Biblio

With the vision of building "A Smart World", Internet of Things (IoT) plays a crucial role where users, computing systems and objects with sensing and actuating capabilities cooperate with unparalleled convenience. Among many applications of IoT, healthcare is the most emerging in today's scenario, as new technological advancement creates opportunity for early detection of illnesses, quick decision generation and even aftercare monitoring. Nowadays, it has become a reality for many patients to be monitored remotely, overcoming traditional logistical obstacles. However, these e-health applications increase the concerns of security, privacy, and integrity of medical data. For secured transmission in IoT healthcare, data that has been gathered from sensors in a patient's body area network needs to be sent to the end user and might need to be aggregated, visualized and/or evaluated before being presented. Here, trust is critical. Therefore, an end-to-end trustworthy system architecture can guarantee the reliable transmission of a patient's data and confirms the success of IoT Healthcare application.

The need to process the verity, volume and velocity of data generated by today's Internet of Things (IoT) devices has pushed both academia and the industry to investigate new architectural alternatives to support the new challenges. As a result, Edge Computing (EC) has emerged to address these issues, by placing part of the cloud resources (e.g., computation, storage, logic) closer to the edge of the network, which allows faster and context dependent data analysis and storage. However, as EC infrastructures grow, different providers who do not necessarily trust each other need to collaborate in order serve different IoT devices. In this context, EC infrastructures, IoT devices and the data transiting the network all need to be subject to identity and provenance checks, in order to increase trust and accountability. Each device/data in the network needs to be identified and the provenance of its actions needs to be tracked. In this paper, we propose a blockchain container based architecture that implements the W3C-PROV Data Model, to track identities and provenance of all orchestration decisions of a business network. This architecture provides new forms of interaction between the different stakeholders, which supports trustworthy transactions and leads to a new decentralized interaction model for IoT based applications.

In the distributed Internet of Things (IoT) architecture, sensors collect data from vehicles, home appliances and office equipment and other environments. Various objects contain the sensor which process data, cooperate and exchange information with other embedded devices and end users in a distributed network. It is important to provide end-to-end communication security and an authentication system to guarantee the security and reliability of the data in such a distributed system. Two-factor authentication is a solution to improve the security level of password-based authentication processes and immunized the system against many attacks. At the same time, the computational and storage overhead of an authentication method also needs to be considered in IoT scenarios. For this reason, many cryptographic schemes are designed especially for the IoT; however, we observe a lack of laboratory hardware test beds and modules, and universal authentication hardware modules. This paper proposes a design and analysis for a hardware module in the IoT which allows the use of two-factor authentication based on smart cards, while taking into consideration the limited processing power and energy reserves of nodes, as well as designing the system with scalability in mind.

Cyber-Physical Systems (CPS) and Internet of Things (IoT) are emerging technologies, which makes the remote sensing and control across heterogeneous network a reality, and has good prospects in industrial applications. Due to the resource constrained feature of CPS devices, the design of security and efficiency balanced authentication scheme for CPS/IoT devices becomes a big challenge in CPS/IoT applications. This paper presents a two-factor authentication with key agreement scheme for CPS/IoT applications. The proposed mechanism integrates IMSI identifier and identity-based remote mutual authentication scheme on BAN logic designs. It supports flawless two-factor and mutual authentication of participants and agreement of session keys for user, device and gateway server. The proposed mechanism also provide user anonymity, it can be adopt in critical applications. Besides, it does not require modifying the software of clients; thus, it is highly flexibly. We believe the proposed mechanism is usable for CPS/IoT applications.

Recent advances in pervasive computing have caused a rapid growth of the Smart Home market, where a number of otherwise mundane pieces of technology are capable of connecting to the Internet and interacting with other similar devices. However, with the lack of a commonly adopted set of guidelines, several IT companies are producing smart devices with their own proprietary standards, leading to highly heterogeneous Smart Home systems in which the interoperability of the present elements is not always implemented in the most straightforward manner. As such, understanding the cyber risk of these cyber-physical systems beyond the individual devices has become an almost intractable problem. This paper tackles this issue by introducing a Smart Home reference architecture which facilitates security analysis. Being composed by three viewpoints, it gives a high-level description of the various functions and components needed in a domestic IoT device and network. Furthermore, this document demonstrates how the architecture can be used to determine the various attack surfaces of a home automation system from which its key vulnerabilities can be determined.

With the pervasiveness of the Internet of Things (IoT) and the rapid progress of wireless communications, Wireless Body Area Networks (WBANs) have attracted significant interest from the research community in recent years. As a promising networking paradigm, it is adopted to improve the healthcare services and create a highly reliable ubiquitous healthcare system. However, the flourish of WBANs still faces many challenges related to security and privacy preserving. In such pervasive environment where the context conditions dynamically and frequently change, context-aware solutions are needed to satisfy the users' changing needs. Therefore, it is essential to design an adaptive access control scheme that can simultaneously authorize and authenticate users while considering the dynamic context changes. In this paper, we propose a context-aware access control and anonymous authentication approach based on a secure and efficient Hybrid Certificateless Signcryption (H-CLSC) scheme. The proposed scheme combines the merits of Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) and Identity-Based Broadcast Signcryption (IBBSC) in order to satisfy the security requirements and provide an adaptive contextual privacy. From a security perspective, it achieves confidentiality, integrity, anonymity, context-aware privacy, public verifiability, and ciphertext authenticity. Moreover, the key escrow and public key certificate problems are solved through this mechanism. Performance analysis demonstrates the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of functional security, storage, communication and computational cost.

In spite of being a promising technology which will make our lives a lot easier we cannot be oblivious to the fact IoT is not safe from online threat and attacks. Thus, along with the growth of IoT we also need to work on its aspects. Taking into account the limited resources that these devices have it is important that the security mechanisms should also be less complex and do not hinder the actual functionality of the device. In this paper, we propose an ECC based lightweight authentication for IoT devices which deploy RFID tags at the physical layer. ECC is a very efficient public key cryptography mechanism as it provides privacy and security with lesser computation overhead. We also present a security and performance analysis to verify the strength of our proposed approach.

Airports are at the forefront of technological innovation, mainly due to the fact that the number of air travel passengers is exponentially increasing every year. As a result, airports enhance infrastructure's intelligence and evolve as smart facilities to support growth, by offering a pleasurable travel experience, which plays a vital role in increasing revenue of aviation sector. New challenges are coming up, which aviation has to deal and adapt, such as the integration of Industrial IoT in airport facilities and the increased use of Bring Your Own Device from travelers and employees. Cybersecurity is becoming a key enabler for safety, which is paramount in the aviation context. Smart airports strive to provide optimal services in a reliable and sustainable manner, by working around the domains of growth, efficiency, safety andsecurity. This paper researches the implementation rate of cybersecurity measures and best practices to improve airports cyber resilience. With the aim to enhance operational practices anddevelop robust cybersecurity governance in smart airports, we analyze security gaps in different areas including technical, organizational practices and policies.

User testing is often used to inform the development of user interfaces (UIs). But what if an interface needs to be developed for a system that does not yet exist? In that case, existing datasets can provide valuable input for UI development. We apply a data-driven approach to the development of a privacy-setting interface for Internet-of-Things (IoT) devices. Applying machine learning techniques to an existing dataset of users' sharing preferences in IoT scenarios, we develop a set of "smart" default profiles. Our resulting interface asks users to choose among these profiles, which capture their preferences with an accuracy of 82%—a 14% improvement over a naive default setting and a 12% improvement over a single smart default setting for all users.

The Internet of Things provides household device users with an ability to connect and manage numerous devices over a common platform. However, the sheer number of possible privacy settings creates issues such as choice overload. This article outlines a data-driven approach to understand how users make privacy decisions in household IoT scenarios. We demonstrate that users are not just influenced by the specifics of the IoT scenario, but also by aspects immaterial to the decision, such as the default setting and its framing.

In order to cater the growing spectrum demands of large scale future 5G Internet of Things (IoT) applications, Dynamic Spectrum Access (DSA) based networks are being proposed as a high-throughput and cost-effective solution. However the lack of understanding of DSA paradigm's inherent security vulnerabilities on IoT networks might become a roadblock towards realizing such spectrum aware 5G vision. In this paper, we make an attempt to understand how such inherent DSA vulnerabilities in particular Spectrum Sensing Data Falsification (SSDF) attacks can be exploited by collaborative group of selfish adversaries and how that can impact the performance of spectrum aware IoT applications. We design a utility based selfish adversarial model mimicking collaborative SSDF attack in a cooperative spectrum sensing scenario where IoT networks use dedicated environmental sensing capability (ESC) for spectrum availability estimation. We model the interactions between the IoT system and collaborative selfish adversaries using a leader-follower game and investigate the existence of equilibrium. Using simulation results, we show the nature of adversarial and system utility components against system variables. We also explore Pareto-optimal adversarial strategy design that maximizes the attacker utility for varied system strategy spaces.

Active Noise Cancellation (ANC) is a classical area where noise in the environment is canceled by producing anti-noise signals near the human ears (e.g., in Bose's noise cancellation headphones). This paper brings IoT to active noise cancellation by combining wireless communication with acoustics. The core idea is to place an IoT device in the environment that listens to ambient sounds and forwards the sound over its wireless radio. Since wireless signals travel much faster than sound, our ear-device receives the sound in advance of its actual arrival. This serves as a glimpse into the future, that we call lookahead, and proves crucial for real-time noise cancellation, especially for unpredictable, wide-band sounds like music and speech. Using custom IoT hardware, as well as lookahead-aware cancellation algorithms, we demonstrate MUTE, a fully functional noise cancellation prototype that outperforms Bose's latest ANC headphone. Importantly, our design does not need to block the ear - the ear canal remains open, making it comfortable (and healthier) for continuous use.

As the Industrial Internet of Things (IIot) becomes more prevalent in critical application domains, ensuring security and resilience in the face of cyber-attacks is becoming an issue of paramount importance. Cyber-attacks against critical infrastructures, for example, against smart water-distribution and transportation systems, pose serious threats to public health and safety. Owing to the severity of these threats, a variety of security techniques are available. However, no single technique can address the whole spectrum of cyber-attacks that may be launched by a determined and resourceful attacker. In light of this, we consider a multi-pronged approach for designing secure and resilient IIoT systems, which integrates redundancy, diversity, and hardening techniques. We introduce a framework for quantifying cyber-security risks and optimizing IIoT design by determining security investments in redundancy, diversity, and hardening. To demonstrate the applicability of our framework, we present a case study in water-distribution systems. Our numerical evaluation shows that integrating redundancy, diversity, and hardening can lead to reduced security risk at the same cost.

Device-to-device communication is widely used for mobile devices and Internet of Things. Authentication and key agreement are critical to build a secure channel between two devices. However, existing approaches often rely on a pre-built fingerprint database and suffer from low key generation rate. We present GeneWave, a fast device authentication and key agreement protocol for commodity mobile devices. GeneWave first achieves bidirectional initial authentication based on the physical response interval between two devices. To keep the accuracy of interval estimation, we eliminate time uncertainty on commodity devices through fast signal detection and redundancy time cancellation. Then, we derive the initial acoustic channel response for device authentication. We design a novel coding scheme for efficient key agreement while ensuring security. Therefore, two devices can authenticate each other and securely agree on a symmetric key. GeneWave requires neither special hardware nor pre-built fingerprint database, and thus it is easyto-use on commercial mobile devices. We implement GeneWave on mobile devices (i.e., Nexus 5X and Nexus 6P) and evaluate its performance through extensive experiments. Experimental results show that GeneWave efficiently accomplish secure key agreement on commodity smartphones with a key generation rate 10× faster than the state-of-the-art approach.

ARM devices (mobile phone, IoT devices) are getting more popular in our daily life due to the low power consumption and cost. These devices carry a huge number of user's private information, which attracts attackers' attention and increase the security risk. The operating systems (e.g., Android, Linux) works out many memory data protection strategies on user's private information. However, the monolithic OS may contain security vulnerabilities that are exploited by the attacker to get root or even kernel privilege. Once the kernel privilege is obtained by the attacker, all data protection strategies will be gone and user's private information can be taken away. In this paper, we propose a hardened memory data protection framework called H-Securebox to defeat kernel-level memory data stolen attacks. H-Securebox leverages ARM hardware virtualization technique to protect the data on the memory with hypervisor privilege. We designed three types H-Securebox for programing developers to use. Although the attacker may have kernel privilege, she can not touch private data inside H-Securebox, since hypervisor privilege is higher than kernel privilege. With the implementation of H-Securebox system assisting by a tiny hypervisor on Raspberry Pi2 development board, we measure the performance overhead of our system and do the security evaluations. The results positively show that the overhead is negligible and the malicious application with root or kernel privilege can not access the private data protected by our system.

With ubiquitous connectivity no longer a dream, the need for efficient communications between smart objects in Internet of Things has become more demanding than ever before. In this context, efficient yet low cost modeling tools, capable of considering the impactful parameters are essential in provisioning such systems. ETX (Expected Transmission count), used in RPL (Routing Protocol for Low power lossy networks), turns to be one of the most widely used routing metrics, so it is considered by IETF for IPv6 routing in low-power wireless sensor networks and to infer many network layer characteristics like packet loss rate. As more and more wireless sensor networks being deployed with 6LoWPAN, having a model to easily obtain network parameters gains importance. This work aims to provide such a tool in the form of a modular analytic model for linear topology of 6LoWPANs which are mostly seen in monitoring systems of linear structures like pipeline, bridges and highways. The proposed model is evaluated in obtaining the ETX metric and packet loss rate in a typical IoT network.

6LoWPAN is a widely used protocol for communication over IPV6 Low-power Wireless Personal Area Networks. Unfortunately, the 6LoWPAN packet fragmentation mechanism possesses vulnerabilities that adversaries can exploit to perform network attacks. Lack of fragment authentication, payload integrity verification, and sender IP address validation lead to fabrication, duplication, and impersonation attacks. Moreover, adversaries can abuse the poor reassembly buffer management technique of the 6LoWPAN layer to perform buffer exhaustion and selective forwarding attacks. In this paper, we propose SecuPAN - a security scheme for mitigating fragmentation-based network attacks in 6LoWPAN networks and devices. We propose a Message Authentication Code based per-fragment integrity and authenticity verification scheme to defend against fabrication and duplication attacks. We also present a mechanism for computing datagram-tag and IPv6 address cryptographically to mitigate impersonation attacks. Additionally, our reputation-based buffer management scheme protects 6LoWPAN devices from buffer reservation attacks. We provide an extensive security analysis of SecuPAN to demonstrate that SecuPAN is secure against strong adversarial scenarios. We also implemented a prototype of SecuPAN on Contiki enabled IoT devices and provided a performance analysis of our proposed scheme.

Wireless Internet of Things (IoT) devices share several features such as limited energy supply, low computing power, limited memory size, and vulnerable radio communication network. IETF proposed the Constrained Application Protocol (CoAP) for this type of network. This paper presents implementation of CoAP into an embedded IoT device used for smart Energy Storage System (ESS) under microgrid environment. Confirmable message type was adopted to provide reliable communication. Since the frame size of IEEE 802.15.4 physical layer was limited to 127 bytes, the header of 6LoWPAN and UDP was compressed to reduce fragmentation and reassembly overhead. Performance of the communication service was tested by measuring round trip time between two end nodes of developed system.

Examining implementations of the 6LoWPAN Internet Standard in major embedded operating systems, we observe that they do not fully interoperate. We find this is due to some inherent design flaws in 6LoWPAN. We propose and demonstrate four principles that can be used to structure protocols for low power devices that encourage interoperability between diverse implementations.

Internet of things (IoT) is the smart network which connects smart objects over the Internet. The Internet is untrusted and unreliable network and thus IoT network is vulnerable to different kind of attacks. Conventional encryption and authentication techniques sometimes fail on IoT based network and intrusion may succeed to destroy the network. So, it is necessary to design intrusion detection system for such network. In our paper, we detect routing attacks such as sinkhole and selective forwarding. We have also tried to prevent our network from these attacks. We designed detection and prevention algorithm, i.e., KMA (Key Match Algorithm) and CBA (Cluster- Based Algorithm) in MatLab simulation environment. We gave two intrusion detection mechanisms and compared their results as well. True positive intrusion detection rate for our work is between 50% to 80% with KMA and 76% to 96% with CBA algorithm.

A significant segment of the Internet of Things (IoT) is the resource constrained Low Power and Lossy Networks (LLNs). The communication protocol used in LLNs is 6LOWPAN (IPv6 over Low-power Wireless Personal Area Network) which makes use of RPL (IPv6 Routing Protocol over Low power and Lossy network) as its routing protocol. In recent times, several security breaches in IoT networks occurred by targeting routers to instigate various DDoS (Distributed Denial of Service) attacks. Hence, routing security has become an important problem in securing the IoT environment. Though RPL meets all the routing requirements of LLNs, it is important to perform a holistic security assessment of RPL as it is susceptible to many security attacks. An important attribute of RPL is its rank property. The rank property defines the placement of sensor nodes in the RPL DODAG (Destination Oriented Directed Acyclic Graphs) based on an Objective Function. Examples of Objective Functions include Expected Transmission Count, Packet Delivery Rate etc. Rank property assists in routing path optimization, reducing control overhead and maintaining a loop free topology through rank based data path validation. In this paper, we investigate the vulnerabilities of the rank property of RPL by constructing an Attack Graph. For the construction of the Attack Graph we analyzed all the possible threats associated with rank property. Through our investigation we found that violation of protocols related to rank property results in several RPL attacks causing topological sub-optimization, topological isolation, resource consumption and traffic disruption. Routing security essentially comprises mechanisms to ensure correct implementation of the routing protocol. In this paper, we also present some observations which can be used to devise mechanisms to prevent the exploitation of the vulnerabilities of the rank property.

Internet-of-Things (IoT) is a resource-constrained network with machines low on power, processing and memory capabilities. Resource constraints in IoT impact the adoption of protocols for design and validation of unique identity (ID) for every machine. Malicious machines spoof ID to pose as administrative machines and program their neighbour systems in the network with malware. The cycle of ID spoofing and infecting the IP-enabled devices with malware creates an entire network popularly termed as the Botnet. In this paper, we study 6LoWPAN and ZigBee for DDoS and ID spoofing vulnerabilities. We propose a design for generation and validation of ID on such systems called Pseudo Random Identity Generator (PRIG). We compare the performance of PRIG-adapted 6LoWPAN with 6LoWPAN in a simulated personal area network (PAN) model under DDoS stress and demonstrate a 93% reduction in ID validation time as well as an improvement of 67% in overall throughput.

The software defined networking framework facilitates flexible and reliable internet of things networks by moving the network intelligence to a centralized location while enabling low power wireless network in the edge. In this paper, we present SD-WSN6Lo, a novel software-defined wireless management solution for 6LoWPAN networks that aims to reduce the management complexity in WSN's. As an example of the technique, a simulation of controlling the power consumption of sensor nodes is presented. The results demonstrate improved energy consumption of approximately 15% on average per node compared to the baseline condition.

Internet of things has become a subject of interest across a different industry domain. It includes 6LoWPAN (Low-Power Wireless Personal Area Network) which is used for a variety of application including home automation, sensor networks, manufacturing and industry application etc. However, gathering such a huge amount of data from such a different domain causes a problem of traffic congestion, high reliability, high energy efficiency etc. In order to address such problems, content based routing (CBR) technique is proposed, where routing paths are decided according to the type of content. By routing the correlated data to hop nodes for processing, a higher data aggregation ratio can be obtained, which in turns reducing the traffic congestion and minimizes the energy consumption. CBR is implemented on top of existing RPL (Routing Protocol for Low Power and Lossy network) and implemented in contiki operating system using cooja simulator. The analysis are carried out on the basis average power consumption, packet delivery ratio etc.

Robotics and the Internet of Things (IoT) are enveloping our society at an exponential rate due to lessening costs and better availability of hardware and software. Additionally, Cloud Robotics and Robot Operating System (ROS) can offset onboard processing power. However, strong and fundamental security practices have not been applied to fully protect these systems., partially negating the benefits of IoT. Researchers are therefore tasked with finding ways of securing communications and systems. Since security and convenience are oftentimes at odds, securing many heterogeneous components without compromising performance can be daunting. Protecting systems from attacks and ensuring that connections and instructions are from approved devices, all while maintaining the performance is imperative. This paper focuses on the development of security best practices and a mesh framework with an open-source, multipoint-to-multipoint virtual private network (VPN) that can tie Linux, Windows, IOS., and Android devices into one secure fabric, with heterogeneous mobile robotic platforms running ROSPY in a secure cloud robotics infrastructure.