The biggest challenge to overcome in mobile crowdsensing is that participants may exhibit malicious or unreliable behavior, thus compromising the accuracy of the data collection process. Therefore, it becomes imperative to design algorithms to accurately classify between reliable and unreliable sensing reports. To address this crucial issue, we propose a novel Framework for optimizing Information Reliability in Smartphone-based participaTory sensing (FIRST) that leverages mobile trusted participants (MTPs) to securely assess the reliability of sensing reports. FIRST models and solves the challenging problem of determining before deployment the minimum number of MTPs to be used to achieve desired classification accuracy. After a rigorous mathematical study of its performance, we extensively evaluate FIRST through an implementation in iOS and Android of a room occupancy monitoring system and through simulations with real-world mobility traces. Experimental results demonstrate that FIRST reduces significantly the impact of three security attacks (i.e., corruption, on/off, and collusion) by achieving a classification accuracy of almost 80% in the considered scenarios. Finally, we discuss our ongoing research efforts to test the performance of FIRST as part of the National Map Corps project.

Technological progress in recent years has allowed the design of new intelligent learning systems in smart environments aiming to facilitate users' lives. As a consequence, besides making use of traditional sensors for monitoring the quantities of interest, such systems can also benefit from information obtained from the users' smart devices, which can now be considered as additional sensing tools. In this article, we present the design of a novel system based on the fog computing paradigm that can improve the services offered to users on a smart campus by using different smart devices, i.e., smartphones, smartwatches, tablets, smartcameras and so on. In particular, we will describe a system in which several smart devices will collect sensory and context information, whilst the cloud will aggregate and analyze this data to extract information of particular interest. The main challenge of this project is to create an intelligent platform that allows new software modules to be added without having to re-design the entire architecture, and that can provide new services to campus users or improve existing ones.

In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide users with more and more functions that make them real sensing platforms. Exploiting the capabilities offered by smartphones, users can collect data from the surrounding environment and share them with other entities in the network thanks to existing communication infrastructures, i.e., 3G/4G/5G or WiFi. In this work, we present a system based on participatory sensing paradigm using smartphones to collect and share local data in order to monitor make a campus "smart". In particular, our system infers the activities performed by users (e.g., students) in a campus in order to identify trends and behavioral patterns. This information allows the system to decide in real-time which actions are needed to provide the best possible services to users, according to their needs and preferences.

The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual information in the fusion process, to increase the accuracy of inference and hence decision making in a dynamically changing environment. In this paper, we propose a context-aware, self-optimizing, adaptive system for sensor data fusion, based on a three-tier architecture. Heterogeneous data collected by sensors at the lowest tier are combined by a dynamic Bayesian network at the intermediate tier, which also integrates contextual information to refine the inference process. At the highest tier, a self-optimization process dynamically reconfigures the sensory infrastructure, by sampling a subset of sensors in order to minimize energy consumption and maximize inference accuracy. A Bayesian approach allows to deal with the imprecision of sensory measurements, due to environmental noise and possible hardware malfunctions. The effectiveness of our approach is demonstrated with the application scenario of the user activity recognition in an Ambient Intelligence system managing a smart home environment. Experimental results show that the proposed solution outperforms static approaches for context-aware multi-sensor fusion, achieving substantial energy savings whilst maintaining a high degree of inference accuracy.