A Kohonen SOM architecture for intrusion detection on in-vehicle communication networks

The diffusion of connected devices in modern vehicles involves a lack in security of the in-vehicle communication networks such as the controller area network (CAN) bus. The CAN bus protocol does not provide security systems to counter cyber and physical attacks. Thus, an intrusion-detection system to identify attacks and anomalies on the CAN bus is desirable. In the present work, we propose a distance-based intrusion-detection network aimed at identifying attack messages injected on a CAN bus using a Kohonen self-organizing map (SOM) network. It is a power classifier that can be trained both as supervised and unsupervised learning. SOM found broad application in security issues, but was never performed on in-vehicle communication networks. We performed two approaches, first using a supervised X-Y fused Kohonen network (XYF) and then combining the XYF network with a K-means clustering algorithm (XYF-K) in order to improve the efficiency of the network. The models were tested on an open source dataset concerning data messages sent on a CAN bus 2.0B and containing large traffic volume with a low number of features and more than 2000 different attack types, sent totally at random. Despite the complex structure of the CAN bus dataset, the proposed architectures showed a high performance in the accuracy of the detection of attack messages

Intrusion detection for in-vehicle communication networks: An unsupervised kohonen SOM approach

The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of its features of high detection rate, short training time, and high versatility. We propose to extend the SOM network to intrusion detection on in-vehicle CAN buses. Many hybrid approaches were proposed to combine the SOM network with other clustering methods, such as the k-means algorithm, in order to improve the accuracy of the model. We introduced a novel distance-based procedure to integrate the SOM network with the K-means algorithm and compared it with the traditional procedure. The models were tested on a car hacking dataset concerning traffic data messages sent on a CAN bus, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The experimentation showed that the proposed method greatly improved detection accuracy over the traditional approach

Potential of Cellulose-Based Superabsorbent Hydrogels as Water Reservoir in Agriculture

The present work deals with the development of a biodegradable superabsorbent hydrogel, based on cellulose derivatives, for the optimization of water resources in agriculture, horticulture and, more in general, for instilling a wiser and savvier approach to water consumption. The sorption capability of the proposed hydrogel was firstly assessed, with specific regard to two variables that might play a key role in the soil environment, that is, ionic strength and pH. Moreover, a preliminary evaluation of the hydrogel potential as water reservoir in agriculture was performed by using the hydrogel in experimental greenhouses, for the cultivation of tomatoes. The soil-water retention curve, in the presence of different hydrogel amounts, was also analysed. The preliminary results showed that the material allowed an efficient storage and sustained release of water to the soil and the plant roots. Although further investigations should be performed to completely characterize the interaction between the hydrogel and the soil, such findings suggest that the envisaged use of the hydrogel on a large scale might have a revolutionary impact on the optimization of water resources management in agriculture

Neural Network Learning Algorithms for High-Precision Position Control and Drift Attenuation in Robotic Manipulators

In this paper, different learning methods based on Artificial Neural Networks (ANNs) are examined to replace the default speed controller for high-precision position control and drift attenuation in robotic manipulators. ANN learning methods including Levenberg–Marquardt and Bayesian Regression are implemented and compared using a UR5 robot with six degrees of freedom to improve trajectory tracking and minimize position error. Extensive simulation and experimental tests on the identification and control of the robot by means of the neural network controllers yield comparable results with respect to the classical controller, showing the feasibility of the proposed approach

Cork-derived hierarchically porous hydroxyapatite with different stoichiometries for biomedical and environmental applications

Hierarchically porous hydroxyapatite derived from cork powder shows excellent performance in biomedicine (low cytotoxicity) and environmental remediation (high Pb2+ removal)

A Tracked Mobile Robotic Lab for Monitoring the Plants Volume and Health

9noPrecision agriculture has been increasingly recognized for its potential ability to improve agricultural productivity, reduce production cost, and minimize damage to the environment. In this work, the current stage of our research in developing a mobile platform equipped with different sensors for orchard monitoring and sensing is presented. In particular, the mobile platform is conceived to monitor and assess both the geometric and volumetric conditions as well as the health state of the canopy. To do so, different sensors have been integrated and efficient data-processing algorithms implemented for a reliable crop monitoring. Experimental tests have been performed allowing to obtain both a precise volume reconstruction of several plants and an NDVI mapping suitable for vegetation state evaluations.openopenopenBietresato, M; Carabin, G; D’Auria, D; Gallo, R; Gasparetto, A.; Ristorto, G; Mazzetto, F; Vidoni, R; Scalera, L.Bietresato, M; Carabin, G; D’Auria, D; Gallo, R; Gasparetto, Alessandro; Ristorto, G; Mazzetto, F; Vidoni, R; Scalera, Lorenz

Valorisation of tuna bone waste through its application for the removal of persistent pharmaceuticals from water matrices

The access to safe and clean water is a critical issue faced by our society. One of the major problems is the presence of contaminants of emerging concern (CECs) in water bodies. CECs include pollutants with poor removal rates in wastewater treatment plants, causing adverse effects on ecosystems and humans. Within CECs, pharmaceuticals received increasing attention due to their continuous release into aquatic environments. Therefore, innovative and sustainable solutions to address this problem are needed.In this work a material for pollutants adsorption was developed from fish bones (tuna), a byproduct of the food industry. The powdery material was obtained by pyrolysis of the bones, leading to tuna bone char (TBC), a composite of hydroxyapatite (Ca10(PO4)6(OH)2) and graphitic carbon. The capacity of the TBC to adsorb tramadol (TRA) and venlafaxine (VNF), two pharmaceuticals increasingly detected in the environment, was evaluated. The batch adsorption assays were performed in different aqueous matrices, some simulating real wastewater with different salinity levels (up to 12 g/L).The results show that TBC can be successfully applied for the adsorption processes ofpersistent pharmaceuticals, with the salinity levels affecting the efficiency of the removal.Overall, the work presents an alternative strategy for the removal of pharmaceuticals from aqueous matrices whilst contributing for mitigating the solid waste generated by the fish industry.info:eu-repo/semantics/publishedVersio

A Full-Field Calibration Approach on Material Parameter Identification

In the recent years, the usage of HS-steels has risen significantly in the automotive field. Their characteristics, such as hardness and favorable weight to strength ratio, can increase safety, fuel efficiency and overall product profitability. In this context, for the design with this material it has become crucial to be able to characterize precisely HS-steels and accurately predict their failure in many complex conditions, to fully exploit their capabilities. One of the most accredited ways to approach the prediction of failure for a wide range of materials is the generalized incremental stress-state dependent damage model GISSMO. The model is highly flexible and provides a framework inside LS-DYNA in which failure parameters can be tuned to reproduce experimental data. The definition of the optimal parameters is an inverse problem, therefore it was implemented using LS-OPT. In this work, the experimental evaluation of the MS1500 was carried out using the digital image correlation (DIC). With such technology, the displacements’ field of the test specimen is recorded.The evalueted field was processed as a family of stress-strain curves (hyper-curves) and became the objective of the optimization. This approach is named full field calibration and in this work was split in two phases. First, the stress-strain curve of the material was defined, then the tuning of the GISSMO parameters was performed. To evaluate the effectiveness of the full field approach a parallel study was implemented. The same routine of optimization run with a single stress-strain curve, which was measured with an extensometer. The comparison between the results obtained with the traditional approach and the results obtained with the full field approach highlighted the strenghts and the limitations of the two methods