Biblio
Power grids are undergoing major changes due to rapid growth in renewable energy resources and improvements in battery technology. While these changes enhance sustainability and efficiency, they also create significant management challenges as the complexity of power systems increases. To tackle these challenges, decentralized Internet-of-Things (IoT) solutions are emerging, which arrange local communities into transactive microgrids. Within a transactive microgrid, "prosumers" (i.e., consumers with energy generation and storage capabilities) can trade energy with each other, thereby smoothing the load on the main grid using local supply. It is hard, however, to provide security, safety, and privacy in a decentralized and transactive energy system. On the one hand, prosumers' personal information must be protected from their trade partners and the system operator. On the other hand, the system must be protected from careless or malicious trading, which could destabilize the entire grid. This paper describes Privacy-preserving Energy Transactions (PETra), which is a secure and safe solution for transactive microgrids that enables consumers to trade energy without sacrificing their privacy. PETra builds on distributed ledgers, such as blockchains, and provides anonymity for communication, bidding, and trading.
New generation communication technologies (e.g., 5G) enhance interactions in mobile and wireless communication networks between devices by supporting a large-scale data sharing. The vehicle is such kind of device that benefits from these technologies, so vehicles become a significant component of vehicular networks. Thus, as a classic application of Internet of Things (IoT), the vehicular network can provide more information services for its human users, which makes the vehicular network more socialized. A new concept is then formed, namely "Vehicular Social Networks (VSNs)", which bring both benefits of data sharing and challenges of security. Traditional public key infrastructures (PKI) can guarantee user identity authentication in the network; however, PKI cannot distinguish untrustworthy information from authorized users. For this reason, a trust evaluation mechanism is required to guarantee the trustworthiness of information by distinguishing malicious users from networks. Hence, this paper explores a trust evaluation algorithm for VSNs and proposes a cloud-based VSN architecture to implement the trust algorithm. Experiments are conducted to investigate the performance of trust algorithm in a vehicular network environment through building a three-layer VSN model. Simulation results reveal that the trust algorithm can be efficiently implemented by the proposed three-layer model.
Some IoT data are time-sensitive and cannot be processed in clouds, which are too far away from IoT devices. Fog computing, located as close as possible to data sources at the edge of IoT systems, deals with this problem. Some IoT data are sensitive and require privacy controls. The proposed Policy Enforcement Fog Module (PEFM), running within a single fog, operates close to data sources connected to their fog, and enforces privacy policies for all sensitive IoT data generated by these data sources. PEFM distinguishes two kinds of fog data processing. First, fog nodes process data for local IoT applications, running within the local fog. All real-time data processing must be local to satisfy real-time constraints. Second, fog nodes disseminate data to nodes beyond the local fog (including remote fogs and clouds) for remote (and non-real-time) IoT applications. PEFM has two components for these two kinds of fog data processing. First, Local Policy Enforcement Module (LPEM), performs direct privacy policy enforcement for sensitive data accessed by local IoT applications. Second, Remote Policy Enforcement Module (RPEM), sets up a mechanism for indirectly enforcing privacy policies for sensitive data sent to remote IoT applications. RPEM is based on creating and disseminating Active Data Bundles-software constructs bundling inseparably sensitive data, their privacy policies, and an execution engine able to enforce privacy policies. To prove effectiveness and efficiency of the solution, we developed a proof-of-concept scenario for a smart home IoT application. We investigate privacy threats for sensitive IoT data and show a framework for using PEFM to overcome these threats.
The IoT node works mostly in a specific scenario, and executes the fixed program. In order to make it suitable for more scenarios, this paper introduces a kind of the IoT node, which can change program at any time. And this node has intelligent and dynamic reconfigurable features. Then, a transport protocol is proposed. It enables this node to work in different scenarios and perform corresponding program. Finally, we use Verilog to design and FPGA to verify. The result shows that this protocol is feasible. It also offers a novel way of the IoT.
Internet of Things (IoT) will be emerged over many of devices that are dynamically networked. Because of distributed and dynamic nature of IoT, designing a recommender system for them is a challenging problem. Recently, cognitive systems are used to design modern frameworks in different types of computer applications such as cognitive radio networks and cognitive peer-to-peer networks. A cognitive system can learn to improve its performance while operating under its unknown environment. In this paper, we propose a framework for cognitive recommender systems in IoT. To the best of our knowledge, there is no recommender system based on cognitive systems in the IoT. The proposed algorithm is compared with the existing recommender systems.
The Radio Frequency Identification (RFID), as one of the key technologies in sensing layer of the Internet of Things (IoT) framework, has increasingly been deployed in a wide variety of application domains. But the reliability of RFID is still a great concern. This article introduces the group management of RFID passwords method, come up with by YUICHI KOBAYASHI and other researchers, which aimed to reduce the risk of privacy disclosure. But for reason that the password and pass key in the method, which are set to protect the ID, doesn't change and the ID is transmitted directly in the unsafe channel, it causes serious vulnerabilities that may be used by resourceful adversary. Thus, we proposed an improved method by using the random number to encrypt the password and switching the password into the temporally valid information. Besides, the protocol encrypts the ID during to avoid the direct transmission situation significantly increases the reliability.
On battery-free IoT devices such as passive RFID tags, it is extremely difficult, if not impossible, to run cryptographic algorithms. Hence physical-layer identification methods are proposed to validate the authenticity of passive tags. However no existing physical-layer authentication method of RFID tags that can defend against the signal replay attack. This paper presents Hu-Fu, a new direction and the first solution of physical layer authentication that is resilient to the signal replay attack, based on the fact of inductive coupling of two adjacent tags. We present the theoretical model and system workflow. Experiments based on our implementation using commodity devices show that Hu-Fu is effective for physical-layer authentication.
In this paper, a distributed architecture for the implementation of smart city has been proposed to facilitate various smart features like solid waste management, efficient urban mobility and public transport, smart parking, robust IT connectivity, safety and security of citizens and a roadmap for achieving it. How massive volume of IoT data can be analyzed and a layered architecture of IoT is explained. Why data integration is important for analyzing and processing of data collected by the different smart devices like sensors, actuators and RFIDs is discussed. The wireless sensor network can be used to sense the data from various locations but there has to be more to it than stuffing sensors everywhere for everything. Why only the sensor is not sufficient for data collection and how human beings can be used to collect data is explained. There is some communication protocols between the volunteers engaged in collecting data to restrict the sharing of data and ensure that the target area is covered with minimum numbers of volunteers. Every volunteer should cover some predefined area to collect data. Then the proposed architecture model is having one central server to store all data in a centralized server. The data processing and the processing of query being made by the user is taking place in centralized server.
The Internet of Things (IoT) is the latest Internet evolution that interconnects billions of devices, such as cameras, sensors, RFIDs, smart phones, wearable devices, ODBII dongles, etc. Federations of such IoT devices (or things) provides the information needed to solve many important problems that have been too difficult to harness before. Despite these great benefits, privacy in IoT remains a great concern, in particular when the number of things increases. This presses the need for the development of highly scalable and computationally efficient mechanisms to prevent unauthorised access and disclosure of sensitive information generated by things. In this paper, we address this need by proposing a lightweight, yet highly scalable, data obfuscation technique. For this purpose, a digital watermarking technique is used to control perturbation of sensitive data that enables legitimate users to de-obfuscate perturbed data. To enhance the scalability of our solution, we also introduce a contextualisation service that achieve real-time aggregation and filtering of IoT data for large number of designated users. We, then, assess the effectiveness of the proposed technique by considering a health-care scenario that involves data streamed from various wearable and stationary sensors capturing health data, such as heart-rate and blood pressure. An analysis of the experimental results that illustrate the unconstrained scalability of our technique concludes the paper.
Fog computing provides a new architecture for the implementation of the Internet of Things (IoT), which can connect sensor nodes to the cloud using the edge of the network. This structure has improved the latency and energy consumption in the cloud. In this heterogeneous and distributed environment, resource allocation is very important. Hence, scheduling will be a challenge to increase productivity and allocate resources appropriately to the tasks. Programs that run in this environment should be protected from intruders. We consider three parameters as authentication, integrity, and confidentiality to maintain security in fog devices. These parameters have time and computational overhead. In the proposed approach, we schedule the modules for the run in fog devices by heuristic algorithms based on data mining technique. The objective function is included CPU utilization, bandwidth, and security overhead. We compare the proposed algorithm with several heuristic algorithms. The results show that our proposed algorithm improved the average energy consumption of 63.27%, cost 44.71% relative to the PSO, ACO, SA algorithms.
IoT (Internet of Things) is a network of interconnected devices, designed to collect and exchange data which can then turn it into information, eventually into wisdom. IoT is a region where digital world converges with physical world. With the evolution of IoT, it is expected to create substantial impact on human lives. IoT ecosystem produces and exchanges sizeable data due to which IoT becomes an attractive target for adversary. The large-scale interconnectivity leads to various potential risk related to information security. Security assurance in IoT ecosystem is one of the major challenges to address. In this context, embedded security becomes a key issue in IoT devices which are constrained in terms of processing, power, memory and bandwidth. The focus of this paper is on the recommended design considerations for constrained IoT devices with the objective to achieve security by default. Considering established set of protocols along with best practices during design and development stage can address majority of security challenges.
Internet of Things (IoT) is an emerging paradigm in information technology (IT) that integrates advancements in sensing, computing and communication to offer enhanced services in everyday life. IoTs are vulnerable to sybil attacks wherein an adversary fabricates fictitious identities or steals the identities of legitimate nodes. In this paper, we model sybil attacks in IoT and evaluate its impact on performance. We also develop a defense mechanism based on behavioural profiling of nodes. We develop an enhanced AODV (EAODV) protocol by using the behaviour approach to obtain the optimal routes. In EAODV, the routes are selected based on the trust value and hop count. Sybil nodes are identified and discarded based on the feedback from neighbouring nodes. Evaluation of our protocol in ns-2 simulator demonstrates the effectiveness of our approach in identifying and detecting sybil nodes in IoT network.
In Sybil attacks, a physical adversary takes multiple fabricated or stolen identities to maliciously manipulate the network. These attacks are very harmful for Internet of Things (IoT) applications. In this paper we implemented and evaluated the performance of RPL (Routing Protocol for Low-Power and Lossy Networks) routing protocol under mobile sybil attacks, namely SybM, with respect to control overhead, packet delivery and energy consumption. In SybM attacks, Sybil nodes take the advantage of their mobility and the weakness of RPL to handle identity and mobility, to flood the network with fake control messages from different locations. To counter these type of attacks we propose a trust-based intrusion detection system based on RPL.
Establishing trust relationships between routing nodes represents a vital security requirement to establish reliable routing processes that exclude infected or selfish nodes. In this paper, we propose a new security scheme for the Internet of things and mainly for the RPL (Routing Protocol for Low-power and Lossy Networks) called: Metric-based RPL Trustworthiness Scheme (MRTS). The primary aim is to enhance RPL security and deal with the trust inference problem. MRTS addresses trust issue during the construction and maintenance of routing paths from each node to the BR (Border Router). To handle this issue, we extend DIO (DODAG Information Object) message by introducing a new trust-based metric ERNT (Extended RPL Node Trustworthiness) and a new Objective Function TOF (Trust Objective Function). In fact, ERNT represents the trust values for each node within the network, and TOF demonstrates how ERNT is mapped to path cost. In MRTS all nodes collaborate to calculate ERNT by taking into account nodes' behavior including selfishness, energy, and honesty components. We implemented our scheme by extending the distributed Bellman-Ford algorithm. Evaluation results demonstrated that the new scheme improves the security of RPL.
Internet of Things (IoT) is characterized by heterogeneous devices that interact with each other on a collaborative basis to fulfill a common goal. In this scenario, some of the deployed devices are expected to be constrained in terms of memory usage, power consumption and processing resources. To address the specific properties and constraints of such networks, a complete stack of standardized protocols has been developed, among them the Routing Protocol for Low-Power and lossy networks (RPL). However, this protocol is exposed to a large variety of attacks from the inside of the network itself. To fill this gap, this paper focuses on the design and the integration of a novel Link reliable and Trust aware model into the RPL protocol. Our approach aims to ensure Trust among entities and to provide QoS guarantees during the construction and the maintenance of the network routing topology. Our model targets both node and link Trust and follows a multidimensional approach to enable an accurate Trust value computation for IoT entities. To prove the efficiency of our proposal, this last has been implemented and tested successfully within an IoT environment. Therefore, a set of experiments has been made to show the high accuracy level of our system.
In the past the security of building automation solely depended on the security of the devices inside or tightly connected to the building. In the last years more devices evolved using some kind of cloud service as a back-end or providers supplying some kind of device to the user. Also, the number of building automation systems connected to the Internet for management, control, and data storage increases every year. These developments cause the appearance of new threats on building automation. As Internet of Thing (IoT) and building automation intertwine more and more these threats are also valid for IoT installations. The paper presents new attack vectors and new threats using the threat model of Meyer et al.[1].
Vehicular Ad-Hoc Network (VANET) is a form of Peer-to-Peer (P2P) wireless communication between vehicles, which is characterized by the high mobility. In practice, VANET can be utilized to cater connections via multi-hop communication between vehicles to provide traffic information seamlessly, such as traffic jam and traffic accident, without the need of dedicated centralized infrastructure. Although dedicated infrastructures may also be involved in VANET, such as Road Side Units (RSUs), most of the time VANET relies solely on Vehicle-to-Vehicle (V2V) communication, which makes it vulnerable to several potential attacks in P2P based communication, as there are no trusted authorities that provide authentication and security. One of the potential threats is a Sybil attack, wherein an adversary uses a considerable number of forged identities to illegitimately infuse false or biased information which may mislead a system into making decisions benefiting the adversary. Avoiding Sybil attacks in VANET is a difficult problem, as there are typically no trusted authorities that provide cryptographic assurance of Sybil resilience. This paper presents a technique to detect and mitigate Sybil attacks, which requires no dedicated infrastructure, by utilizing just V2V communication. The proposed method work based on underlying assumption that says the mobility of vehicles in high vehicle density and the limited transmission power of the adversary creates unique groups of vehicle neighbors at a certain time point, which can be calculated in a statistical fashion providing a temporal and spatial analysis to verify real and impersonated vehicle identities. The proposed method also covers the mitigation procedures to create a trust model and announce neighboring vehicles regarding the detected tempered identities in a secure way utilizing Diffie-Hellman key distribution. This paper also presents discussions concerning the proposed approach with regard to benefits and drawbacks of sparse road condition and other potential threats.
In the production process of embedded device, due to the frequent reuse of third-party libraries or development kits, there are large number of same vulnerabilities that appear in more than one firmware. Homology analysis is often used in detecting this kind of vulnerabilities caused by code reuse or third-party reuse and in the homology analysis, the widely used methods are mainly Binary difference analysis, Normalized compression distance, String feature matching and Fuzz hash. But when we use these methods for homology analysis, we found that the detection result is not ideal and there is a high false positive rate. Focusing on this problem, we analyzed the application scenarios of these four methods and their limitations by combining different methods and different types of files and the experiments show that the combination of methods and files have a better performance in homology analysis.
The widespread diffusion of the Internet of Things (IoT) is introducing a huge number of Internet-connected devices in our daily life. Mainly, wearable devices are going to have a large impact on our lifestyle, especially in a healthcare scenario. In this framework, it is fundamental to secure exchanged information between these devices. Among other factors, it is important to take into account the link between a wearable device and a smart unit (e.g., smartphone). This connection is generally obtained via specific wireless protocols such as Bluetooth Low Energy (BLE): the main topic of this work is to analyse the security of this communication link. In this paper we expose, via an experimental campaign, a methodology to perform a vulnerability assessment (VA) on wearable devices communicating with a smartphone. In this way, we identify several security issues in a set of commercial wearable devices.
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.
This paper investigates the suitability of employing various measurable features derived from multiple wearable devices (Apple Watch), for the generation of unique authentication and encryption keys related to the user. This technique is termed as ICMetrics. The ICMetrics technology requires identifying the suitable features in an environment for key generation most useful for online services. This paper presents an evaluation of the feasibility of identifying a unique user based on desirable feature set and activity data collected over short and long term and explores how the number of samples being factored into the ICMetrics system affects uniqueness of the key.