With the proliferation of data in Internet-related applications, incidences of cyber security have increased manyfold. Energy management, which is one of the smart city layers, has also been experiencing cyberattacks. Furthermore, the Distributed Energy Resources (DER), which depend on different controllers to provide energy to the main physical smart grid of a smart city, is prone to cyberattacks. The increased cyber-attacks on DER systems are mainly because of its dependency on digital communication and controls as there is an increase in the number of devices owned and controlled by consumers and third parties. This paper analyzes the major cyber security and privacy challenges that might inflict, damage or compromise the DER and related controllers in smart cities. These challenges highlight that the security and privacy on the Internet of Things (IoT), big data, artificial intelligence, and smart grid, which are the building blocks of a smart city, must be addressed in the DER sector. It is observed that the security and privacy challenges in smart cities can be solved through the distributed framework, by identifying and classifying stakeholders, using appropriate model, and by incorporating fault-tolerance techniques.

Smart cities deploy large numbers of sensors and collect a tremendous amount of data from them. For example, Advanced Metering Infrastructures (AMIs), which consist of physical meters that collect usage data about public utilities such as power and water, are an important building block in a smart city. In a typical sensor network, the measurement devices are connected through a computer network, which exposes them to cyber attacks. Furthermore, the data is centrally managed at the operator’s servers, making it vulnerable to insider threats.Our goal is to protect the integrity of data collected by large-scale sensor networks and the firmware in measurement devices from cyber attacks and insider threats. To this end, we first develop a comprehensive threat model for attacks against data and firmware integrity, which can target any of the stakeholders in the operation of the sensor network. Next, we use our threat model to analyze existing defense mechanisms, including signature checks, remote firmware attestation, anomaly detection, and blockchain-based secure logs. However, the large size of the Trusted Computing Base and a lack of scalability limit the applicability of these existing mechanisms. We propose the Feather-Light Blockchain Infrastructure (FLBI) framework to address these limitations. Our framework leverages a two-layer architecture and cryptographic threshold signature chains to support large networks of low-capacity devices such as meters and data aggregators. We have fully implemented the FLBI’s end-to-end functionality on the Hyperledger Fabric and private Ethereum blockchain platforms. Our experiments show that the FLBI is able to support millions of end devices.

In order to solve the problem of untargeted data security grading methods in the process of power grid data governance, this paper analyzes the mainstream data security grading standards at home and abroad, investigates and sorts out the characteristics of power grid data security grading requirements, and proposes a method that considers national, social, and A grid data security classification scheme for the security impact of four dimensions of individuals and enterprises. The plan determines the principle of power grid data security classification. Based on the basic idea of “who will be affected to what extent and to what extent when the power grid data security is damaged”, it defines three classification factors that need to be considered: the degree of impact, the scope of influence, and the objects of influence, and the power grid data is divided into five security levels. In the operation stage of power grid data security grading, this paper sorts out the experience and gives the recommended grading process. This scheme basically conforms to the status quo of power grid data classification, and lays the foundation for power grid data governance.

The increasing demand for the interconnected IoT based smart grid is facing threats from cyber-attacks due to inherent vulnerability in the smart grid network. There is a pressing need to evaluate and model these vulnerabilities in the network to avoid cascading failures in power systems. In this paper, we propose and evaluate a vulnerability assessment framework based on attack probability for the protection and security of a smart grid. Several factors were taken into consideration such as the probability of attack, propagation of attack from a parent node to child nodes, effectiveness of basic metering system, Kalman estimation and Advanced Metering Infrastructure (AMI). The IEEE-300 bus smart grid was simulated using MATPOWER to study the effectiveness of the proposed framework by injecting false data injection attacks (FDIA); and studying their propagation. Our results show that the use of severity assessment standards such as Common Vulnerability Scoring System (CVSS), AMI measurements and Kalman estimates were very effective for evaluating the vulnerability assessment of smart grid in the presence of FDIA attack scenarios.

In this paper we consider cyber security requirements of the smart buildings. We identify cyber risks, threats, attack scenarios, security objectives and related security controls. The work was done as a part of a smart building design and construction work. From the controls identified w e concluded security practices for engineering-in smart buildings security. The paper provides an idea toward which system security engineers can strive in the basic design and implementation of the most critical components of the smart buildings. The intent of the concept is to help practitioners to avoid ad hoc approaches in the development of security mechanisms for smart buildings with shared space.

The proliferation of autonomous and connected vehicles on our roads is increasingly felt. However, the problems related to the optimization of the energy consumed, to the safety, and to the security of these do not cease to arise on the tables of debates bringing together the various stakeholders. By focusing on the security aspect of such systems, we can realize that there is a family of problems that must be investigated as soon as possible. In particular, those that may manifest as the system expands. Therefore, this work aims to model and simulate the behavior of a system of autonomous and connected vehicles in the face of a malware invasion. In order to achieve the set objective, we propose a model to our system which is inspired by those used in epidimology, such as SI, SIR, SIER, etc. This being adapted to our case study, stochastic processes are defined in order to characterize its dynamics. After having fixed the values of the various parameters, as well as those of the initial conditions, we run 100 simulations of our system. After which we visualize the results got, we analyze them, and we give some interpretations. We end by outlining the lessons and recommendations drawn from the results.

Smart grid is a new generation of grid that inte-grates traditional grid and grid information system, and infor-mation security of smart grid is an extremely important part of the whole grid. The research of trusted computing technology provides new ideas to protect the information security of the power grid. To address the problem of large deviations in the calculation of credible dynamic thresholds due to the existence of characteristics such as self-similarity and traffic bursts in smart grid information collection, a traffic prediction model based on ARMA and Poisson process is proposed. And the Hurst coefficient is determined more accurately using R/S analysis, which finally improves the efficiency and accuracy of the trusted dynamic threshold calculation.

Aiming at the specificity and complexity of the power IoT terminal, a method of power IoT terminal firmware vulnerability detection based on memory fuzzing is proposed. Use the method of bypassing the execution to simulate and run the firmware program, dynamically monitor and control the execution of the firmware program, realize the memory fuzzing test of the firmware program, design an automatic vulnerability exploitability judgment plug-in for rules and procedures, and provide power on this basis The method and specific process of the firmware vulnerability detection of the IoT terminal. The effectiveness of the method is verified by an example.

For the huge charging demands of numerous electric vehicles (EVs), coordinated charging is increasing in power grid. However, since connected with public networks, the coordinated charging control system is in a low-level cyber security and greatly vulnerable to malicious attacks. This paper investigates the malicious mode attack (MMA), which is a new cyber-attack pattern that simultaneously attacks massive EV charging piles to generate continuous sinusoidal power disturbance with the same frequency as the poorly-damped wide-area electromechanical mode. Thereby, high amplitude forced oscillations are stimulated by MMA, which seriously threats the stability of power systems and the power supply of charging stations. The potential threat of MMA is clarified by investigating the vulnerability of the IoT-based coordinated charging load control system, and an MMA process like Mirai is pointed out as an example. An MMA model is established for impact analysis. A hardware test platform is built for the verification of the MMA model. Test result verified the existence of MMA and the accuracy of the MMA model.

Security is a key concern across the world, and it has been a common thread for all critical sectors. Nowadays, it may be stated that security is a backbone that is absolutely necessary for personal safety. The most important requirements of security systems for individuals are protection against theft and trespassing. CCTV cameras are often employed for security purposes. The biggest disadvantage of CCTV cameras is their high cost and the need for a trustworthy individual to monitor them. As a result, a solution that is both easy and cost-effective, as well as secure has been devised. The smart door lock is built on Raspberry Pi technology, and it works by capturing a picture through the Pi Camera module, detecting a visitor's face, and then allowing them to enter. Local binary pattern approach is used for Face recognition. Remote picture viewing, notification, on mobile device are all possible with an IOT based application. The proposed system may be installed at front doors, lockers, offices, and other locations where security is required. The proposed system has an accuracy of 89%, with an average processing time is 20 seconds for the overall process.

With billions of devices already connected to the network's edge, the Internet of Things (IoT) is shaping the future of pervasive computing. Nonetheless, IoT applications still cannot escape the need for the computing resources available at the fog layer. This becomes challenging since the fog nodes are not necessarily secure nor reliable, which widens even further the IoT threat surface. Moreover, the security risk appetite of heterogeneous IoT applications in different domains or deploy-ment contexts should not be assessed similarly. To respond to this challenge, this paper proposes a new approach to optimize the allocation of secure and reliable fog computing resources among IoT applications with varying security risk level. First, the security and reliability levels of fog nodes are quantitatively evaluated, and a security risk assessment methodology is defined for IoT services. Then, an online, incentive-compatible mechanism is designed to allocate secure fog resources to high-risk IoT offloading requests. Compared to the offline Vickrey auction, the proposed mechanism is computationally efficient and yields an acceptable approximation of the social welfare of IoT devices, allowing to attenuate security risk within the edge network.

With their variety of application verticals, smart cities represent a killer scenario for Cloud-IoT computing, e.g. fog computing. Such applications require a management capable of satisfying all their requirements through suitable service placements, and of balancing among QoS-assurance, operational costs, deployment security and, last but not least, energy consumption and carbon emissions. This keynote discusses these aspects over a motivating use case and points to some open challenges.

Connected devices are being deployed at a steady rate, providing services like data collection. Pervasive applications rely on those edge devices to seamlessly provide services to users. To connect applications and edge devices, using a middleware has been a popular approach. The research is active on the subject as there are many open challenges. The secure management of the edge devices and the security of the middleware are two of them. As security is a crucial requirement for pervasive environment, we propose a middleware architecture easing the secure use of edge devices for pervasive applications, while supporting the heterogeneity of communication protocols and the dynamism of devices. Because of the heterogeneity in protocols and security features, not all edge devices are equally secure. To allow the pervasive applications to gain control over this heterogeneous security, we propose a model to describe edge devices security. This model is accessible by the applications through our middleware. To validate our work, we developed a demonstrator of our middleware and we tested it in a concrete scenario.

The Internet of Things (IoT) aims to introduce pervasive computation into the human environment. The processing on a cloud platform is suggested due to the IoT devices' resource limitations. High latency while transmitting IoT data from its edge network to the cloud is the primary limitation. Modern IoT applications frequently use fog computing, an unique architecture, as a replacement for the cloud since it promises faster reaction times. In this work, a fog layer is introduced in smart vital sign monitor design in order to serve faster. Context aware computing makes use of environmental or situational data around the object to invoke proactive services upon its usable content. Here in this work the fog layer is intended to provide local data storage, data preprocessing, context awareness and timely analysis.

Large-scale onboarding of industrial cyber physical systems requires efficiency and security. In situations with the dynamic addition of devices (e.g., from subcontractors entering a workplace), automation of the onboarding process is desired. The Eclipse Arrowhead framework, which provides a platform for industrial automation, requires reliable, flexible, and secure device onboarding to local clouds. In this paper, we propose a device onboarding method in the Arrowhead framework where decentralized authorization is provided by Power of Attorney. The model allows users to subgrant power to trusted autonomous devices to act on their behalf. We present concepts, an implementation of the proposed system, and a use case for scalable onboarding where Powers of Attorney at two levels are used to allow a subcontractor to onboard its devices to an industrial site. We also present performance evaluation results.

In the last decade, numerous Industrial IoT systems have been deployed. Attack vectors and security solutions for these are an active area of research. However, to the best of our knowledge, only very limited insight in the applicability and real-world comparability of attacks exists. To overcome this widespread problem, we have developed and realized an approach to collect attack traces at a larger scale. An easily deployable system integrates well into existing networks and enables the investigation of attacks on unmodified commercial devices.

Worldwide, societies are rapidly becoming more connected, owing primarily to the growing number of intelligent things and smart applications (e.g, smart automobiles, smart wearable devices, etc.) These have occurred in tandem with the Internet Of Things, a new method of connecting the physical and virtual worlds. It is a new promising paradigm whereby every ‘thing’ can connect to anything via the Internet. However, with IoT systems being deployed even on large-scale, security concerns arise amongst other challenges. Hence the need to allocate appropriate protection of resources. The realization of secure IoT systems could only be accomplished with a comprehensive understanding of the particular needs of a specific system. How-ever, this paradigm lacks a proper and exhaustive classification of security requirements. This paper presents an approach towards understanding and classifying the security requirements of IoT devices. This effort is expected to play a role in designing cost-efficient and purposefully secured future IoT systems. During the coming up with and the classification of the requirements, We present a variety of set-ups and define possible attacks and threats within the scope of IoT. Considering the nature of IoT and security weaknesses as manifestations of unrealized security requirements, We put together possible attacks and threats in categories, assessed the existent IoT security requirements as seen in literature, added more in accordance with the applied domain of the IoT and then classified the security requirements. An IoT system can be secure, scalable, and flexible by following the proposed security requirement classification.

Memory-based vulnerabilities are becoming more and more common in low-power and low-cost devices in IOT. We study several low-level vulnerabilities that lead to memory corruption in C and C++ programs, and how to use stack corruption and format string attack to exploit these vulnerabilities. Automatic methods for resisting memory attacks, such as stack canary and address space layout randomization ASLR, are studied. These methods do not need to change the source program. However, a return-oriented programming (ROP) technology can bypass them. Control flow integrity (CFI) can resist the destruction of ROP technology. In fact, the security design is holistic. Finally, we summarize the rules of security coding in embedded devices, and propose two novel methods of software anomaly detection process for IOT devices in the future.

Microcontroller-based embedded systems have become ubiquitous with the emergence of IoT technology. Given its critical roles in many applications, its security is becoming increasingly important. Unfortunately, MCU devices are especially vulnerable. Code reuse attacks are particularly noteworthy since the memory address of firmware code is static. This work seeks to combat code reuse attacks, including ROP and more advanced JIT-ROP via continuous randomization. Previous proposals are geared towards full-fledged OSs with rich runtime environments, and therefore cannot be applied to MCUs. We propose the first solution for ARM-based MCUs. Our system, named HARM, comprises a secure runtime and a binary analysis tool with rewriting module. The secure runtime, protected inside the secure world, proactively triggers and performs non-bypassable randomization to the firmware running in a sandbox in the normal world. Our system does not rely on any firmware feature, and therefore is generally applicable to both bare-metal and RTOS-powered firmware. We have implemented a prototype on a development board. Our evaluation results indicate that HARM can effectively thaw code reuse attacks while keeping the performance and energy overhead low.

The spread of the Internet of Things (IoT) and the use of smart control systems in many mission-critical or safety-critical applications domains, like automotive or aeronautical, make devices attractive targets for attackers. Nowadays, several of these are mixed-criticality systems, i.e., they run both high-criticality tasks (e.g., a car control system) and low-criticality ones (e.g., infotainment). High-criticality routines often employ Real-Time Operating Systems (RTOS) to enforce hard real-time requirements, while the tasks with lower constraints can be delegated to more generic-purpose operating systems (GPOS).Much of the control code for these devices is written in memory-unsafe languages such as C and C++. This makes them susceptible to powerful binary attacks, such as the famous Return-Oriented Programming (ROP). Control-Flow Integrity (CFI) is the most investigated security technique to protect against such threats. At now, CFI solutions for real-time embedded systems are not as mature as the ones for general-purpose systems, and even more, there is a lack of in-depth studies on how different operating systems with different security requirements and timing constraints can coexist on a single multicore platform.This paper aims at drawing attention to the subject, discussing the current scientific proposal, and in turn proposing a solution for an optimized asymmetric verification system for execution integrity. By using an embedded hypervisor, predefined cores could be dedicated to only high or low-criticality tasks, with the high-priority core being monitored by the lower-criticality core, relying on offline binary instrumentation and a light exchange of information and signals at runtime. The work also presents preliminary results about a possible implementation for multicore ARM platforms, running both RTOS and GPOS, both in terms of security and performance penalties.

Smart Security Solutions are in high demand with the ever-increasing vulnerabilities within the IT domain. Adjusting to a Work-From-Home (WFH) culture has become mandatory by maintaining required core security principles. Therefore, implementing and maintaining a secure Smart Home System has become even more challenging. ARGUS provides an overall network security coverage for both incoming and outgoing traffic, a firewall and an adaptive bandwidth management system and a sophisticated CCTV surveillance capability. ARGUS is such a system that is implemented into an existing router incorporating cloud and Machine Learning (ML) technology to ensure seamless connectivity across multiple devices, including IoT devices at a low migration cost for the customer. The aggregation of the above features makes ARGUS an ideal solution for existing Smart Home System service providers and users where hardware and infrastructure is also allocated. ARGUS was tested on a small-scale smart home environment with a Raspberry Pi 4 Model B controller. Its intrusion detection system identified an intrusion with 96% accuracy while the physical surveillance system predicts the user with 81% accuracy.

The technology advance and convergence of cyber physical systems, smart sensors, short-range wireless communications, cloud computing, and smartphone apps have driven the proliferation of Internet of things (IoT) devices in smart homes and smart industry. In light of the high heterogeneity of IoT system, the prevalence of system vulnerabilities in IoT devices and applications, and the broad attack surface across the entire IoT protocol stack, a fundamental and urgent research problem of IoT security is how to effectively collect, analyze, extract, model, and visualize the massive network traffic of IoT devices for understanding what is happening to IoT devices. Towards this end, this paper develops and demonstrates an end-to-end system with three key components, i.e., the IoT network traffic monitoring system via programmable home routers, the backend IoT traffic behavior analysis system in the cloud, and the frontend IoT visualization system via smartphone apps, for monitoring, analyzing and virtualizing network traffic behavior of heterogeneous IoT devices in smart homes. The main contributions of this demonstration paper is to present a novel system with an end-to-end process of collecting, analyzing and visualizing IoT network traffic in smart homes.

Internet of Medical Things (IoMT) is a rapidly growing branch of IoT (Internet of Things), which requires special treatment to cyber security due to confidentiality of healthcare data and patient health threat. Healthcare data and automated medical devices might become vulnerable targets of malicious cyber-attacks. While a large number of robotic applications, including medical and healthcare, employ robot operating system (ROS) as their backbone, not enough attention is paid for ROS security. The paper discusses a security of ROS-based swing doors automation in the context of a robotic hospital framework, which should be protected from cyber-attacks.

With the development of Internet of Things (IoT) technology, the transaction behavior of IoT devices has gradually increased, which also brings the problem of transaction data security and transaction processing efficiency. As one of the research hotspots in the field of data security, blockchain technology has been widely applied in the maintenance of transaction records and the construction of financial payment systems. However, the proportion of microtransactions in the Internet of Things poses challenges to the coupling of blockchain and IoT devices. This paper proposes a three-party scalable architecture based on “IoT device-edge server-blockchain”. In view of the characteristics of micropayment, the verification mechanism of the execution results of the off-chain transaction is designed, and the bridge node is designed in the off-chain architecture, which ensures the finality of the blockchain to the transaction. According to system evaluation, this scalable architecture improves the processing efficiency of micropayments on blockchain, while ensuring its decentration equal to that of blockchain. Compared with other blockchain-based IoT device payment schemes, our architecture is more excellent in activity.

Nowadays, lives are very much easier with the help of IoT. Due to lack of protection and a greater number of connections, the management of IoT becomes more difficult To manage the network flow, a Software Defined Networking (SDN) has been introduced. The SDN has a great capability in automatic and dynamic distribution. For harmful attacks on the controller a centralized SDN architecture unlocks the scope. Therefore, to reduce these attacks in real-time, a securing SDN enabled IoT scenario infrastructure of Fog networks is preferred. The virtual switches have network enforcement authorized decisions and these are executed through the SDN network. Apart from this, SDN switches are generally powerful machines and simultaneously these are used as fog nodes. Therefore, SDN looks like a good selection for Fog networks of IoT. Moreover, dynamically distributing the necessary crypto keys are allowed by the centralized and software channel protection management solution, in order to establish the Datagram Transport Layer Security (DTIS) tunnels between the IoT devices, when demanded by the cyber security framework. Through the extensive deployment of this combination, the usage of CPU is observed to be 30% between devices and the latencies are in milliseconds range, and thus it presents the system feasibility with less delay. Therefore, by comparing with the traditional SDN, it is observed that the energy consumption is reduced by more than 90%.