With the rise of IoT applications, about 20.4 billion devices will be online in 2020, and that number will rise to 75 billion a month by 2025. Different sensors in IoT devices let them get and process data remotely and in real time. Sensors give them information that helps them make smart decisions and manage IoT environments well. IoT Security is one of the most important things to think about when you're developing, implementing, and deploying IoT platforms. People who use the Internet of Things (IoT) say that it allows people to communicate, monitor, and control automated devices from afar. This paper shows how to use Deep learning and machine learning to make an IDS that can be used on IoT platforms as a service. In the proposed method, a cnn mapped the features, and a random forest classifies normal and attack classes. In the end, the proposed method made a big difference in all performance parameters. Its average performance metrics have gone up 5% to 6%.

For the Internet of things (IoT) secure data aggregation issues, data privacy-preserving and limited computation ability and energy of nodes should be tradeoff. Based on analyzing the pros-and-cons of current works, a low energy- consuming secure data aggregation method (LCSDA) was proposed. This method uses shortest path principle to choose neighbor nodes and generates the data aggregation paths in the cluster based on prim minimum spanning tree algorithm. Simulation results show that this method could effectively cut down energy consumption and reduce the probability of cluster head node being captured, in the same time preserving data privacy.

The proliferation of linked devices in decisive infrastructure fields including health care and the electric grid is transforming public perceptions of critical infrastructure. As the world grows more mobile and connected, as well as as the Internet of Things (IoT) expands, the growing interconnectivity of new critical sectors is being fuelled. Interruptions in any of these areas can have ramifications across numerous sectors and potentially the world. Crucial industries are critical to contemporary civilization. In today's hyper-connected world, critical infrastructure is more vulnerable than ever to cyber assaults, whether they are state-sponsored, carried out by criminal organizations, or carried out by individuals. In a world where more and more gadgets are interconnected, hackers have more and more entry points via which they may damage critical infrastructure. Significant modifications to an organization's main technological systems have created a new threat surface. The study's goal is to raise awareness about the challenges of protecting digital infrastructure in the future while it is still in development. Fog architecture is designed based on functionality once the infrastructure that creates large data has been established. There's also an in-depth look of fog-enabled IoT network security requirements. The next section examines the security issues connected with fog computing, as well as the privacy and trust issues raised by fog-enabled Internet of Things (IoT). Block chain is also examined to see how it may help address IoT security problems, as well as the complimentary interrelationships between block-chain and fog computing. Additionally, Formalizes big data security goal and scope, develops taxonomy for identifying risks to fog-based Internet of Things systems, compares current development contributions to security service standards, and proposes interesting study areas for future studies, all within this framework

The Internet of Things devices is rapidly becoming widespread, as are IoT services. Their achievement has not gone unnoticed, as threats as well as attacks towards IoT devices as well as services continue to grow. Cyber attacks are not unique to IoT, however as IoT becomes more ingrained in our lives as well as communities, it is imperative to step up as well as take cyber defense seriously. As a result, there is a genuine need to protect IoT, which necessitates a thorough understanding of the dangers and attacks against IoT infrastructure. The purpose of this study is to define threat types, as well as to assess and characterize intrusions and assaults against IoT devices as well as services

In recent years, the need for seamless connectivity has increased across various network platforms with demands coming from industries, home, mobile, transportation and office networks. The 5th generation (5G) network is being deployed to meet such demand of high-speed seamless network device connections. The seamless connectivity 5G provides could be a security threat allowing attacks such as distributed denial of service (DDoS) because attackers might have easy access into the network infrastructure and higher bandwidth to enhance the effects of the attack. The aim of this research is to provide a security solution for 5G technology to DDoS attacks by managing the response to threats posed by DDoS. Deploying a security policy language which is reactive and event-oriented fits into a flexible, efficient, and lightweight security approach. A policy in our language consists of an event whose occurrence triggers a policy rule where one or more actions are taken.

With the growing number of IoT applications and devices, IoT security breaches are a dangerous reality. Cost pressure and complexity of security tests for embedded systems and networked infrastructure are often the excuse for skipping them completely. In our paper we introduce SecLab security test lab to overcome that problem. Based on a flexible and lightweight architecture, SecLab allows developers and IoT security specialists to harden their systems with a low entry hurdle. The open architecture supports the reuse of existing external security test libraries and scalability for the assessment of complex IoT Systems. A reference implementation of security tests in a realistic IoT application scenario proves the approach.

The electrical grid connects all the generating stations to supply uninterruptible power to the consumers. With the advent of technology, smart sensors and communication are integrated with the existing grid to behave like a smart system. This smart grid is a two-way communication that connects the consumers and producers. It is a connected smart network that integrates electricity generation, transmission, substation, distribution, etc. In this smart grid, clean, reliable power with a high-efficiency rate of transmission is available. In this paper, a highly efficient smart management system of a smart grid with overall protection is proposed. This management system checks and monitors the parameters periodically. This future technology also develops a smart transformer with ac and dc compatibility, for self-protection and for the healing process.

In order to solve the problem of high data collision probability, high access delay and high-power consumption in random access process of power Internet of Things, an access scheme for large-scale micro-power wireless sensors based on slot-scheduling and hybrid mode is presented. This scheme divides time into different slots and designs a slot-scheduling algorithm according to network workload and power consumption. Sensors with different service priorities are arranged in different time slots for competitive access, using appropriate random-access mechanism. And rationally arrange the number of time slots and competing end-devices in different time slots. This scheme is able to meet the timeliness requirements of different services and reduce the overall network power consumption when dealing with random access scenarios of large-scale micro-power wireless sensor network. Based on the simulation results of actual scenarios, this access scheme can effectively reduce the overall power consumption of the network, and the high priority services can meet the timeliness requirements on the premise of lower power consumption, while the low priority services can further reduce power consumption.

For some countries around the world, meeting demand is a serious concern. Power supply market is increasingly increasing, posing a big challenge for various countries throughout the world. The increasing expansion in the market for power needs upgrading system dependability to increase the smart grid's resilience. This smart electric grid has a sensor that analyses grid power availability and sends regular updates to the organisation. The internet is currently being utilized to monitor processes and place orders for running variables from faraway places. A large number of scanners have been used to activate electrical equipment for domestic robotics for a long period in the last several days. Conversely, if it is not correctly implemented, it will have a negative impact on cost-effectiveness as well as productivity. For something like a long time, home automation has relied on a large number of sensor nodes to control electrical equipment. Since there are so many detectors, this isn't cost-effective. In this article, develop and accept a wireless communication component and a management system suitable for managing independent efficient network units from voltage rises and voltage control technologies in simultaneous analyzing system reliability in this study. This research paper has considered secondary method to collect relevant and in-depth data related to the wireless sensor network and its usage in smart grid monitoring.

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.