Biblio

Nowadays, Internet Of Everything (IOE) has demanded for a wide range of applications areas. IOE is started to replaces an Internet Of things (IOT). IOE is a combination of massive number of computing elements and sensors, people, processes and data through the Internet infrastructure. Device to Device communication and interfacing of Wireless Sensor network with IOE can makes any system as a Smart System. With the increased the use of Internet and Internet connected devices has opportunities for hackers to launch attacks on unprecedented scale and impact. The IOE can serve the varied security in the various sectors like manufacturing, agriculture, smart grid, payments, IoT gateways, healthcare and industrial ecosystems. To secure connections among people, process, data, and things, is a major challenge in Internet of Everything.. This paper focuses on various security Issues and Authentication Schemes in the IOE systems.

Cyber-Physical Systems (CPS) use computational resources to control physical processes and provide critical services. For this reason, an attack in these systems may have dangerous consequences in the physical world. Hence, cyber- resilience is a fundamental property to ensure the safety of the people, the environment and the controlled physical processes. In this paper, we present metrics to quantify the cyber-resilience level based on the design, structure, stability, and performance under the attack of a given CPS. The metrics provide reference points to evaluate whether the system is better prepared or not to face the adversaries. This way, it is possible to quantify the ability to recover from an adversary using its mathematical model based on actuators saturation. Finally, we validate our approach using a numeric simulation on the Tennessee Eastman control challenge problem.

An open source and low-cost Supervisory Control and Data Acquisition System based on Node-RED and Arduino microcontrollers is presented in this paper. The system is designed for monitoring, supervision, and remotely controlling motors and sensors deployed for oil and gas facilities. The Internet of Things (IoT) based SCADA system consists of a host computer on which a server is deployed using the Node-RED programming tool and two terminal units connected to it: Arduino Uno and Arduino Mega. The Arduino Uno collects and communicates the data acquired from the temperature, flowrate, and water level sensors to the Node-Red on the computer through the serial port. It also uses a local liquid crystal display (LCD) to display the temperature. Node-RED on the computer retrieves the data from the voltage, current, rotary, accelerometer, and distance sensors through the Arduino Mega. Also, a web-based graphical user interface (GUI) is created using Node-RED and hosted on the local server for parsing the collected data. Finally, an HTTP basic access authentication is implemented using Nginx to control the clients' access from the Internet to the local server and to enhance its security and reliability.

Blockchain technology is a decentralized ledger of all transactions across peer to peer network. Being decentralized in nature, a blockchain is highly secure as no single user can alter or remove an entry in the blockchain. The security of office premises and data is a very major concern for any organization. This paper majorly focuses on its application of blockchain technology in security surveillance. This paper proposes a blockchain based multi level network model for security surveillance system. The proposed system architecture is composed of different blockchain based systems connected to a multi level decentralized blockchain system to insure authentication, secure storage, Integrity and accountability.

Given a network with a set of vulnerable actuators (and sensors), the security index of an actuator equals the minimum number of sensors and actuators that needs to be compromised so as to conduct a perfectly undetectable attack using the said actuator. This paper deals with the problem of computing actuator security indices for discrete-time LTI network systems, using a structured systems framework. We show that the actuator security index is generic, that is for almost all realizations the actuator security index remains the same. We refer to such an index as generic security index (generic index) of an actuator. Given that the security index quantifies the vulnerability of a network, the generic index is quite valuable for large scale energy systems. Our second contribution is to provide graph-theoretic conditions for computing the generic index. The said conditions are in terms of existence of linkings on appropriately-defined directed (sub)graphs. Based on these conditions, we present an algorithm for computing the generic index.

This paper proposes an infrared sensor human target locating system for the Internet of Things. In this design, the wireless sensor network is designed and developed to detect human targets by using 6LoWPAN protocol and pyroelectric infrared (PIR) sensors. Based on the detection data acquired by multiple sensor nodes, K-means++ clustering algorithm combined with cost function is applied to complete human target location in a 10m×10m detection area. The experimental results indicate the human locating system works well and the user can view the location information on the terminal devices.

Vehicular Ad Hoc Network (VANET) is a promising technology for autonomous driving as it provides many benefits and user conveniences to improve road safety and driving comfort. Sybil attack is one of the most serious threats in vehicular communications because attackers can generate multiple forged identities to disseminate false messages to disrupt safety-related services or misuse the systems. To address this issue, we propose a Sybil attack detection scheme using ADAS (Advanced Driving Assistant System) sensors installed on modern passenger vehicles, without the assistance of trusted third party authorities or infrastructure. Also, a deep learning based object detection technique is used to accurately identify nearby objects for Sybil attack detection and the multi-step verification process minimizes the false positive of the detection.

Cyber Physical System (CPS) is one of the emerging technologies of the day due to its large number of applications. Its applications extends to automotive, commercial, medical, home appliances and manufacturing industries. Mass research is being conducted in this area including design models, signal processing, control system models, communication models and security. One of the most important aspects of these is security and privacy of CPS. There are a number of vulnerabilities and threats that can be used by an attacker to exploit a cyber physical system. This paper provides a brief review of current security threats, vulnerabilities and its solutions for CPS. For the sake of simplicity the security threats have been divided into two classes i.e. control security and information security. Based on this division various attack methods and their possible solutions have been discussed.

The ongoing digitalization of the power distribution grid will improve the operational support and automation which is believed to increase the system reliability. However, in an integrated and interdependent cyber-physical system, new threats appear which must be understood and dealt with. Of particular concern, in this paper, is the causes of an inconsistent view between the physical system (here power grid) and the Information and Communication Technology (ICT) system (here Distribution Management System). In this paper we align the taxonomy used in International Electrotechnical Commission (power eng.) and International Federation for Information Processing (ICT community), define a metric for inconsistencies, and present a modelling approach using Stochastic Activity Networks to assess the consequences of inconsistencies. The feasibility of the approach is demonstrated in a simple use case.

Due to the wide implementation of communication networks, industrial control systems are vulnerable to malicious attacks, which could cause potentially devastating results. Adversaries launch integrity attacks by injecting false data into systems to create fake events or cover up the plan of damaging the systems. In addition, the complexity and nonlinearity of control systems make it more difficult to detect attacks and defense it. Therefore, a novel security situation awareness framework based on particle filtering, which has good ability in estimating state for nonlinear systems, is proposed to provide an accuracy understanding of system situation. First, a system state estimation based on particle filtering is presented to estimate nodes state. Then, a voting scheme is introduced into hazard situation detection to identify the malicious nodes and a local estimator is constructed to estimate the actual system state by removing the identified malicious nodes. Finally, based on the estimated actual state, the actual measurements of the compromised nodes are predicted by using the situation prediction algorithm. At the end of this paper, a simulation of a continuous stirred tank is conducted to verify the efficiency of the proposed framework and algorithms.

A Cyber Physical Sensor System (CPSS) consists of a computing platform equipped with wireless access points, sensors, and actuators. In a Cyber Physical System, CPSS constantly collects data from a physical object that is under process and performs local real-time control activities based on the process algorithm. The collected data is then transmitted through the network layer to the enterprise command and control center or to the cloud computing services for further processing and analysis. This paper investigates the CPSS' most common cyber security threats and vulnerabilities and provides countermeasures. Furthermore, the paper addresses how the CPSS are attacked, what are the leading consequences of the attacks, and the possible remedies to prevent them. Detailed case studies are presented to help the readers understand the CPSS threats, vulnerabilities, and possible solutions.

In this work, the unknown cyber-attacks on cyber-physical systems are reconstructed using sliding mode differentiation techniques in concert with the sparse recovery algorithm, when only several unknown attacks out of a long list of possible attacks are considered non-zero. The approach is applied to a model of the electric power system, and finally, the efficacy of the proposed techniques is illustrated via simulations of a real electric power system.

In military operations, Commander's Intent describes the desired end state and purpose of the operation, expressed in a concise and clear manner. Command by intent is a paradigm that empowers subordinate units to exercise measured initiative to meet mission goals and accept prudent risk within commander's intent. It improves agility of military operations by allowing exploitation of local opportunities without an explicit directive from the commander to do so. This paper discusses what the paradigm entails in terms of architectural decisions for data fusion systems tasked with real-time information collection to satisfy operational mission goals. In our system, information needs of decisions are expressed at a high level, and shared among relevant nodes. The selected nodes, then, jointly operate to meet mission information needs by forwarding and caching relevant data without explicit directives regarding the objects to fetch and sources to contact. A preliminary evaluation of the system is presented using a target tracking application, set in the context of a NATO-based mission scenario, called Anglova. Evaluation results show that delegating some decision authority to the data fusion system (in terms of objects to fetch and sources to contact) allows it to save more network resources, while also increasing mission success rate. The system is therefore particularly well-suited to operation in partially denied or contested environments, where resource bottlenecks caused by adversarial activity impair one's ability to collect real-time information for mission-critical decision making.

Wireless wearable embedded devices dominate the Internet of Things (IoT) due to their ability to provide useful information about the body and its local environment. The constrained resources of low power processors, however, pose a significant challenge to run-time error logging and hence, product reliability. Error logs classify error type and often system state following the occurrence of an error. Traditional error logging algorithms attempt to balance storage and accuracy by selectively overwriting past log entries. Since a specific combination of firmware faults may result in system instability, preserving all error occurrences becomes increasingly beneficial as IOT systems become more complex. In this paper, a novel hash-based error logging algorithm is presented which has both constant insertion time and constant memory while also exhibiting no false negatives and an acceptable false positive error rate. Both theoretical analysis and simulations are used to compare the performance of the hash-based and traditional approaches.

In this proposed method, the traditional elevators are upgraded in such a way that any alarming situation in the elevator can be detected and then sent to a main center where further action can be taken accordingly. Different emergency situation can be handled by implementing the system. Smart elevator system works by installing different modules inside the elevator such as speed sensors which will detect speed variations occurring above or below a certain threshold of elevator speed. The smart elevator system installed within the elevator sends a message to the emergency response center and sends an automated call as well. The smart system also includes an emotion detection algorithm which will detect emotions of the individual based on their expression in the elevator. The smart system also has a whisper detection system as well to know if someone stuck inside the elevator is alive during any hazardous situation. A broadcast signal is used as a check in the elevator system to evaluate if every part of the system is in stable state. Proposed system can completely replace the current elevator systems and become part of smart homes.

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.

This article is about study of honeypots. In this work, we use some honeypot sensors deployment and analysis to identify, currently, what are the main attacks and security breaches explored by attackers to compromise systems. For example, a common server or service exposed to the Internet can receive a million of hits per day, but sometimes would not be easy to identify the difference between legitimate access and an attacker trying to scan, and then, interrupt the service. Finally, the objective of this research is to investigate the efficiency of the honeypots sensors to identify possible safety gaps and new ways of attacks. This research aims to propose some guidelines to avoid or minimize the damage caused by these attacks in real systems.

Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (textless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- textbartextbar

This paper presents the Scanning, Vulnerabilities, Exploits and Detection tool (SVED). SVED facilitates reliable and repeatable cyber security experiments by providing a means to design, execute and log malicious actions, such as software exploits, as well the alerts provided by intrusion detection systems. Due to its distributed architecture, it is able to support large experiments with thousands of attackers, sensors and targets. SVED is automatically updated with threat intelligence information from various services.

The vulnerabilities in today's supply chain have raised serious concerns about the security and trustworthiness of electronic components and systems. Testing for device provenance, detection of counterfeit integrated circuits/systems, and traceability are challenging issues to address. In this paper, we develop a novel RFID-based system suitable for electronic component and system Counterfeit detection and System Traceability called CST. CST is composed of different types of on-chip sensors and in-system structures that provide the information needed to detect multiple counterfeit IC types (recycled, cloned, etc.), verify the authenticity of the system with some degree of confidence, and track/identify boards. Central to CST is an RFID tag employed as storage and a channel to read the information from different types of chips on the printed circuit board (PCB) in both power-off and power-on scenarios. Simulations and experimental results using Spartan 3E FPGAs demonstrate the effectiveness of this system. The efficiency of the radio frequency (RF) communication has also been verified via a PCB prototype with a printed slot antenna.

The integration of physical systems with distributed embedded computing and communication devices offers advantages on reliability, efficiency, and maintenance. At the same time, these embedded computers are susceptible to cyber-attacks that can harm the performance of the physical system, or even drive the system to an unsafe state; therefore, it is necessary to deploy security mechanisms that are able to automatically detect, isolate, and respond to potential attacks. Detection and isolation mechanisms have been widely studied for different types of attacks; however, automatic response to attacks has attracted considerably less attention. Our goal in this paper is to identify trends and recent results on how to respond and reconfigure a system under attack, and to identify limitations and open problems. We have found two main types of attack protection: i) preventive, which identifies the vulnerabilities in a control system and then increases its resiliency by modifying either control parameters or the redundancy of devices; ii) reactive, which responds as soon as the attack is detected (e.g., modifying the non-compromised controller actions).

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.
 

The vast majority of today's critical infrastructure is supported by numerous feedback control loops and an attack on these control loops can have disastrous consequences. This is a major concern since modern control systems are becoming large and decentralized and thus more vulnerable to attacks. This paper is concerned with the estimation and control of linear systems when some of the sensors or actuators are corrupted by an attacker. We give a new simple characterization of the maximum number of attacks that can be detected and corrected as a function of the pair (A,C) of the system and we show in particular that it is impossible to accurately reconstruct the state of a system if more than half the sensors are attacked. In addition, we show how the design of a secure local control loop can improve the resilience of the system. When the number of attacks is smaller than a threshold, we propose an efficient algorithm inspired from techniques in compressed sensing to estimate the state of the plant despite attacks. We give a theoretical characterization of the performance of this algorithm and we show on numerical simulations that the method is promising and allows to reconstruct the state accurately despite attacks. Finally, we consider the problem of designing output-feedback controllers that stabilize the system despite sensor attacks. We show that a principle of separation between estimation and control holds and that the design of resilient output feedback controllers can be reduced to the design of resilient state estimators.