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In this paper, we present a system that allows visually impaired people to autonomously navigate in an unknown indoor and outdoor environment. The system, explicitly designed for low vision people, can be generalized to other users in an easy way. We assume that special landmarks are posed for helping the users in the localization of pre-defined paths. Our novel approach exploits the use of both the inertial sensors and the camera integrated into the smartphone as sensors. Such a navigation system can also provide direction estimates to the tracking system to the users. The success of out approach is proved both through experimental tests performed in controlled indoor environments and in real outdoor installations. A comparison with deep learning methods has been presented. © 2013 IEEE.

Industrial Control Devices are one of the major targets for hackers due to their exposure to threats. The principle of "air gaps" (disconnecting the Industrial Control Network from the operational networks) is not anymore feasible in a connected world. In this paper, a host anomaly detection system for Critical Infrastructures networks is presented. The device, called Smart Extension, also implements a filtering strategy in order to secure a single host reacting to cyber threats. Therefore, it is positioned in the network between PLC (Programmable Logic Controller) and the SCADA (Supervisory Control and Data Acquisition) control centre, more precisely just in front of the PLC. Finally, experimental results are shown in order to explain the internal working procedures in a possible case study.

Over the past decade, industrial control systems have experienced a massive integration with information technologies. Industrial networks have undergone numerous technical transformations to protect operational and production processes, leading today to a new industrial revolution. Information Technology tools are not able to guarantee confidentiality, integrity and availability in the industrial domain, therefore it is of paramount importance to understand the interaction of the physical components with the networks. For this reason, usually, the industrial control systems are an example of Cyber-Physical Systems (CPS). This paper aims to provide a tool for the detection of cyber attacks in cyber-physical systems. This method is based on Machine Learning to increase the security of the system. Through the analysis of the values assumed by Machine Learning it is possible to evaluate the classification performance of the three models. The model obtained using the training set, allows to classify a sample of anomalous behavior and a sample that is related to normal behavior. The attack identification is implemented in water tank system, and the identification approach using Machine Learning aims to avoid dangerous states, such as the overflow of a tank. The results are promising, demonstrating its effectiveness.

Critical Infrastructures (CIs) such as power grid, water and gas distribution are controlled by Industrial Control Systems (ICS). Sensors and actuators of a physical plant are managed by the ICS. Data and commands transmitted over the network from the Programmable Logic Controllers (PLCs) are saved and parsed within the Historian. Generally, this architecture guarantees to check for any process anomalies that may occur due to component failures and cyber attacks. The other use of this data allows activities such as forensic analysis. To secure the network is also crucial to protect the communication between devices. A cyber attack on the log devices could jeopardize any forensic analysis be it for maintenance, or discovering an attack trail. In this paper is proposed a strategy to secure plant operational data recorded in the Historian and data exchange in the network. An integrity checking mechanism, in combination with blockchain, is used to ensure data integrity. Data redundancy is achieved by applying an efficient replication mechanism and enables data recovery after an attack.

Cyber Physical Systems are characterized by a strong interaction among networking, sensing, and control functionalities. Moreover, the recent advent of Internet of Things extended their information sharing capability. However, the interaction between Internet and Cyber Physical Systems requires increased efforts for guaranteeing the security of connected systems. In the industrial field, the problem becomes more complex due to the need of protecting a large attack surface while guaranteeing system availability and real-time response to the detection of threats. In this contribution, we deal with the injection of tampered data into the communication channel with the aim of modifying the status of the physical system. To cope with this attack, we design a secure control system able to detect the injection of tampered data by coding the output of the measurement systems. The proposed approach is based on the use of permutation matrices, whose scheme varies upon a secret pattern obtained exploiting the Fibonacci $p$ -sequences. The detection strategy is compliant with the time delay constraints typical of a Cyber Physical System. An analysis of the security performances of the proposed system is presented along with the experimental proof of its effectiveness.

In this chapter, we present a system for indoor and outdoor localization and navigation to allow the low visionusers in experiencing cultural heritagein autonomy. The system is based on the joint utilizationof dead-reckoning and computer visiontechniques on a smartphone-centric tracking system. The system is explicitly designed for visually impaired people, but it can be easily generalized to other users, and it is built under the assumption that special reference signals, such as colored tapes, painted lines, or tactile paving, are deployed in the environment for guiding visually impaired users along pre-defined paths. Differently from previous works on localization, which are focused only on the utilization of inertial sensors integrated into the smartphones, we exploit the smartphone cameraas an additional sensor that, on one side, can help the visually impaired user to identify the paths and, on the other side, can provide direction estimates to the tracking system. The users with the help of the navigation systemmay experience the museum or the cultural sitein autonomy, by following a path previously decided and by going from a location of interest to another, without any external personal assistant. We demonstrate the effectiveness of our approach, by means of experimental tests performed in a controlled indoor environmentand in a real outdoor installation.

Over the last year, the Internet of Things (IoT) drove the development of cyber-physical systems, leading the convergence between information and operational technologies. This coupling improves performances and saves costs but increases the number of vulnerabilities and the attack surface to malicious actors. Consequently, it is mandatory to understand how IoT devices can be properly integrated into a more secure environment. This issue is even more crucial in the field of Critical Infrastructures, such as energy, water, transportation, and telecommunications. To this aim, this paper analyzes the concept of opacity for Discrete Event Systems (DES) and applies it to a real system. The opacity describes the ability of the system to keep some states secret even if an attacker knows the happening of some events. The proposed approach is validated by applying the concept of opacity on the analysis over a simple system. It is demonstrated that opacity can be guaranteed and exploited when systems having different levels of security are integrated.

Industrial Control systems traditionally achieved security by using proprietary protocols to communicate in an isolated environment from the outside. This paradigm is changed with the advent of the Industrial Internet of Things that foresees flexible and interconnected systems. In this contribution, a device acting as a connection between the operational technology network and information technology network is proposed. The device is an intrusion detection system related to legacy systems that is able to collect and reporting data to and from industrial IoT devices. It is based on the common signature based intrusion detection system developed in the information technology domain, however, to cope with the constraints of the operation technology domain, it exploits anomaly based features. Specifically, it is able to analyze the traffic on the network at application layer by mean of deep packet inspection, parsing the information carried by the proprietary protocols. At a later stage, it collect and aggregate data from and to IoT domain. A simple set up is considered to prove the effectiveness of the approach.

The interaction among networking, sensing, and control in the modern industry results in a variety of new devices used in many sectors such as health, energy distribution, and transportation. The on-going tendency of exploiting automation and data exchange in manufacturing technologies leads to the Industry 4.0. The fourth industrial revolution deals with Cyber-Physical Systems, the Internet of Things, cloud computing, and cognitive computing converging towards the Industrial Internet of Things. To be successful, this new era requires innovative paradigms to ensure the security of provided services and connected systems. In the industrial field, the problem gets more complex due to the need of protecting a large attack surface while guaranteeing the availability of the systems and the real-time response to the presence of threats. In this chapter, we perform an analysis of the existing industrial threats and we present a distributed intrusion detection methodology to deal with attacks affecting the Industrial Internet of Things scenarios.

Industry 4.0 advent opens several cyber-threats scenarios originally designedfor classic information technology, drawing the attention to serious risks for themodern industrial control networks. To cope with this problem, in this paper weaddress the security issues related to covert channels applied to industrial networks,identifying the new vulnerability points when information technologies convergewith operational technologies such as edge computing infrastructures. Specifically,we define two signaling strategies where we exploit the Modbus/TCP protocol astarget to set up a covert channel. Once the threat channel is established, passiveand active offensive methodologies are further exploited by implementing and testingthem on a real Industrial Internet of Things testbed. The experimental resultshighlight the potential damage of such specific threats and the easy extrapolationof the attacks to other types of channels in order to show the new risks for the Industry4.0. Related to this, we discuss some countermeasures offering an overviewof possible mitigation and defensive measures.

Complex and heterogeneous systems characterize the Industry 4.0. Due to the Information Technology (IT) convergence towards the Operational Technology (OT), the development of innovative cyber-physical security tools represents a milestone for the Industrial Control Systems (ICSs) protection. In this context, honeypots are systems used as decoys to detect and analyze malicious actions. However, industrial networks require specific honeypot development capabilities. In this work, we present MimePot, a cyber-physical honeypot conceived for industrial control networks. Compared to classic honeypots, MimePot offers a model-based approach: it is able to simulate physical processes to lure skilled attackers targeting industrial plants. Moreover, MimePot uses the Software Defined Networking (SDN) technology to provide a consistent future proof security approach. We demonstrate the usefulness of MimePot performing data integrity attacks against a water distribution system in a simulated environment.

In this paper, we present an innovative cyber physical system for indoor and outdoor localization and navigation, based on the joint utilization of dead-reckoning and computer vision techniques on a smartphone-centric tracking system. The system is explicitly designed for visually impaired people, but it can be easily generalized to other users, and it is built under the assumption that special reference signals, such as colored tapes, painted lines, or tactile paving, are deployed in the environment for guiding visually impaired users along pre-defined paths. Differently from previous works on localization, which are focused only on the utilization of inertial sensors integrated into the smartphones, we exploit the smartphone camera as an additional sensor that, on one side, can help the visually impaired user to identify the paths and, on the other side, can provide direction estimates to the tracking system. We demonstrate the effectiveness of our approach, by means of experimental tests performed in a real outdoor installation and in a controlled indoor environment.

The reliability of the localization of Wireless Sensor Networks in presence of errors or malicious data alteration is a challenging research topic: recently, several studies have been carried out to identify, remove or neglect the faulted/malicious nodes. This paper addresses the capability of a network, composed of range-capable nodes and anchor nodes (i.e., nodes that know their position), to detect a faulty or malicious alteration of the information provided by the anchor nodes. Specifically, we consider biases for the position of anchor nodes that alter the localization of the network, and we provide conditions under which the nodes are able to detect the event, with particular reference to two distance-based localization algorithms, namely trilateration and Shadow Edge Localization Algorithm.

We present an innovative smartphone-centric tracking system for indoor and outdoor environments, based on the joint utilization of dead-reckoning and computer vision (CV) techniques. The system is explicitly designed for visually impaired people (although it could be easily generalized to other users) and it is built under the assumption that special reference signals, such as painted lines, colored tapes or tactile pavings are deployed in the environment for guiding visually impaired users along pre-defined paths. Thanks to highly optimized software, we are able to execute the CV and sensor-fusion algorithms in run-time on low power hardware such as a normal smartphone, precisely tracking the users movements.

In this paper it is presented a testing prototype of a reconfiguration system for photovoltaic (PV) plants. The system enables to increase the total energy output by reducing the electrical mismatch between the PV array modules. The architecture of the implemented switching matrix, performing the dynamic electrical interconnections of the PV panels, enables to reconfigure nine solar modules in a series-parallel (SP) configuration. The contribution is organized as follows. A brief state of the art is first presented, followed by a comparison between the SP and Total-Cross-Tied (TCT) connections. The prototype then is thoroughly described as well as the main design choices. Finally some tests and an extrapolation of its performance over a real size PV field are reported.