Biblio

Future that IoT has to enhance the productivity on healthcare applications.

The Internet of Things (IoT) promises to tackle a range of environmental challenges and deliver large efficiency gains in industry by embedding computational intelligence, sensing and control in our physical environment. Multiple independent parties are increasingly seeking to leverage shared IoT infrastructure, using a similar model to the cloud, and thus require constrained IoT devices to become microservice-hosting platforms that can securely and concurrently execute their code and interoperate. This vision demands that heterogeneous services, peripherals and platforms are provided with an expanded set of security guarantees to prevent third-party services from hijacking the platform, resource-level access control and accounting, and strong isolation between running processes to prevent unauthorized access to third-party services and data. This paper introduces Polyglot CerberOS, a resource-secure operating system for multi-tenant IoT devices that is realised through a reconfigurable virtual machine which can simultaneously execute interoperable services, written in different languages. We evaluate Polyglot CerberOS on IETF Class-1 devices running both Java and C services. The results show that interoperability and strong security guarantees for multilingual services on multi-tenant commodity IoT devices are feasible, in terms of performance and memory overhead, and transparent for developers.

Ever-driven by technological innovation, the Internet of Things (IoT) is continuing its exceptional evolution and growth into the common consumer space. In the wake of these developments, this paper proposes a framework for an IoT home security system that is secure, expandable, and accessible. Congruent with the ideals of the IoT, we are proposing a system utilizing an ultra-low-power wireless sensor network which would interface with a central hub via Bluetooth 4, commonly referred to as Bluetooth Low Energy (BLE), to monitor the home. Additionally, the system would interface with an Amazon Echo to accept user voice commands. The aforementioned central hub would also act as a web server and host an internet accessible configuration page from which users could monitor and customize their system. An internet-connected system would carry the capability to notify the users of system alarms via SMS or email. Finally, this proof of concept is intended to demonstrate expandability into other areas of home automation or building monitoring functions in general.

Nowadays, the rapid development of the Internet of Things facilitates human life and work, while it also brings great security risks to the society due to the frequent occurrence of various security issues. IoT device has the characteristics of large-scale deployment and single responsibility application, which makes it easy to cause a chain reaction and results in widespread privacy leakage and system security problems when the software vulnerability is identified. It is difficult to guarantee that there is no security hole in the IoT operating system which is usually designed for MCU and has no kernel mode. An alternative solution is to identify the security issues in the first time when the system is hijacked and suspend the suspicious task before it causes irreparable damage. This paper proposes KLRA (A Kernel Level Resource Auditing Tool) for IoT Operating System Security This tool collects the resource-sensitive events in the kernel and audit the the resource consumption pattern of the system at the same time. KLRA can take fine-grained events measure with low cost and report the relevant security warning in the first time when the behavior of the system is abnormal compared with daily operations for the real responsibility of this device. KLRA enables the IoT operating system for MCU to generate the security early warning and thereby provides a self-adaptive heuristic security mechanism for the entire IoT system.

Industrial control systems are changing from monolithic to distributed and interconnected architectures, entering the era of industrial IoT. One fundamental issue is that security properties of such distributed control systems are typically only verified empirically, during development and after system deployment. We propose a novel modelling framework for the security verification of distributed industrial control systems, with the goal of moving towards early design stage formal verification. In our framework we model industrial IoT infrastructures, attack patterns, and mitigation strategies for countering attacks. We conduct model checking-based formal analysis of system security through scenario execution, where the analysed system is exposed to attacks and implement mitigation strategies. We study the applicability of our framework for large systems using a scalability analysis.

In this paper, we propose a new randomized response algorithm that can achieve differential-privacy and utility guarantees for consumer's behaviors, and process a batch of data at each time. Firstly, differing from traditional differential private approach-es, we add randomized response noise into the behavior signa-tures matrix to achieve an acceptable utility-privacy tradeoff. Secondly, a behavior signature modeling method based on sparse coding is proposed. After some lightweight trainings us-ing the energy consumption data, the dictionary will be associat-ed with the behavior characteristics of the electric appliances. At last, through the experimental results verification, we find that our Algorithm can preserve consumer's privacy without comprising utility.

In Smart Grids (SGs), data aggregation process is essential in terms of limiting packet size, data transmission amount and data storage requirements. This paper presents a novel Domingo-Ferrer additive privacy based Secure Data Aggregation (SDA) scheme for Fog Computing based SGs (FCSG). The proposed protocol achieves end-to-end confidentiality while ensuring low communication and storage overhead. Data aggregation is performed at fog layer to reduce the amount of data to be processed and stored at cloud servers. As a result, the proposed protocol achieves better response time and less computational overhead compared to existing solutions. Moreover, due to hierarchical architecture of FCSG and additive homomorphic encryption consumer privacy is protected from third parties. Theoretical analysis evaluates the effects of packet size and number of packets on transmission overhead and the amount of data stored in cloud server. In parallel with the theoretical analysis, our performance evaluation results show that there is a significant improvement in terms of data transmission and storage efficiency. Moreover, security analysis proves that the proposed scheme successfully ensures the privacy of collected data.

Recently a huge trend on the internet of things (IoT) and an exponential increase in automated tools are helping malware producers to target IoT devices. The traditional security solutions against malware are infeasible due to low computing power for large-scale data in IoT environment. The number of malware and their variants are increasing due to continuous malware attacks. Consequently, the performance improvement in malware analysis is critical requirement to stop rapid expansion of malicious attacks in IoT environment. To solve this problem, the paper proposed a novel framework for classifying malware in IoT environment. To achieve flne-grained malware classification in suggested framework, the malware image classification system (MICS) is designed for representing malware image globally and locally. MICS first converts the suspicious program into the gray-scale image and then captures hybrid local and global malware features to perform malware family classification. Preliminary experimental outcomes of MICS are quite promising with 97.4% classification accuracy on 9342 windows suspicious programs of 25 families. The experimental results indicate that proposed framework is quite capable to process large-scale IoT malware.

The growing interest in the smart device/home/city has resulted in increasing popularity of Internet of Things (IoT) deployment. However, due to the open and heterogeneous nature of IoT networks, there are various challenges to deploy an IoT network, among which security and scalability are the top two to be addressed. To improve the security and scalability for IoT networks, we propose a Software-Defined Virtual Private Network (SD-VPN) solution, in which each IoT application is allocated with its own overlay VPN. The VPN tunnels used in this paper are VxLAN based tunnels and we propose to use the SDN controller to push the flow table of each VPN to the related OpenvSwitch via the OpenFlow protocol. The SD-VPN solution can improve the security of an IoT network by separating the VPN traffic and utilizing service chaining. Meanwhile, it also improves the scalability by its overlay VPN nature and the VxLAN technology.

Through the internet and local networks, IoT devices exchange data. Most of the IoT devices are low-power devices, meaning that they are designed to use less electric power. To secure data transmission, it is required to encrypt the messages. Encryption and decryption of messages are computationally expensive activities, thus require considerable amount of processing and memory power which is not affordable to low-power IoT devices. Therefore, not all secure transmission protocols are low-power IoT devices friendly. This study proposes a secure data transmission protocol for low-power IoT devices. The design inherits some features in Kerberos and onetime password concepts. The protocol is designed for devices which are connected to each other, as in a fully connected network topology. The protocol uses symmetric key cryptography under the assumption of that the device specific keys are never being transmitted over the network. It resists DoS, message replay and Man-of-the-middle attacks while facilitating the key security concepts such as Authenticity, Confidentiality and Integrity. The designed protocol uses less number of encryption/decryption cycles and maintain session based strong authentication to facilitate secure data transmission among nodes.

The main security problems, typical for the Internet of Things (IoT), as well as the purpose of gaining unauthorized access to the IoT, are considered in this paper. Common characteristics of the most widespread botnets are provided. A method to detect compromised IoT devices included into a botnet is proposed. The method is based on a model of logistic regression. The article describes a developed model of logistic regression which allows to estimate the probability that a device initiating a connection is running a bot. A list of network protocols, used to gain unauthorized access to a device and to receive instructions from common and control (C&C) server, is provided too.

Nanotechnology provides new solutions for numerous applications that have a significant effect on almost every aspect of our community including health monitoring, smart cities, military, agriculture, and industry. The interconnection of nanoscale devices with existing communication networks over the Internet defines a novel networking paradigm called the Internet of Nano-Things (IoNT). The IoNT involves a large number of nanosensors that used to provide more precise and detailed information about a particular object to enable a better understanding of object behaviour. In this paper, we investigate the challenges and opportunities of the IoNT system in various applications. An overview of the IoNT is first introduced. This is followed by a discussion of the network architecture of the IoNT and various applications that benefit from integrating IoT with nanotechnology. In the end, since security is considered to be one of the main issues of the IoNT system, we provide an in-depth discussion on security goals, attack vectors and security challenges of the IoNT system.

Thanks to its decentralized structure and immutability, blockchain technology has the potential to address relevant security and privacy challenges in the Internet of Things (IoT). In particular, by hosting and executing smart contracts, blockchain allows secure, flexible, and traceable message communication between IoT devices. The unique characteristics of IoT systems, such as heterogeneity and pervasiveness, however, pose challenges in designing smart contracts for such systems. In this paper, we study these challenges and propose a design approach for smart contracts used in IoT systems. The main goal of our design model is to enhance the development of IoT smart contracts based on the inherent pervasive attributes of IoT systems. In particular, the design model allows the smart contracts to encapsulate functionalities such as contractlevel communication between IoT devices, access to data-sources within contracts, and interoperability of heterogeneous IoT smart contracts. The essence of our approach is structuring the design of IoT smart contracts as self-contained software services, inspired by the microservice architecture model. The flexibility, scalability and modularity of this model make it an efficient approach for developing pervasive IoT smart contracts.

We provide the first solution to an important question, "how a physical-layer RFID authentication method can defend against signal replay attacks". It was believed that if the attacker has a device that can replay the exact same reply signal of a legitimate tag, any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes.

This paper presents a novel research model of identity and the use of this model to answer some interesting research questions. Information travels in the cyber world, not only bringing us convenience and prosperity but also jeopardy. Protecting this information has been a commonly discussed issue in recent years. One type of this information is Personally Identifiable Information (PII), often used to perform personal authentication. People often give PIIs to organizations, e.g., when applying for a new job or filling out a new application on a website. While the use of such PII might be necessary for authentication, giving PII increases the risk of its exposure to criminals. We introduce two innovative approaches based on our model of identity to help evaluate and find an optimal set of PIIs that satisfy authentication purposes but minimize risk of exposure. Our model paves the way for more informed selection of PIIs by organizations that collect them as well as by users who offer PIIs to these organizations.

Security is a key concern in Internet of Things (IoT) designs. In a heterogeneous and complex environment, service providers and service requesters must trust each other. On-off attack is a sophisticated trust threat in which a malicious device can perform good and bad services randomly to avoid being rated as a low trust node. Some countermeasures demands prior level of trust knowing and time to classify a node behavior. In this paper, we introduce a Smart Middleware that automatically assesses the IoT resources trust, evaluating service providers attributes to protect against On-off attacks.

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 one of the emerging technologies that has seized the attention of researchers, the reason behind that was the IoT expected to be applied in our daily life in the near future and human will be wholly dependent on this technology for comfort and easy life style. Internet of things is the interconnection of internet enabled things or devices to connect with each other and to humans in order to achieve some goals or the ability of everyday objects to connect to the Internet and to send and receive data. However, the Internet of Things (IoT) raises significant challenges that could stand in the way of realizing its potential benefits. This paper discusses access control area as one of the most crucial aspect of security and privacy in IoT and proposing a new way of access control that would decide who is allowed to access what and who is not to the IoT subjects and sensors.

The Internet of Things (IoT) provides transparent and seamless incorporation of heterogeneous and different end systems. It has been widely used in many applications such as smart homes. However, people may resist the IOT as long as there is no public confidence that it will not cause any serious threats to their privacy. Effective secure key management for things authentication is the prerequisite of security operations. In this paper, we present an interactive key management protocol and a non-interactive key management protocol to minimize the communication cost of the things. The security analysis show that the proposed schemes are resilient to various types of attacks.

Voice interfaces are increasingly becoming integrated into a variety of Internet of Things (IoT) devices. Such systems can dramatically simplify interactions between users and devices with limited displays. Unfortunately voice interfaces also create new opportunities for exploitation. Specifically any sound-emitting device within range of the system implementing the voice interface (e.g., a smart television, an Internet-connected appliance, etc) can potentially cause these systems to perform operations against the desires of their owners (e.g., unlock doors, make unauthorized purchases, etc). We address this problem by developing a technique to recognize fundamental differences in audio created by humans and electronic speakers. We identify sub-bass over-excitation, or the presence of significant low frequency signals that are outside of the range of human voices but inherent to the design of modern speakers, as a strong differentiator between these two sources. After identifying this phenomenon, we demonstrate its use in preventing adversarial requests, replayed audio, and hidden commands with a 100%/1.72% TPR/FPR in quiet environments. In so doing, we demonstrate that commands injected via nearby audio devices can be effectively removed by voice interfaces.

Access control in the Internet of Things (IoT) often depends on a situation — for example, "the user is at home” — that can only be tracked using multiple devices. In contrast to the (well-studied) smartphone frameworks, enforcement of situational constraints in the IoT poses new challenges because access control is fundamentally decentralized. It takes place in multiple independent frameworks, subjects are often external to the enforcement system, and situation tracking requires cross-framework interaction and permissioning. Existing IoT frameworks entangle access-control enforcement and situation tracking. This results in overprivileged, redundant, inconsistent, and inflexible implementations. We design and implement a new approach to IoT access control. Our key innovation is to introduce "environmental situation oracles” (ESOs) as first-class objects in the IoT ecosystem. An ESO encapsulates the implementation of how a situation is sensed, inferred, or actuated. IoT access-control frameworks can use ESOs to enforce situational constraints, but ESOs and frameworks remain oblivious to each other's implementation details. A single ESO can be used by multiple access-control frameworks across the ecosystem. This reduces inefficiency, supports consistent enforcement of common policies, and — because ESOs encapsulate sensitive device-access rights — reduces overprivileging. ESOs can be deployed at any layer of the IoT software stack where access control is applied. We implemented prototype ESOs for the IoT resource layer, based on the IoTivity framework, and for the IoT Web services, based on the Passport middleware.

Secure group-oriented communication is crucial to a wide range of applications in Internet of Things (IoT). Security problems related to group-oriented communications in IoT-based applications placed in a privacy-sensitive environment have become a major concern along with the development of the technology. Unfortunately, many IoT devices are designed to be portable and light-weight; thus, their functionalities, including security modules, are heavily constrained by the limited energy resources (e.g., battery capacity). To address these problems, we propose a group key management scheme based on a novel physically unclonable function (PUF) design: multistage interconnected PUF (MIPUF) to secure group communications in an energy-constrained environment. Our design is capable of performing key management tasks such as key distribution, key storage and rekeying securely and efficiently. We show that our design is secure against multiple attack methods and our experimental results show that our design saves 47.33% of energy globally comparing to state-of-the-art Elliptic-curve cryptography (ECC)-based key management scheme on average.

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.

In this paper, we present two web-based attacks against local IoT devices that any malicious web page or third-party script can perform, even when the devices are behind NATs. In our attack scenario, a victim visits the attacker's website, which contains a malicious script that communicates with IoT devices on the local network that have open HTTP servers. We show how the malicious script can circumvent the same-origin policy by exploiting error messages on the HTML5 MediaError interface or by carrying out DNS rebinding attacks. We demonstrate that the attacker can gather sensitive information from the devices (e.g., unique device identifiers and precise geolocation), track and profile the owners to serve ads, or control the devices by playing arbitrary videos and rebooting. We propose potential countermeasures to our attacks that users, browsers, DNS providers, and IoT vendors can implement.

Blockchain technology has been increasingly used for decentralizing cloud-based Internet of Things (IoT) architectures to address some limitations faced by centralized systems. While many existing efforts are successful in leveraging blockchain for decentralization with multiple servers (full nodes) to handle faulty nodes, an important issue has arisen that external clients (also called lightweight clients) have to rely on a relay node to communicate with the full nodes in the blockchain. Compromization of such relay nodes may result in a security breach and even a blockage of IoT sensors from the network. We propose censorship resistant decentralized IoT management systems, which include a "diffusion" function to deliver all messages from sensors to all full nodes and an augmented consensus protocol to check data loss, replicate processing outcome, and facilitate opportunistic outcome delivery. We also leverage the cryptographic tool of aggregate signature to reduce the complexity of communication and signature verification.