Biblio
Permissioned Blockchain (PBC) has become a prevalent data structure to ensure that the records are immutable and secure. However, PBC still has significant challenges before it can be realized in different applications. One of such challenges is the overhead of the communication which is required to execute the Byzantine Agreement (BA) protocol that is needed for consensus building. As such, it may not be feasible to implement PBC for resource constrained environments such as Internet-of-Things (IoT). In this paper, we assess the communication overhead of running BA in an IoT environment that consists of wireless nodes (e.g., Raspberry PIs) with meshing capabilities. As the the packet loss ratio is significant and makes BA unfeasible to scale, we propose a network coding based approach that will reduce the packet overhead and minimize the consensus completion time of the BA. Specifically, various network coding approaches are designed as a replacement to TCP protocol which relies on unicasting and acknowledgements. The evaluation on a network of Raspberry PIs demonstrates that our approach can significantly improve scalability making BA feasible for medium size IoT networks.
The rapid growth of population and industrialization has given rise to the way for the use of technologies like the Internet of Things (IoT). Innovations in Information and Communication Technologies (ICT) carries with it many challenges to our privacy's expectations and security. In Smart environments there are uses of security devices and smart appliances, sensors and energy meters. New requirements in security and privacy are driven by the massive growth of devices numbers that are connected to IoT which increases concerns in security and privacy. The most ubiquitous threats to the security of the smart grids (SG) ascended from infrastructural physical damages, destroying data, malwares, DoS, and intrusions. Intrusion detection comprehends illegitimate access to information and attacks which creates physical disruption in the availability of servers. This work proposes an intrusion detection system using data mining techniques for intrusion detection in smart grid environment. The results showed that the proposed random forest method with a total classification accuracy of 98.94 %, F-measure of 0.989, area under the ROC curve (AUC) of 0.999, and kappa value of 0.9865 outperforms over other classification methods. In addition, the feasibility of our method has been successfully demonstrated by comparing other classification techniques such as ANN, k-NN, SVM and Rotation Forest.
A privately owned smart device connected to a corporate network using a USB connection creates a potential channel for malware infection and its subsequent spread. For example, air-gapped (a.k.a. isolated) systems are considered to be the most secure and safest places for storing critical datasets. However, unlike network communications, USB connection streams have no authentication and filtering. Consequently, intentional or unintentional piggybacking of a malware infected USB storage or a mobile device through the air-gap is sufficient to spread infection into such systems. Our findings show that the contact rate has an exceptional impact on malware spread and destabilizing free malware equilibrium. This work proposes a USB authentication and delegation protocol based on radiofrequency identification (RFID) in order to stabilize the free malware equilibrium in air-gapped networks. The proposed protocol is modelled using Coloured Petri nets (CPN) and the model is verified and validated through CPN tools.
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 concept of Internet of Things (IoT) has received considerable attention and development in recent years. There have been significant studies on access control models for IoT in academia, while companies have already deployed several cloud-enabled IoT platforms. However, there is no consensus on a formal access control model for cloud-enabled IoT. The access-control oriented (ACO) architecture was recently proposed for cloud-enabled IoT, with virtual objects (VOs) and cloud services in the middle layers. Building upon ACO, operational and administrative access control models have been published for virtual object communication in cloud-enabled IoT illustrated by a use case of sensing speeding cars as a running example. In this paper, we study AWS IoT as a major commercial cloud-IoT platform and investigate its suitability for implementing the afore-mentioned academic models of ACO and VO communication control. While AWS IoT has a notion of digital shadows closely analogous to VOs, it lacks explicit capability for VO communication and thereby for VO communication control. Thus there is a significant mismatch between AWS IoT and these academic models. The principal contribution of this paper is to reconcile this mismatch by showing how to use the mechanisms of AWS IoT to effectively implement VO communication models. To this end, we develop an access control model for virtual objects (shadows) communication in AWS IoT called AWS-IoT-ACMVO. We develop a proof-of-concept implementation of the speeding cars use case in AWS IoT under guidance of this model, and provide selected performance measurements. We conclude with a discussion of possible alternate implementations of this use case in AWS IoT.
In this paper, the problem of network connectivity is studied for an adversarial Internet of Battlefield Things (IoBT) system in which an attacker aims at disrupting the connectivity of the network by choosing to compromise one of the IoBT nodes at each time epoch. To counter such attacks, an IoBT defender attempts to reestablish the IoBT connectivity by either deploying new IoBT nodes or by changing the roles of existing nodes. This problem is formulated as a dynamic multistage Stackelberg connectivity game that extends classical connectivity games and that explicitly takes into account the characteristics and requirements of the IoBT network. In particular, the defender's payoff captures the IoBT latency as well as the sum of weights of disconnected nodes at each stage of the game. Due to the dependence of the attacker's and defender's actions at each stage of the game on the network state, the feedback Stackelberg solution [feedback Stackelberg equilibrium (FSE)] is used to solve the IoBT connectivity game. Then, sufficient conditions under which the IoBT system will remain connected, when the FSE solution is used, are determined analytically. Numerical results show that the expected number of disconnected sensors, when the FSE solution is used, decreases up to 46% compared to a baseline scenario in which a Stackelberg game with no feedback is used, and up to 43% compared to a baseline equal probability policy.
The exponential increase of Internet of Things (IoT) devices have resulted in a range of new and unanticipated vulnerabilities associated with their use. IoT devices from smart homes to smart enterprises can easily be compromised. One of the major problems associated with the IoT is maintaining security; the vulnerable nature of IoT devices poses a challenge to many aspects of security, including security testing and analysis. It is trivial to perform the security analysis for IoT devices to understand the loop holes and very nature of the devices itself. Given these issues, there has been less emphasis on security testing and analysis of the IoT. In this paper, we show our preliminary efforts in the area of security analysis for IoT devices and introduce a security IoT testbed for performing security analysis. We also discuss the necessary design, requirements and the architecture to support our security analysis conducted via the proposed testbed.
Consent is a key measure for privacy protection and needs to be `meaningful' to give people informational power. It is increasingly important that individuals are provided with real choices and are empowered to negotiate for meaningful consent. Meaningful consent is an important area for consideration in IoT systems since privacy is a significant factor impacting on adoption of IoT. Obtaining meaningful consent is becoming increasingly challenging in IoT environments. It is proposed that an ``apparency, pragmatic/semantic transparency model'' adopted for data management could make consent more meaningful, that is, visible, controllable and understandable. The model has illustrated the why and what issues regarding data management for potential meaningful consent [1]. In this paper, we focus on the `how' issue, i.e. how to implement the model in IoT systems. We discuss apparency by focusing on the interactions and data actions in the IoT system; pragmatic transparency by centring on the privacy risks, threats of data actions; and semantic transparency by focusing on the terms and language used by individuals and the experts. We believe that our discussion would elicit more research on the apparency model' in IoT for meaningful consent.
This paper outlines the IoT Databox model as a means of making the Internet of Things (IoT) accountable to individuals. Accountability is a key to building consumer trust and mandated in data protection legislation. We briefly outline the `external' data subject accountability requirement specified in actual legislation in Europe and proposed legislation in the US, and how meeting requirement this turns on surfacing the invisible actions and interactions of connected devices and the social arrangements in which they are embedded. The IoT Databox model is proposed as an in principle means of enabling accountability and providing individuals with the mechanisms needed to build trust in the IoT.
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.
The Internet of Things (IoT) revolution has brought millions of small, low-cost, connected devices into our homes, cities, infrastructure, and more. However, these devices are often plagued by security vulnerabilities that pose threats to user privacy or can threaten the Internet architecture as a whole. Home networks can be particularly vulnerable to these threats as they typically have no network administrator and often contain unpatched or otherwise vulnerable devices. In this paper, we argue that the unique security challenges of home networks require a new network-layer architecture to both protect against external threats and mitigate attacks from compromised devices. We present initial findings based on traffic analysis from a small-scale IoT testbed toward identifying predictable patterns in IoT traffic that may allow construction of a policy-based framework to restrict malicious traffic. Based on our observations, we discuss key features for the design of this architecture to promote future developments in network-layer security in smart home networks.
In the age of IOT, as more and more devices are getting connected to the internet through wireless networks, a better security infrastructure is required to protect these devices from massive attacks. For long SSIDs and passwords have been used to authenticate and secure Wi-Fi networks. But the SSID and password combination is vulnerable to security exploits like phishing and brute-forcing. In this paper, a completely automated Wi-Fi authentication system is proposed, that generates Time-based One-Time Passwords (TOTP) to secure Wi-Fi networks. This approach aims to black box the process of connecting to a Wi-Fi network for the user and the process of generating periodic secure passwords for the network without human intervention.
The Internet of Vehicles (IoV) is a complex and dynamic mobile network system that enables information sharing between vehicles, their surrounding sensors, and clouds. While IoV opens new opportunities in various applications and services to provide safety on the road, it introduces new challenges in the field of digital forensics investigations. The existing tools and procedures of digital forensics cannot meet the highly distributed, decentralized, dynamic, and mobile infrastructures of the IoV. Forensic investigators will face challenges while identifying necessary pieces of evidence from the IoV environment, and collecting and analyzing the evidence. In this article, we propose TrustIoV - a digital forensic framework for the IoV systems that provides mechanisms to collect and store trustworthy evidence from the distributed infrastructure. Trust-IoV maintains a secure provenance of the evidence to ensure the integrity of the stored evidence and allows investigators to verify the integrity of the evidence during an investigation. Our experimental results on a simulated environment suggest that Trust-IoV can operate with minimal overhead while ensuring the trustworthiness of evidence in a strong adversarial scenario.
There are billions of Internet of things (IoT) devices connecting to the Internet and the number is increasing. As a still ongoing technology, IoT can be used in different fields, such as agriculture, healthcare, manufacturing, energy, retailing and logistics. IoT has been changing our world and the way we live and think. However, IoT has no uniform architecture and there are different kinds of attacks on the different layers of IoT, such as unauthorized access to tags, tag cloning, sybil attack, sinkhole attack, denial of service attack, malicious code injection, and man in middle attack. IoT devices are more vulnerable to attacks because it is simple and some security measures can not be implemented. We analyze the privacy and security challenges in the IoT and survey on the corresponding solutions to enhance the security of IoT architecture and protocol. We should focus more on the security and privacy on IoT and help to promote the development of IoT.
Over the last few decades, accessibility scenarios have undergone a drastic change. Today the way people access information and resources is quite different from the age when internet was not evolved. The evolution of the Internet has made remarkable, epoch-making changes and has become the backbone of smart city. The vision of smart city revolves around seamless connectivity. Constant connectivity can provide uninterrupted services to users such as e-governance, e-banking, e-marketing, e-shopping, e-payment and communication through social media. And to provide uninterrupted services to such applications to citizens is our prime concern. So this paper focuses on smart handoff framework for next generation heterogeneous networks in smart cities to provide all time connectivity to anyone, anyhow and anywhere. To achieve this, three strategies have been proposed for handoff initialization phase-Mobile controlled, user controlled and network controlled handoff initialization. Each strategy considers a different set of parameters. Results show that additional parameters with RSSI and adaptive threshold and hysteresis solve ping-pong and corner effect problems in smart city.
The Internet of Things (IoT) architecture is expected to evolve into a model containing various open systems, integrated environments, and platforms, which can be programmed and can provide secure services on demand. However, not much effort has been devoted towards the security of such an IoT architecture. In this paper, we present an IoT architecture that supports deploying dynamic security policies for IoT services. In this approach, IoT devices, gateways, and data are open and programmable to IoT application developers and service operators. Fine-grained security policies can be programmed and dynamically adjusted according to users' requirements, devices' capabilities and networking environments. The implementation and test results show that new security policies can be created and deployed rapidly and demonstrate the feasibility of the architecture.
Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However, current determination of heart rate through mobile applications suffers from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for selection of PPG signals using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%.
The Internet of Things (IoT) is a design implementation of embedded system design that connects a variety of devices, sensors, and physical objects to a larger connected network (e.g. the Internet) which requires human-to-human or human-to-computer interaction. While the IoT is expected to expand the user's connectivity and everyday convenience, there are serious security considerations that come into account when using the IoT for distributed authentication. Furthermore the incorporation of biometrics to IoT design brings about concerns of cost and implementing a 'user-friendly' design. In this paper, we focus on the use of electrocardiogram (ECG) signals to implement distributed biometrics authentication within an IoT system model. Our observations show that ECG biometrics are highly reliable, more secure, and easier to implement than other biometrics.
In IoT environments, the user may have many devices to connect each other and share the data. Also, the device will not have the powerful computation and storage ability. Many studies have focused on the lightweight authentication between the cloud server and the client in this environment. They can use the cloud server to help sensors or proxies to finish the authentication. But in the client side, how to create the group session key without the cloud capability is the most important issue in IoT environments. The most popular application network of IoT environments is the wireless body area network (WBAN). In WBAN, the proxy usually needs to control and monitor user's health data transmitted from the sensors. In this situation, the group authentication and group session key generation is needed. In this paper, in order to provide an efficient and robust group authentication and group session key generation in the client side of IoT environments, we propose a lightweight authentication scheme with dynamic group members in IoT environments. Our proposed scheme can satisfy the properties including the flexible generation of shared group keys, the dynamic participation, the active revocation, the low communication and computation cost, and no time synchronization problem. Also our scheme can achieve the security requirements including the mutual authentication, the group session key agreement, and prevent all various well-known attacks.
This study stems from the premise that we need to break away from the "reactive" cycle of developing defenses against new DDoS attacks (e.g., amplification) by proactively investigating the potential for new types of DDoS attacks. Our specific focus is on pulsating attacks, a particularly debilitating type that has been hypothesized in the literature. In a pulsating attack, bots coordinate to generate intermittent pulses at target links to significantly reduce the throughput of TCP connections traversing the target. With pulsating attacks, attackers can cause significantly greater damage to legitimate users than traditional link flooding attacks. To date, however, pulsating attacks have been either deemed ineffective or easily defendable for two reasons: (1) they require a central coordinator and can thus be tracked; and (2) they require tight synchronization of pulses, which is difficult even in normal non-congestion scenarios. This paper argues that, in fact, the perceived drawbacks of pulsating attacks are in fact not fundamental. We develop a practical pulsating attack called CICADAS using two key ideas: using both (1) congestion as an implicit signal for decentralized implementation, and (2) a Kalman-filter-based approach to achieve tight synchronization. We validate CICADAS using simulations and wide-area experiments. We also discuss possible countermeasures against this attack.