Biblio

The rapid growth of power Internet of Things devices has led to traditional data security sharing mechanisms that are no longer suitable for attribute and permission management of massive devices. In response to this problem, this article proposes a blockchain-based data security sharing mechanism for the power Internet of Things, which reduces the risk of data leakage through decentralization in the architecture and promotes the integration of multiple information and methods.

In the Internet of Things (IoT), it is feasible to interconnect networks of different devices and all these different devices, such as smartphones, sensor devices, and vehicles, are controlled according to a particular user. These different devices are delivered and accept the information on the network. This thing is to motivate us to do work on IoT and the devices used are sensor nodes. The validation of data delivery completely depends on the checks of count data forwarding in each node. In this research, we propose the Link Hop Value-based Intrusion Detection System (L-IDS) against the blackhole attack in the IoT with the assist of WSN. The sensor nodes are connected to other nodes through the wireless link and exchange data routing, as well as data packets. The LHV value is identified as the attacker's presence by integrating the data delivery in each hop. The LHV is always equivalent to the Actual Value (AV). The RPL routing protocol is used IPv6 to address the concept of routing. The Routing procedure is interrupted by an attacker by creating routing loops. The performance of the proposed L-IDS is compared to the RPL routing security scheme based on existing trust. The proposed L-IDS procedure is validating the presence of the attacker at every source to destination data delivery. and also disables the presence of the attacker in the network. Network performance provides better results in the existence of a security scheme and also fully represents the inoperative presence of black hole attackers in the network. Performance metrics show better results in the presence of expected IDS and improve network reliability.

The Internet of Things (IoT) is enabling smart houses, where multiple users with complex social relationships interact with smart devices. This requires sophisticated access control specification and enforcement models, that are currently lacking. In this paper, we introduce the extended generalized role based access control (EGRBAC) model for smart home IoT. We provide a formal definition for EGRBAC and illustrate its features with a use case. A proof-of-concept demonstration utilizing AWS-IoT Greengrass is discussed in the appendix. EGRBAC is a first step in developing a comprehensive family of access control models for smart home IoT.

Social Internet of Things (SIoT) is an extension of Internet of Things (IoT) that converges with Social networking concepts to create Social networks of interconnected smart objects. This convergence allows the enrichment of the two paradigms, resulting into new ecosystems. While IoT follows two interaction paradigms, human-to-human (H2H) and thing-to-thing (T2T), SIoT adds on human-to-thing (H2T) interactions. SIoT enables smart “Social objects” that intelligently mimic the social behavior of human in the daily life. These social objects are equipped with social functionalities capable of discovering other social objects in the surroundings and establishing social relationships. They crawl through the social network of objects for the sake of searching for services and information of interest. The notion of trust and trustworthiness in social communities formed in SIoT is still new and in an early stage of investigation. In this paper, our contributions are threefold. First, we present the fundamentals of SIoT and trust concepts in SIoT, clarifying the similarities and differences between IoT and SIoT. Second, we categorize the trust management solutions proposed so far in the literature for SIoT over the last six years and provide a comprehensive review. We then perform a comparison of the state of the art trust management schemes devised for SIoT by performing comparative analysis in terms of trust management process. Third, we identify and discuss the challenges and requirements in the emerging new wave of SIoT, and also highlight the challenges in developing trust and evaluating trustworthiness among the interacting social objects.

Today, Internet of Things (IoT) devices mostly operate in enclosed, proprietary environments. To unfold the full potential of IoT applications, a unifying and permissionless environment is crucial. All IoT devices, even unknown to each other, would be able to trade services and assets across various domains. In order to realize those applications, uniquely resolvable identities are essential. However, quantifiable trust in identities and their authentication are not trivially provided in such an environment due to the absence of a trusted authority. This research presents a new identity and trust framework for IoT devices, based on Distributed Ledger Technology (DLT). IoT devices assign identities to themselves, which are managed publicly and decentralized on the DLT's network as Self Sovereign Identities (SSI). In addition to the Identity Management System (IdMS), the framework provides a Web of Trust (WoT) approach to enable automatic trust rating of arbitrary identities. For the framework we used the IOTA Tangle to access and store data, achieving high scalability and low computational overhead. To demonstrate the feasibility of our framework, we provide a proof-of-concept implementation and evaluate the set objectives for real world applicability as well as the vulnerability against common threats in IdMSs and WoTs.

Despite the latest initiatives and research efforts to increase user privacy in digital scenarios, identity-related cybercrimes such as identity theft, wrong identity or user transactions surveillance are growing. In particular, blanket surveillance that might be potentially accomplished by Identity Providers (IdPs) contradicts the data minimization principle laid out in GDPR. Hence, user movements across Service Providers (SPs) might be tracked by malicious IdPs that become a central dominant entity, as well as a single point of failure in terms of privacy and security, putting users at risk when compromised. To cope with this issue, the OLYMPUS H2020 EU project is devising a truly privacy-preserving, yet user-friendly, and distributed identity management system that addresses the data minimization challenge in both online and offline scenarios. Thus, OLYMPUS divides the role of the IdP among various authorities by relying on threshold cryptography, thereby preventing user impersonation and surveillance from malicious or nosy IdPs. This paper overviews the OLYMPUS framework, including requirements considered, the proposed architecture, a series of use cases as well as the privacy analysis from the legal point of view.

The growing prevalence of Internet-of-Things (IoT) technology has led to an increase in the development of heterogeneous smart applications. Smart applications may involve a collaborative participation between IoT devices. Participation of IoT devices for specific application requires a tamper-proof identity to be generated and stored, in order to completely represent the device, as well as to eliminate the possibility of identity spoofing and presence of rogue devices in a network. In this paper, we present a composite Identity-of-Things (IDoT) approach on IoT devices with permissioned blockchain implementation for distributed identity management model. Our proposed approach considers both application and device domains in generating the composite identity. In addition, the use of permissioned blockchain for identity storage and verification allows the identity to be immutable. A simulation has been carried out to demonstrate the application of the proposed identity management model.

Humans are a key part of software development, including customers, designers, coders, testers and end users. In this keynote talk I explain why incorporating human-centric issues into software engineering for next-generation applications is critical. I use several examples from our recent and current work on handling human-centric issues when engineering various `smart living' cloud- and edge-based software systems. This includes using human-centric, domain-specific visual models for non-technical experts to specify and generate data analysis applications; personality impact on aspects of software activities; incorporating end user emotions into software requirements engineering for smart homes; incorporating human usage patterns into emerging edge computing applications; visualising smart city-related data; reporting diverse software usability defects; and human-centric security and privacy requirements for smart living systems. I assess the usefulness of these approaches, highlight some outstanding research challenges, and briefly discuss our current work on new human-centric approaches to software engineering for smart living applications.

Internet of Things (IoT) systems are becoming widely used, which makes them to be a high-value target for both hackers and crackers. From gaining access to sensitive information to using them as bots for complex attacks, the variety of advantages after exploiting different security vulnerabilities makes the security of IoT devices to be one of the most challenging desideratum for cyber security experts. In this paper, we will propose a new IoT system, designed to ensure five data principles: confidentiality, integrity, availability, authentication and authorization. The innovative aspects are both the usage of a web-based communication and a custom dynamic data request structure.

Remote patient monitoring is a system that focuses on patients care and attention with the advent of the Internet of Things (IoT). The technology makes it easier to track distance, but also to diagnose and provide critical attention and service on demand so that billions of people are safer and more safe. Skincare monitoring is one of the growing fields of medical care which requires IoT monitoring, because there is an increasing number of patients, but cures are restricted to the number of available dermatologists. The IoT-based skin monitoring system produces and store volumes of private medical data at the cloud from which the skin experts can access it at remote locations. Such large-scale data are highly vulnerable and otherwise have catastrophic results for privacy and security mechanisms. Medical organizations currently do not concentrate much on maintaining safety and privacy, which are of major importance in the field. This paper provides an IoT based skin surveillance system based on a blockchain data protection and safety mechanism. A secure data transmission mechanism for IoT devices used in a distributed architecture is proposed. Privacy is assured through a unique key to identify each user when he registers. The principle of blockchain also addresses security issues through the generation of hash functions on every transaction variable. We use blockchain consortiums that meet our criteria in a decentralized environment for controlled access. The solutions proposed allow IoT based skin surveillance systems to privately and securely store and share medical data over the network without disturbance.

Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive validator selection and consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger.

With the development of the Internet of Things (IoT), it has been widely deployed. As many embedded devices are connected to the network and massive amounts of security-sensitive data are stored in these devices, embedded devices in IoT have become the target of attackers. The trusted computing is a key technology to guarantee the security and trustworthiness of devices' execution environment. This paper focuses on security problems on IoT devices, and proposes a security architecture for IoT devices based on the trusted computing technology. This paper implements a security management system for IoT devices, which can perform integrity measurement, real-time monitoring and security management for embedded applications, providing a safe and reliable execution environment and whitelist-based security protection for IoT devices. This paper also designs and implements an embedded security protection system based on trusted computing technology, containing a measurement and control component in the kernel and a remote graphical management interface for administrators. The kernel layer enforces the integrity measurement and control of the embedded application on the device. The graphical management interface communicates with the remote embedded device through the TCP/IP protocol, and provides a feature-rich and user-friendly interaction interface. It implements functions such as knowledge base scanning, whitelist management, log management, security policy management, and cryptographic algorithm performance testing.

The advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) enabled a new class of smart and interactive devices. With their continuous connectivity and their access to valuable information in both the digital and physical world, they are attractive targets for security attackers. Hence, with their integration into both the industry and consumer devices, they added a new surface for cybersecurity attacks. These potential threats call for special care of security vulnerabilities during the design of IoT devices and CPS. The design of secure systems is a complex task, especially if they must adhere to other constraints, such as performance, power consumption, and others. A range of design space exploration tools have been proposed in academics, which aim to support system designers in their task of finding the optimal selection of hardware components and task mappings. Said tools offer a limited way of modeling attack scenarios as constraints for a system under design. The framework proposed in this paper aims at closing this gap, offering system designers a way to consider security attacks and security risks during the early design phase. It offers designers to model security constraints from the view of potential attackers, assessing the probability of successful security attacks and security risk. The framework's feasibility and performance is demonstrated by revisiting a potential system design of an industry partner.

Internet of Things (IoT) is experiencing significant growth in the safety-critical applications which have caused new security challenges. These devices are becoming targets for different types of physical attacks, which are exacerbated by their diversity and accessibility. Therefore, there is a strict necessity to support embedded software developers to identify and remediate the vulnerabilities and create resilient applications against such attacks. In this paper, we propose a hardware security vulnerability assessment based on fault injection of an embedded application. In our security assessment, we apply a fault injection attack by using our clock glitch generator on a critical medical IoT device. Furthermore, we analyze the potential risks of ignoring these attacks in this embedded application. The results will inform the embedded software developers of various security risks and the required steps to improve the security of similar MCU-based applications. Our hardware security assessment approach is easy to apply and can lead to secure embedded IoT applications against fault attacks.

This paper describe most popular IoT protocols used for IoT embedded systems and research their advantage and disadvantage. Hardware stage used in this experiment is described in this article - it is used Esp32 and programming language C. It is very important to use corrected IoT protocol that is determines of purpose, hardware and software of system. There are so different IoT protocols, because they are cover vary requirements for vary cases.

Manufacturing limitations, configuration and maintenance flaws associated with the Internet of Things (IoT) devices have resulted in an ever-expanding attack surface. Attackers exploit IoT devices to steal private information, take part in botnets, perform Denial of Service (DoS) attacks and use their resources for the mining of cryptocurrency. In this paper, we experimentally evaluate a hypothesis that attacks on IoT devices follow the generalised Cyber Kill Chain (CKC) model. We used a medium-interaction honeypot to capture and analyse more than 30,000 attacks targeting IoT devices. We classified the steps taken by the attackers using the CKC model and extended CKC to an IoT Kill Chain (IoTKC) model. The IoTKC provides details about IoT-specific attack characteristics and attackers' activities in the exploitation of IoT devices.

Over the past decade, distributed CSMA, which forms the basis for WiFi, has been deployed ubiquitously to provide seamless and high-speed mobile internet access. However, distributed CSMA might not be ideal for future IoT/M2M applications, where the density of connected devices/sensors/controllers is expected to be orders of magnitude higher than that in present wireless networks. In such high-density networks, the overhead associated with completely distributed MAC protocols will become a bottleneck. Moreover, IoT communications are likely to have strict QoS requirements, for which the `best-effort' scheduling by present WiFi networks may be unsuitable. This calls for a clean-slate redesign of the wireless MAC taking into account the requirements for future IoT/M2M networks. In this paper, we propose a reservation-based (for minimal overhead) wireless MAC designed specifically with IoT/M2M applications in mind.

Nowadays, the emerging Internet-of-Things (IoT) emphasize the need for the security of network-connected devices. Additionally, there are two types of services in IoT devices that are easily exploited by attackers, weak authentication services (e.g., SSH/Telnet) and exploited services using command injection. Based on this observation, we propose IoTCMal, a hybrid IoT honeypot framework for capturing more comprehensive malicious samples aiming at IoT devices. The key novelty of IoTC-MAL is three-fold: (i) it provides a high-interactive component with common vulnerable service in real IoT device by utilizing traffic forwarding technique; (ii) it also contains a low-interactive component with Telnet/SSH service by running in virtual environment. (iii) Distinct from traditional low-interactive IoT honeypots[1], which only analyze family categories of malicious samples, IoTCMal primarily focuses on homology analysis of malicious samples. We deployed IoTCMal on 36 VPS1 instances distributed in 13 cities of 6 countries. By analyzing the malware binaries captured from IoTCMal, we discover 8 malware families controlled by at least 11 groups of attackers, which mainly launched DDoS attacks and digital currency mining. Among them, about 60% of the captured malicious samples ran in ARM or MIPs architectures, which are widely used in IoT devices.

Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet of Things (IoT) and Vehicle to Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable of managing the mmWave spectrum sharing efficiently. However, Cognitive mmWave Radios are vulnerable to malicious users due to the complex dynamic radio environment and the shared access medium. This indicates the necessity to implement techniques able to detect precisely any anomalous behaviour in the spectrum to build secure and efficient radios. In this work, we propose a comparison framework between deep generative models: Conditional Generative Adversarial Network (C-GAN), Auxiliary Classifier Generative Adversarial Network (AC-GAN), and Variational Auto Encoder (VAE) used to detect anomalies inside the dynamic radio spectrum. For the sake of the evaluation, a real mmWave dataset is used, and results show that all of the models achieve high probability in detecting spectrum anomalies. Especially, AC-GAN that outperforms C-GAN and VAE in terms of accuracy and probability of detection.

The current trend of IoT user is toward the use of services and data externally due to voluminous processing, which demands resourceful machines. Instead of relying on the cloud of poor connectivity or a limited bandwidth, the IoT user prefers to use a cloudlet-based fog computing. However, the choice of cloudlet is solely dependent on its trust and reliability. In practice, even though a cloudlet possesses a required trusted platform module (TPM), we argue that the presence of a TPM is not enough to make the cloudlet trustworthy as the TPM supports only the primitive security of the bootstrap. Besides uncertainty in security, other uncertain conditions of the network (e.g. network bandwidth, latency and expectation time to complete a service request for cloud-based services) may also prevail for the cloudlets. Therefore, in order to evaluate the trust value of multiple cloudlets under uncertainty, this paper broadly proposes the empirical process for evaluation of trust. This will be followed by a measure of trust-based reputation of cloudlets through computational intelligence such as fuzzy logic and ant colony optimization (ACO). In the process, fuzzy logic-based inference and membership evaluation of trust are presented. In addition, ACO and its pheromone communication across different colonies are being modeled with multiple cloudlets. Finally, a measure of affinity or popular trust and reputation of the cloudlets is also proposed. Together with the context of application under multiple cloudlets, the computationally intelligent approaches have been investigated in terms of performance. Hence the contribution is subjected towards building a trusted cloudlet-based fog platform.

This paper deals with novel group-based Authentication and Key Agreement protocol for Internet of Things(IoT) enabled LTE/LTE-A network to overcome the problems of computational overhead, complexity and problem of heterogeneous devices, where other existing methods are lagging behind in attaining security requirements and computational overhead. In this work, two Groups are created among Machine Type Communication Devices (MTCDs) on the basis of device type to reduce complexity and problems of heterogeneous devices. This paper fulfills all the security requirements such as preservation, mutual authentication, confidentiality. Bio-metric authentication has been used to enhance security level of the network. The security and performance analysis have been verified through simulation results. Moreover, the performance of the proposed Novel Group-Based Authentication and key Agreement(AKA) Protocol is analyzed with other existing IoT enabled LTE/LTE-A protocol.

Document integrity and origin for E2E S2S in IoTcloud have recently received considerable attention because of their importance in the real-world fields. Maintaining integrity could protect decisions made based on these message/image documents. Authentication and integrity solutions have been conducted to recognise or protect any modification in the exchange of documents between E2E S2S (smart-to-smart). However, none of the proposed schemes appear to be sufficiently designed as a secure scheme to prevent known attacks or applicable to smart devices. We propose a robust scheme that aims to protect the integrity of documents for each users session by integrating HMAC-SHA-256, handwritten feature extraction using a local binary pattern, one-time random pixel sequence based on RC4 to randomly hide authentication codes using LSB. The proposed scheme can provide users with one-time bio-key, robust message anonymity and a disappearing authentication code that does not draw the attention of eavesdroppers. Thus, the scheme improves the data integrity for a users messages/image documents, phase key agreement, bio-key management and a one-time message/image document code for each users session. The concept of stego-anonymity is also introduced to provide additional security to cover a hashed value. Finally, security analysis and experimental results demonstrate and prove the invulnerability and efficiency of the proposed scheme.

There are various Lightweight Block Ciphers (LBC) nowadays that exist to meet the demand on security requirements of the current trend in computing world, the application in the resource-constrained devices, and the Internet of Things (IoT) technologies. One way to evaluate these LBCs is to conduct a performance analysis. Performance evaluation parameters seek appropriate value such as encryption time, security level, scalability, and flexibility. Like SIMECK block cipher whose algorithm design was anchored with the SIMON and SPECK block ciphers were efficient in security and performance, there is a need to revisit its design. This paper aims to present a comparative study on the performance analysis of the enhanced round function of the SIMECK Family block cipher. The enhanced ARX structure of the round function on the three variants shows an efficient performance over the original algorithm in different simulations using the following methods of measurement; avalanche effect, runtime performance, and brute-force attack. Its recommended that the enhanced round function of the SIMECK family be evaluated by different security measurements and attacks.

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results.

Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users' IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.