Biblio

Advancement in communication technologies and the Internet of Things (IoT) is driving adoption in smart cities that aims to increase operational efficiency and improve the quality of services and citizen welfare, among other potential benefits. The privacy, reliability, and integrity of communications must be ensured so that actions can be appropriate, safe, accurate, and implemented promptly after receiving actionable information. In this work, we present a multi-tier methodology consisting of an authentication and trust-building/distribution framework designed to ensure the safety and validity of the information exchanged in the system. Blockchain protocols and Radio Frequency-Distinct Native Attributes (RF-DNA) combine to provide a hardware-software codesigned system for enhanced device identity and overall system trustworthiness. Our threat model accounts for counterfeiting, breakout fraud, and bad mouthing of one entity by others. Entity trust (e.g., IoT devices) depends on quality and level of participation, quality of messages, lifetime of a given entity in the system, and the number of known "bad" (non-consensus) messages sent by that entity. Based on this approach to trust, we are able to adjust trust upward and downward as a function of real-time and past behavior, providing other participants with a trust value upon which to judge information from and interactions with the given entity. This approach thereby reduces the potential for manipulation of an IoT system by a bad or byzantine actor.

The measurement and provision of precise and up-to-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic control systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data, such as velocity of individual vehicles as well as vehicle type information, can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%.

The growing demand for safety in urban environments is supported by monitoring using video surveillance. The need to analyze multiple video-flows from different cameras deployed around the city by heterogeneous owners introduces vulnerabilities and privacy issues. Video frames, timestamps, and camera settings can be digitally manipulated by malicious users; the positions of cameras, their orientation and their mechanical settings can be physically manipulated. Digital and physical manipulations may have several effects, including the change of the observed scene and the potential violation of neighbors' privacy. To face these risks, we introduce BlockSee, a blockchain-based video surveillance system that jointly provides validation and immutability to camera settings and surveillance videos, making them readily available to authorized users in case of events. The encouraging results obtained with BlockSee pave the way to new distributed city-wide monitoring systems.

In the open network environment, the network offensive information is implanted in big data environment, so it is necessary to carry out accurate location marking of network offensive information, to realize network attack detection, and to implement the process of accurate location marking of network offensive information. Combined with big data analysis method, the location of network attack nodes is realized, but when network attacks cross in series, the performance of attack information tagging is not good. An accurate marking technique for network attack information is proposed based on big data fusion tracking recognition. The adaptive learning model combined with big data is used to mark and sample the network attack information, and the feature analysis model of attack information chain is designed by extracting the association rules. This paper classifies the data types of the network attack nodes, and improves the network attack detection ability by the task scheduling method of the network attack information nodes, and realizes the accurate marking of the network attacking information. Simulation results show that the proposed algorithm can effectively improve the accuracy of marking offensive information in open network environment, the efficiency of attack detection and the ability of intrusion prevention is improved, and it has good application value in the field of network security defense.

The article considers some aspects of modeling information security circuits for information and communication systems used in Smart City. As a basic research paradigm, the postulates of game theory and mathematical dependencies based on Markov processes were used. Thus, it is possible to sufficiently substantively describe the procedure for selecting rational variants of cyber security systems used to protect information technologies in Smart City. At the same time, using the model proposed by us, we can calculate the probability of cyber threats for the Smart City systems, as well as the cybernetic risks of diverse threats. Further, on the basis of the described indicators, rational contour options are chosen to protect the information systems used in Smart City.

IoT-based services are widely increasing due to their advantages such as economy, automation, and comfort. Smart cities are among major applications of IoT-based systems. However, security and privacy threats are vital issues challenging the utilization of such services. Connectivity nature, variety of data technology, and volume of data maintained through these systems make their security analysis a difficult process. Threat modeling is one the best practices for security analysis, especially for complex systems. This paper proposes a threat extraction method for IoT-based systems. We elaborate on a smart city scenario with three services including lighting, car parking, and waste management. Investigating on these services, firstly, we identify thirty-two distinct threat types. Secondly, we distinguish threat root causes by associating a threat to constituent parts of the IoT-based system. In this way, threat instances can be extracted using the proposed derivation rules. Finally, we evaluate our method on a smart car parking scenario as well as on an E-Health system and identify more than 50 threat instances in each cases to show that the method can be easily generalized for other IoT-based systems whose constituent parts are known.

In order to study the application of improved image hashing algorithm in image tampering detection, based on compressed sensing and ring segmentation, a new image hashing technique is studied. The image hash algorithm based on compressed sensing and ring segmentation is proposed. First, the algorithm preprocesses the input image. Then, the ring segment is used to extract the set of pixels in each ring region. These aggregate data are separately performed compressed sensing measurements. Finally, the hash value is constructed by calculating the inner product of the measurement vector and the random vector. The results show that the algorithm has good perceived robustness, uniqueness and security. Finally, the ROC curve is used to analyze the classification performance. The comparison of ROC curves shows that the performance of the proposed algorithm is better than FM-CS, GF-LVQ and RT-DCT.

With advances in information and communication technologies, cities are getting smarter to enhance the quality of human life. In smart cities, safety (including security) is an essential issue. In this paper, by reviewing several safe city projects, smart city facilities for the safety are presented. With considering the facilities, a design for a crime intelligence system is introduced. Then, concentrating on how to support police activities (i.e., emergency call reporting reception, patrol activity, investigation activity, and arrest activity) with immersive technologies in order to reduce a crime rate and to quickly respond to emergencies in the safe city, smart policing with augmented reality (AR) and virtual reality (VR) is explained.

We are living in a society where technology is present everywhere we go. We are striving towards smart homes, smart cities, Internet of Things, Internet of Everything. Not so long ago, a password was all you needed for secure authentication. Nowadays, even the most complicated passwords are not considered enough. Multi-factor authentication is gaining more and more terrain. Complex system may also require more than one solution for real, strong security. The present paper proposes a framework based with MFA as a basis for access control and data analytics. Events within a cyber-physical system are processed and analyzed in an attempt to detect, prevent and mitigate possible attacks.

This paper presents the Helix SandBox, an open platform for quick prototyping of smart environment applications. Its architecture was designed to be a lightweight solution that aimed to simplify the instance integration and setup of the main Generic Enablers provided in the FIWARE architecture. As a Powered by FIWARE platform, the SandBox operates with the NGSI standard for interoperability between systems. The platform offers a container-based multicloud architecture capable of running in public, private and bare metal clouds or even in the leading hypervisors available. This paper also proposes a multi-layered architecture capable of integrates the cloud, fog, edge and IoT layers through the federation concept. Lastly, we present two Smart Cities applications conducted in the form of Proof of Concept (PoC) that use the Helix SandBox platform as back-end.

In the network security risk assessment on critical information infrastructure of smart city, to describe attack vectors for predicting possible initial access is a challenging task. In this paper, an attack vector evaluation model based on weakness, path and action is proposed, and the formal representation and quantitative evaluation method are given. This method can support the assessment of attack vectors based on known and unknown weakness through combination of depend conditions. In addition, defense factors are also introduced, an attack vector evaluation model of integrated defense is proposed, and an application example of the model is given. The research work in this paper can provide a reference for the vulnerability assessment of attack vector.

Due to the rise of huge population in mankind and the large variety of upcoming utilization of power, the energy requirement has substantially increased. Smart Grid is a very important part of the Smart Cities initiative and is one of the crucial components in distribution and reconciliation of energy. Security of the smart grid infrastructure, which is an integral part of the smart grid framework, intended at transitioning the conventional power grid system into a robust, reliable, adaptable and intelligent energy utility, is an impending problem that needs to be arrested quickly. With the increasingly intensifying integration of smart devices in the smart grid infrastructure with other interconnected applications and the communication backbone is compelling both the energy users and the energy utilities to thoroughly look into the privacy and security issues of the smart grid. In this paper, we present challenges of the existing security mechanisms deployed in the smart grid framework and we tried to bring forward the unresolved problems that would highlight the security aspects of Smart Grid as a challenging area of research and development in the future.

Internet of things is a network formed between two or more devices through internet which helps in sharing data and resources. IoT is present everywhere and lot of applications in our day-to-day life such as smart homes, smart grid system which helps in reducing energy consumption, smart garbage collection to make cities clean, smart cities etc. It has some limitations too such as concerns of security of the network and the cost of installations of the devices. There have been many researches proposed various method in improving the IoT systems. In this paper, we have discussed about the scope and limitations of IoT in various fields and we have also proposed a technique to secure offline architecture of IoT.

The tremendous advances in information and communications technology (ICT), as well as the embedded systems, have been led to the emergence of the novel concept of the internet of things (IoT). Enjoying IoT-based technologies, many objects and components can be connected to each other through the internet or other modern communicational platforms. Embedded systems which are computing machines for special purposes like those utilized in high-tech devices, smart buildings, aircraft, and vehicles including advanced controllers, sensors, and meters with the ability of information exchange using IT infrastructures. The phrase "internet", in this context, does not exclusively refer to the World Wide Web rather than any type of server-based or peer-to-peer networks. In this study, the application of IoT in smart grids is addressed. Hence, at first, an introduction to the necessity of deployment of IoT in smart grids is presented. Afterwards, the applications of IoT in three levels of generation, transmission, and distribution is proposed. The generation level is composed of applications of IoT in renewable energy resources, wind and solar in particular, thermal generation, and energy storage facilities. The deployment of IoT in transmission level deals with congestion management in power system and guarantees the security of the system. In the distribution level, the implications of IoT in active distribution networks, smart cities, microgrids, smart buildings, and industrial sector are evaluated.

Motivated by ubiquitous surveillance cameras in a smart city, a monitoring service can be provided to citizens. However, the rise of privacy concerns may disrupt this advanced service. Yet, the existing cloud-based services have not clearly proven that they can preserve Wth-privacy in which the relationship of three types of information, i.e., who requests the service, what the target is and where the camera is, does not leak. We address this problem by proposing a content-centric privacy platform (C2P2) that enables the construction of a Wth-privacy-preserving monitoring service without cloud dependency. C2P2 uses an image classification model of a target serving as the key to access the monitoring service specific to the target. In C2P2, communication is based on information-centric networking (ICN) that enables privacy preservation to be centered on the content itself rather than relying on a centralized system. Moreover, to preserve the privacy of bystanders, C2P2 separates the sensitive information (e.g., human faces) from the non-sensitive information (e.g., image background), while the privacy-aware forwarding strategies in C2P2 enable data aggregation and prevent privacy leakage resulting from false positive of image recognition. We evaluate the privacy leakage of C2P2 compared to that of the cloud-based system. The privacy analysis shows that, compared to the cloud-based system, C2P2 achieves a lower privacy loss ratio while reducing the communication cost significantly.

Integrating multiple programmable components and subsystems developed by different manufacturers into a final system (a system of systems) can create some security concerns. While there are many efforts for developing interoperability approaches to enable smooth, reliable and safe integration among different types of components to build final systems for different applications, less attention is usually given for the security aspects of this integration. This may leave the final systems exposed and vulnerable to potential security attacks. The issues elevate further when such systems are also connected to other networks such as the Internet or systems like fog and cloud computing. This issue can be found in important industrial applications like smart medical, smart manufacturing and smart city systems. As a result, along with performance, safety and reliability; multisystem integration must also be highly secure. This paper discusses the security issues instigated by such integration. In addition, it proposes a middleware framework to address the security issues for integrated multisystem applications.

The main objective of this research work is to enhance the data storage capacity of the QR codes. By achieving the research aim, we can visualize rapid increase in application domains of QR Codes, mostly for smart cities where one needs to store bulk amount of data. Nowadays India is experiencing demonetization step taken by Prime Minister of the country and QR codes can play major role for this step. They are also helpful for cashless society as many vendors have registered themselves with different e-wallet companies like paytm, freecharge etc. These e-wallet companies have installed QR codes at cash counter of such vendors. Any time when a customer wants to pay his bills, he only needs to scan that particular QR code. Afterwards the QR code decoder application start working by taking necessary action like opening payment gateway etc. So, objective of this research study focuses on solving this issue by applying proposed methodology.

In the process of mobile intelligent terminal for file transfer, ensure the safety of data transmission is significant. It is necessary to prevent the file from being eavesdropped and tampered during transmission. The method of using double encryption on covert channel is proposed in this paper based on the analysis of encryption algorithms and covert channel, which uses asymmetric encryption algorithm to encrypt the key of symmetric encryption, to form hidden information, and to carry out covert transmission through covert channels to enhance the security of mobile terminal data transmission. By simulating the above scenarios in intelligent mobile terminal, the confidentiality and concealment of important information are realized in the transmission process.

Vehicular Ad-hoc NETwork (VANET) is a vital transportation technology that facilitates the vehicles to share sensitive information (such as steep-curve warnings and black ice on the road) with each other and with the surrounding infrastructure in real-time to avoid accidents and enable comfortable driving experience.To achieve these goals, VANET requires a secure environment for authentic, reliable and trusted information dissemination among the network entities. However, VANET is prone to different attacks resulting in the dissemination of compromised/false information among network nodes. One way to manage a secure and trusted network is to introduce trust among the vehicular nodes. To this end, various Trust Models (TMs) are developed for VANET and can be broadly categorized into three classes, Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Hybrid Trust Models (HTM). These TMs evaluate trust based on the received information (data), the vehicle (entity) or both through different mechanisms. In this paper, we present a comparative study of the three TMs. Furthermore, we evaluate these TMs against the different trust, security and quality-of-service related benchmarks. Simulation results revealed that all these TMs have deficiencies in terms of end-to-end delays, event detection probabilities and false positive rates. This study can be used as a guideline for researchers to design new efficient and effective TMs for VANET.

There is increased interest in smart vehicles acting as both data consumers and producers in smart cities. Vehicles can use smart city data for decision-making, such as dynamic routing based on traffic conditions. Moreover, the multitude of embedded sensors in vehicles can collectively produce a rich data set of the urban landscape that can be used to provide a range of services. Key to the success of this vision is a scalable and private architecture for trusted data sharing. This paper proposes a framework called SpeedyChain, that leverages blockchain technology to allow smart vehicles to share their data while maintaining privacy, integrity, resilience, and non-repudiation in a decentralized and tamper-resistant manner. Differently from traditional blockchain usage (e.g., Bitcoin and Ethereum), the proposed framework uses a blockchain design that decouples the data stored in the transactions from the block header, thus allowing fast addition of data to the blocks. Furthermore, an expiration time for each block is proposed to avoid large sized blocks. This paper also presents an evaluation of the proposed framework in a network emulator to demonstrate its benefits.

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.

Nowadays, Vehicular ad hoc network confronts many challenges in terms of security and privacy, due to the fact that data transmitted are diffused in an open access environment. However, highest of drivers want to maintain their information discreet and protected, and they do not want to share their confidential information. So, the private information of drivers who are distributed in this network must be protected against various threats that may damage their privacy. That is why, confidentiality, integrity and availability are the important security requirements in VANET. This paper focus on security threat in vehicle network especially on the availability of this network. Then we regard the rational attacker who decides to lead an attack based on its adversary's strategy to maximize its own attack interests. Our aim is to provide reliability and privacy of VANET system, by preventing attackers from violating and endangering the network. to ensure this objective, we adopt a tree structure called attack tree to model the attacker's potential attack strategies. Also, we join the countermeasures to the attack tree in order to build attack-defense tree for defending these attacks.

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas, such as smart homes, smart cities, health care, and transportation. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to battlefield specific challenges, such as the absence of communication infrastructure, heterogeneity of devices, and susceptibility to cyber-physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and information dissemination in the presence of adversaries. This paper aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to quantify the information dissemination among heterogeneous network devices. Consequently, a tractable optimization problem is formulated that can assist commanders in cost effectively planning the network and reconfiguring it according to the changing mission requirements.

Parameter estimation in wireless sensor networks (WSN) using encrypted non-binary quantized data is studied. In a WSN, sensors transmit their observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. Encryption approaches for WSNs with fixed threshold binary quantization were previously explored. However, fixed threshold binary quantization limits parameter estimation to scalar parameters. In this paper, we propose a stochastic encryption approach for WSNs that can operate on non-binary quantized observations and has the capability for vector parameter estimation. We extend a binary stochastic encryption approach proposed previously, to a non-binary generalized case. Sensor outputs are quantized using a quantizer with R + 1 levels, where R $ε$ 1, 2, 3,..., encrypted by flipping them with certain flipping probabilities, and then transmitted. Optimal estimators using maximum-likelihood estimation are derived for both a legitimate fusion center (LFC) and a third party fusion center (TPFC) perspectives. We assume the TPFC is unaware of the encryption. Asymptotic analysis of the estimators is performed by deriving the Cramer-Rao lower bound for LFC estimation, and the asymptotic bias and variance for TPFC estimation. Numerical results validating the asymptotic analysis are presented.

Traditional security measures for large-scale critical infrastructure systems have focused on keeping adversaries out of the system. As the Internet of Things (IoT) extends into millions of homes, with tens or hundreds of devices each, the threat landscape is complicated. IoT devices have unknown access capabilities with unknown reach into other systems. This paper presents ongoing work on how techniques in sensor verification and cyber-physical modeling and analysis on bulk power systems can be applied to identify malevolent IoT devices and secure smart and connected communities against the most impactful threats.