Biblio

Authentication is the fundamental security service used in almost all remote applications. All such sensitive applications over an open network need authentication mechanism that should be delivered in a trusted way. In this paper, we design an RSA based authentication system for smart IoT environment over the air network using state-of-the-art industry standards. Our system provide security services including X.509 certificate, RSA based Public Key Infrastructure (PKI), challenge/response protocols with the help of proxy induced security service provider. We describe an innovative system model, protocol design, system architecture and evaluation against known threats. Also the implemented solution designed as an add on service for multiple other sensitive applications (smart city apps, cyber physical systems etc.) which needs the support of X.509 certificate based on hard tokens to populate other security services including confidentiality, integrity, non-repudiation, privacy and anonymity of the identities. The proposed scheme is evaluated against known vulnerabilities and given detail comparisons with popular known authentication schemes. The result shows that our proposed scheme mitigate all the known security risks and provide highest level assurance to smart gadgets.

IoT trust management is a security solution that assures the trust between different IoT entities before establishing any relationship with other anonymous devices. Recent researches presented in the literature tend to use a Blockchain-based trust management model for IoT besides the fog node approach in order to address the constraints of IoT resources. Actually, Blockchain has solved many drawbacks of centralized models. However, it is still not preferable for dealing with massive data produced by IoT because of its drawbacks such as delay, network overhead, and scalability issues. Therefore, in this paper we define some factors that should be considered when designing scalable models, and we propose a fully distributed trust management model for IoT that provide a large-scale trust model and address the limitations of Blockchain. We design our model based on a new approach called Holochain considering some security issues, such as detecting misbehaviors, data integrity and availability.

Growing amounts of research on IoT and its implications for security, privacy, economy and society has been carried out to inform policies and design. However, ordinary people who are citizens and users of these emerging technologies have rarely been involved in the processes that inform these policies, governance mechanisms and design due to the institutionalised processes that prioritise objective knowledge over subjective ones. People's subjective experiences are often discarded. This priority is likely to further widen the gap between people, technology policies and design as technologies advance towards delegated human agencies, which decreases human interfaces in technology-mediated relationships with objects, systems, services, trade and other (often) unknown third-party beneficiaries. Such a disconnection can have serious implications for policy implementation, especially when it involves human limitations. To address this disconnection, we argue that a space for people to meaningfully contribute their subjective knowledge — experience- to complex technology policies that, in turn, shape their experience and well-being needs to be constructed. To this end, our paper contributes the design and pilot implementation of a method to reconnect and involve people in IoT security policymaking and development.

Upon the new paradigm of Cellular Internet of Things, through the usage of technologies such as Narrowband IoT (NB-IoT), a massive amount of IoT devices will be able to use the mobile network infrastructure to perform their communications. However, it would be beneficial for these devices to use the same security mechanisms that are present in the cellular network architecture, so that their connections to the application layer could see an increase on security. As a way to approach this, an identity management and provisioning mechanism, as well as an identity federation between an IoT platform and the cellular network is proposed as a way to make an IoT device deemed worthy of using the cellular network and perform its actions.

Internet of Things is nowadays growing faster than ever before. Operators are planning or already creating dedicated networks for this type of devices. There is a need to create dedicated solutions for this type of network, especially solutions related to information security. In this article we present a mechanism of security-aware routing, which takes into account the evaluation of trust in devices and packet flows. We use trust relationships between flows and network nodes to create secure SDN paths, not ignoring also QoS and energy criteria. The system uses SDN infrastructure, enriched with Cognitive Packet Networks (CPN) mechanisms. Routing decisions are made by Random Neural Networks, trained with data fetched with Cognitive Packets. The proposed network architecture, implementing the security-by-design concept, was designed and is being implemented within the SerIoT project to demonstrate secure networks for the Internet of Things (IoT).

In recent years, there is a surge of interest in approaches pertaining to security issues of Internet of Things deployments and applications that leverage machine learning and deep learning techniques. A key prerequisite for enabling such approaches is the development of scalable infrastructures for collecting and processing security-related datasets from IoT systems and devices. This paper introduces such a scalable and configurable data collection infrastructure for data-driven IoT security. It emphasizes the collection of (security) data from different elements of IoT systems, including individual devices and smart objects, edge nodes, IoT platforms, and entire clouds. The scalability of the introduced infrastructure stems from the integration of state of the art technologies for large scale data collection, streaming and storage, while its configurability relies on an extensible approach to modelling security data from a variety of IoT systems and devices. The approach enables the instantiation and deployment of security data collection systems over complex IoT deployments, which is a foundation for applying effective security analytics algorithms towards identifying threats, vulnerabilities and related attack patterns.

With the emergence of IoT, wearable devices are drawing attention and becoming part of our daily life. These wearable devices collect private information about their wearers. Mostly, a secure authentication process is used to verify a legitimate user that relies on the mobile terminal. Similarly, remote cloud services are used for verification and authentication of both wearable devices and wearers. Security is necessary to preserve the privacy of users. Some traditional authentication protocols are proposed which have vulnerabilities and are prone to different attacks like forgery, de-synchronization, and un-traceability issues. To address these vulnerabilities, recently, Wu et al. (2017) proposed a cloud-assisted authentication scheme which is costly in terms of computations required. Therefore this paper proposed an improved, lightweight and computationally efficient authentication scheme for wearable devices. The proposed scheme provides similar level of security as compared to Wu's (2017) scheme but requires 41.2% lesser computations.

In this paper, we focuses on an access control issue in the Internet of Things (IoT). Generally, we firstly propose a decentralized IoT system based on blockchain. Then we establish a secure fine-grained access control strategies for users, devices, data, and implement the strategies with smart contract. To trigger the smart contract, we design different transactions. Finally, we use the multi-index table struct for the access right's establishment, and store the access right into Key-Value database to improve the scalability of the decentralized IoT system. In addition, to improve the security of the system we also store the access records on the blockchain and database.

Recent advances in networking and communication technologies have enabled Internet-of-Things (IoT) devices to communicate more frequently and faster. An IoT device typically transmits data over the Internet which is an insecure channel. Cyber attacks such as denial-of-service (DoS), man-in-middle, and SQL injection are considered as big threats to IoT devices. In this paper, an anomaly-based intrusion detection scheme is proposed that can protect sensitive information and detect novel cyber-attacks. The Artificial Bee Colony (ABC) algorithm is used to train the Random Neural Network (RNN) based system (RNN-ABC). The proposed scheme is trained on NSL-KDD Train+ and tested for unseen data. The experimental results suggest that swarm intelligence and RNN successfully classify novel attacks with an accuracy of 91.65%. Additionally, the performance of the proposed scheme is also compared with a hybrid multilayer perceptron (MLP) based intrusion detection system using sensitivity, mean of mean squared error (MMSE), the standard deviation of MSE (SDMSE), best mean squared error (BMSE) and worst mean squared error (WMSE) parameters. All experimental tests confirm the robustness and high accuracy of the proposed scheme.

Context-based zero-interaction has become the trend for smart IoT device pairing. In this paper, we propose a secure and usable mechanism to authenticate devices co-located in smart home scenario, and build a secure communication channel between legitimate devices by utilizing on-board microphones to capture a common audio context. After receiving randomly generated sound signals, smart IoT device uses the time intervals between salient sound signals to derive audio fingerprint which can be matched among co-present devices and then be used to bootstrap trust of the devices. The protocol is based on the idea that devices co-located within a physical security boundary (e.g., single family house) can hear similar sounds, and the devices outside would miss parts of sound signals due to the attenuation when sounds pass through the wall. To accelerate the generation rate of audio fingerprint, an extra sound source is introduced. We implement our protocol on Android devices, and the experiment results show that the protocol can distinguish the malicious devices outside from the legitimate devices located inside a security boundary and can quickly establish a strong secret-key between legitimate devices.

Nowadays, physical health of equipment controlled by Cyber-Physical Systems (CPS) is a significant concern. This paper reports a work, in which, a hardware is placed between Programmable Logic Controller (PLC) and the actuator as a solution. The proposed hardware operates in two conditions, i.e. passive and active. Operation of the proposed solution is based on the repetitive operational profile of the actuators. The normal operational profile of the actuator is fed to the protective hardware and is considered as the normal operating condition. In the normal operating condition, the middleware operates in its passive mode and simply monitors electronic signals passing between PLC and Actuator. In case of any malicious operation, the proposed hardware operates in its active mode and both slowly stops the actuator and sends an alert to SCADA server initiating execution of the actuator's emergency profile. Thus, the proposed hardware gains control over the actuator and prevents any physical damage on the operating devices. Two sample experiments are reported in which, results of implementing the proposed solution are reported and assessed. Results show that once the PLC sends incorrect data to actuator, the proposed hardware detects it as an anomaly. Therefore, it does not allow the PLC to send incorrect and unauthorized data pattern to its actuator. Significance of the paper is in introducing a solution to prevent destruction of physical devices apart from source or purpose of the encountered anomaly and apart from CPS functionality or PLC model and operation.

The Internet of Things (IoT) market is growing rapidly, allowing continuous evolution of new technologies. Alongside this development, most IoT devices are easy to compromise, as security is often not a prioritized characteristic. This paper proposes a novel IoT Security Model (IoTSM) that can be used by organizations to formulate and implement a strategy for developing end-to-end IoT security. IoTSM is grounded by the Software Assurance Maturity Model (SAMM) framework, however it expands it with new security practices and empirical data gathered from IoT practitioners. Moreover, we generalize the model into a conceptual framework. This approach allows the formal analysis for security in general and evaluates an organization's security practices. Overall, our proposed approach can help researchers, practitioners, and IoT organizations, to discourse about IoT security from an end-to-end perspective.

The Internet of Things (IoT) consists of embedded devices that sense and manage our environment in a growing range of applications. Large-scale IoT systems such as smart cities require significant investment in both equipment and personnel. To maximize return on investment, IoT platforms should support multiple third-party applications and adaptation of infrastructure over time. Realizing the vision of shared IoT platforms demands strong security guarantees. That is particularly challenging considering the limited capability and resource constraints of many IoT devices. In this paper, we present SPEED, an approach to secure erasure with verifiability in IoT. Secure erasure is a fundamental property when it comes to share an IoT platform with other users which guarantees the cleanness of a device's memory at the beginning of the application deployment as well as at the time of releasing the underlying IoT device. SPEED relies on two security primitives: memory isolation and distance bounding protocol. We evaluate the performance of SPEED by implementing it on a simple bare-metal IoT device belongs to Class-1. Our evaluation results show a limited overhead in terms of memory footprint, time, and energy consumption.

Although the vision of 5G is to accommodate billions IoT devices and applications, its success depends very much on its ability to provide enhanced and affordable security. This paper introduces an Identity Federation solution which reuses the SIM authentication for cellular IoT devices enabling single-sign-on. The proposed solution alleviates the IoT provider's burden of device identity management at the same time as the operational costs are reduced considerably. The proposed solution is realized by open source software for LTE, identity management and IoT.

The Internet of Things (IoT) is an emerging technology, an extension of the traditional Internet which make everything is connected each other based on Radio Frequency Identification (RFID), Sensor, GPS or Machine to Machine technologies, etc. The security issues surrounding IoT have been of detrimental impact to its development and has consequently attracted research interest. However, there are very few approaches which assess the security of IoT from the perspective of an attacker. Penetration testing is widely used to evaluate traditional internet or systems security to date and it normally spends numerous cost and time. In this paper, we analyze the security problems of IoT and propose a penetration testing approach and its automation based on belief-desire-intention (BDI) model to evaluate the security of the IoT.

The Internet of things (IoT) is a distributed, networked system composed of many embedded sensor devices. Unfortunately, these devices are resource constrained and susceptible to malicious data-integrity attacks and failures, leading to unreliability and sometimes to major failure of parts of the entire system. Intrusion detection and failure handling are essential requirements for IoT security. Nevertheless, as far as we know, the area of data-integrity detection for IoT has yet to receive much attention. Most previous intrusion-detection methods proposed for IoT, particularly for wireless sensor networks (WSNs), focus only on specific types of network attacks. Moreover, these approaches usually rely on using precise values to specify abnormality thresholds. However, sensor readings are often imprecise and crisp threshold values are inappropriate. To guarantee a lightweight, dependable monitoring system, we propose a novel hierarchical framework for detecting abnormal nodes in WSNs. The proposed approach uses fuzzy logic in event-condition-action (ECA) rule-based WSNs to detect malicious nodes, while also considering failed nodes. The spatiotemporal semantics of heterogeneous sensor readings are considered in the decision process to distinguish malicious data from other anomalies. Following our experiments with the proposed framework, we stress the significance of considering the sensor correlations to achieve detection accuracy, which has been neglected in previous studies. Our experiments using real-world sensor data demonstrate that our approach can provide high detection accuracy with low false-alarm rates. We also show that our approach performs well when compared to two well-known classification algorithms.

Collaborative smart services provide functionalities which exploit data collected from different sources to provide benefits to a community of users. Such data, however, might be privacy sensitive and their disclosure has to be avoided. In this paper, we present a distributed multi-tier framework intended for smart-environment management, based on usage control for policy evaluation and enforcement on devices belonging to different collaborating entities. The proposed framework exploits secure multi-party computation to evaluate policy conditions without disclosing actual value of evaluated attributes, to preserve privacy. As reference example, a smart-grid use case is presented.

Recently, as the age of the Internet of Things is approaching, there are more and more devices that communicate data with each other by incorporating sensors and communication functions in various objects. If the IoT is miniaturized, it can be regarded as a sensor having only the sensing ability and the low performance communication ability. Low-performance sensors are difficult to use high-quality communication, and wireless security used in expensive wireless communication devices cannot be applied. Therefore, this paper proposes authentication and key Agreement that can be applied in sensor networks using communication with speed less than 1 Kbps and has limited performances.

The Internet of Things (IoT) is the network where physical devices, sensors, appliances and other different objects can communicate with each other without the need for human intervention. Wireless Sensor Networks (WSNs) are main building blocks of the IoT. Both the IoT and WSNs have many critical and non-critical applications that touch almost every aspect of our modern life. Unfortunately, these networks are prone to various types of security threats. Therefore, the security of IoT and WSNs became crucial. Furthermore, the resource limitations of the devices used in these networks complicate the problem. One of the most recent and effective approaches to address such challenges is machine learning. Machine learning inspires many solutions to secure the IoT and WSNs. In this paper, we survey the different threats that can attack both IoT and WSNs and the machine learning techniques developed to counter them.

Internet-of-Things (IoTs) are becoming more and more popular in our life. IoT devices are generally designed for sensing or actuation purposes. However, the current sensing system on IoT devices lacks the understanding of sensing needs, which diminishes the sensing flexibility, isolation, and security when multiple sensing applications need to use sensor resources. In this work, we propose VirtSense, an ARM TrustZone based virtual sensing system, to provide each sensing application a virtual sensor instance, which further enables a safe, flexible and isolated sensing environment on the IoT devices. Our preliminary results show that VirtSense: 1) can provide virtual sensor instance for each sensing application so that the sensing needs of each application will be satisfied without affecting others; 2) is able to enforce access control policy even under an untrusted environment.

Internet of Things refers to a paradigm consisting of a variety of uniquely identifiable day to day things communicating with one another to form a large scale dynamic network. Securing access to this network is a current challenging issue. This paper proposes an encryption system suitable to IoT features. In this system we integrated the fuzzy commitment scheme in DCT-based recognition method for fingerprint. To demonstrate the efficiency of our scheme, the obtained results are analyzed and compared with direct matching (without encryption) according to the most used criteria; FAR and FRR.

Popularization of the Internet-of-Things (IoT) has brought widespread concerns on IoT security, especially in face of several recent security incidents related to IoT devices. Due to the resource-constrained nature of many IoT devices, security offloading has been proposed to provide good-enough security for IoT with minimum overhead on the devices. In this paper, we investigate the inevitable risk associated with security offloading: the unprotected and unmonitored transmission from IoT devices to the offloaded security mechanisms. An important challenge in modeling the security risk is the dynamic nature of IoT due to demand fluctuations and infrastructure instability. We propose a stochastic model to capture both the expected and worst-case security risks of an IoT system. We then propose a framework to efficiently address the optimal robust deployment of security mechanisms in IoT. We use results from extensive simulations to demonstrate the superb performance and efficiency of our approach compared to several other algorithms.

An attack detection scheme is proposed to detect data integrity attacks on sensors in Cyber-Physical Systems (CPSs). A combined fingerprint for sensor and process noise is created during the normal operation of the system. Under sensor spoofing attack, noise pattern deviates from the fingerprinted pattern enabling the proposed scheme to detect attacks. To extract the noise (difference between expected and observed value) a representative model of the system is derived. A Kalman filter is used for the purpose of state estimation. By subtracting the state estimates from the real system states, a residual vector is obtained. It is shown that in steady state the residual vector is a function of process and sensor noise. A set of time domain and frequency domain features is extracted from the residual vector. Feature set is provided to a machine learning algorithm to identify the sensor and process. Experiments are performed on two testbeds, a real-world water treatment (SWaT) facility and a water distribution (WADI) testbed. A class of zero-alarm attacks, designed for statistical detectors on SWaT are detected by the proposed scheme. It is shown that a multitude of sensors can be uniquely identified with accuracy higher than 90% based on the noise fingerprint.

The Semantic Web can be used to enable the interoperability of IoT devices and to annotate their functional and nonfunctional properties, including security and privacy. In this paper, we will show how to use the ontology and JSON-LD to annotate connectivity, security and privacy properties of IoT devices. Out of that, we will present our prototype for a lightweight, secure application level protocol wrapper that ensures communication consistency, secrecy and integrity for low cost IoT devices like the ESP8266 and Photon particle.

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.