Deployment Of Internet Of Things Devices Research Articles

ADEPT: Detection and Identification of Correlated Attack Stages in IoT Networks

The fast-growing Internet-of-Things (IoT) market has opened up a large threat landscape, given the wide deployment of IoT devices in both consumer and commercial spaces. Attacks on IoT devices generally consist of multiple stages and are dispersed spatially and temporally. These characteristics make it challenging to detect and identify the attack stages using solutions that tend to be localized in space and time. In this work, we present Adept, a distributed framework to detect and identify the individual attack stages in a coordinated attack. Adept works in three phases. First, network traffic of IoT devices is processed locally for detecting anomalies with respect to their benign profiles. Any alert corresponding to a potential anomaly is sent to a security manager, where aggregated alerts are mined, using frequent itemset mining (FIM), for detecting patterns correlated across both time and space. Finally, using both alert-level and pattern-level information as features, we employ a machine learning approach to identify individual attack stages in the generated alerts. We carry out extensive experiments, with emulated and realistic network traffic; the results demonstrate the effectiveness of the proposed framework in terms of its ability in attack-stage detection and identification.

Searchable Encryption with Access Control in Industrial Internet of Things (IIoT)

The technological advancements in the Internet of Things (IoT) and related technologies lead to revolutionary advancements in many sectors. One of these sectors, is the industrial sector red that leverages IoT technologies forming the Industrial Internet of Things (IIoT). IIoT has the potential to enhance the manufacturing process by improving the quality, trace‐ability, and integrity of the industrial processes. The enhancement of the manufacturing process is achieved by deploying IoT devices (sensors) across the manufacturing facilities; therefore, monitoring systems are required to collect (from multiple locations) and analyse the data, most likely in the cloud. As a result, IIoT monitoring systems should be secure, preserve the privacy, and provide real‐time responses for critical decision‐making. In this review, we identified a gap in the state‐of‐the‐art of secure IIoT and propose a set of criteria for secure and privacy preserving IIoT systems to enhance efficiency and deliver better IIoT applications.

Requirements for Security, Privacy, and Trust in the Internet of Things [Guest Editors' Introduction

The articles in this special section focus on system requirements for security, privacy, and trust for the Internet of Things (IoT). IoT has huge potential to push monitoring, computing, and communication deeply into the home, the workplace, medicine, manufacturing, and critical infrastructure. This increased capability offers the hope that urban settings can be transformed in various ways through the innovative deployment of IoT devices. Driven by a decline in the cost of sensors, many cities have adapted a plan to transition services toward being a fully smart city by implementing real-time data-driven management services across urban systems, including efficiently managing water, energy, waste, policing, and transportation among other citywide services. Cities continue to attract new people and the United Nations (UN) estimates that by 2030, more than 60% of the global population will live in large cities.1 With nearly 38 million people, Tokyo tops the UN’s ranking of most populous cities, followed by Delhi, Shanghai, Mexico City, São Paulo, and Mumbai.

Congestion free opportunistic multipath routing load balancing scheme for Internet of Things (IoT)

Internet of Things (IoT) interconnects billions of devices to form a heterogeneous network over the Internet. The heterogeneous communication infrastructure of IoT opens a door for the research community to design new protocols by utilizing minimal resources of these tiny devices to achieve accurate results. Therefore, the research community suggests various techniques to resolve the load balancing issue of IoT and prolong the network lifetime. Although, these techniques are effective at some stage in managing the load balancing issue, they have some side effects on network performance such as high communication costs, end-to-end (E2E) delay, packet lost ratio (PLR), throughput, and individual sensor devices lifespan, etc. Therefore, an efficient lightweight load balancing routing protocol is needed to be developed to address the aforementioned issues in IoT networks. In this paper, Dynamic hop selection static routing protocol (DHSSRP) is proposed to resolve the load balancing issue of IoT networks in congestion-free and priority-based communication infrastructure. The proposed DHSSRP routing protocol priorities the sensitive/critical information of sensors devices with static routing and divert the neighbor’s sensor communication with an alternate hop selection path, which manages the network traffic in a congestion-free environment. The traffic management of the DHSSRP routing protocol based on priority-based information balances the energy consumption with a balanced traffic environment, which maximizes the lifespan of the deployed IoT devices in the network. The results captured during simulation for the proposed scheme were compared with the field-proven scheme, which showed a significant improvement for the metrics, such as communication cost, computational cost, traffic congestion, throughput, PLR, and network lifetime. Moreover, the results observed for an individuals sensor devices to participate in the network until the end-stage showed a 95.8 % result with the same onboard battery power.

A Large-Scale Empirical Study on the Vulnerability of Deployed IoT Devices

The Internet of Things (IoT) has become ubiquitous and greatly affected peoples’ daily lives. With the increasing development of IoT devices, the corresponding security issues are becoming more and more challenging. Such a severe security situation raises the following questions that need urgent attention: What are the primary security threats that IoT devices face currently? How do vendors and users deal with these threats? In this article, we aim to answer these critical questions through a large-scale systematic study. Specifically, we perform a ten-month-long empirical study on the vulnerability of 1,362,906 IoT devices varying from six types. The results show sufficient evidence that N-days vulnerability is seriously endangering the IoT devices: 385,060 (28.25 percent) devices suffer from at least one N-days vulnerability. Moreover, 2669 of these vulnerable devices may have been compromised by botnets. We further reveal the massive differences among five popular IoT search engines: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shodan</i> [1], <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Censys</i> [2], [3], <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Zoomeye</i> [4], <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fofa</i> [5], and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NTI</i> [6]. To study whether vendors and users adopt defenses against the threats, we measure the security of MQTT [7] servers, and identify that 12740 (88 percent) MQTT servers have no password protection. Our analysis can serve as an important guideline for investigating the security of IoT devices, as well as advancing the development of a more secure environment for IoT systems.

Resistance of IoT Sensors against DDoS Attack in Smart Home Environment.

Smart devices along with sensors are gaining in popularity with the promise of making life easier for the owner. As the number of sensors in an Internet of Things (IoT) system grows, a question arises as to whether the transmission between the sensors and the IoT devices is reliable and whether the user receives alerts correctly and in a timely manner. Increased deployment of IoT devices with sensors increases possible safety risks. It is IoT devices that are often misused to create Distributed Denial of Service (DDoS) attacks, which is due to the weak security of IoT devices against misuse. The article looks at the issue from the opposite point of view, when the target of a DDoS attack are IoT devices in a smart home environment. The article examines how IoT devices and the entire smart home will behave if they become victims of a DDoS attack aimed at the smart home from the outside. The question of security was asked in terms of whether a legitimate user can continue to control and receive information from IoT sensors, which is available during normal operation of the smart home. The case study was done both from the point of view of the attack on the central units managing the IoT sensors directly, as well as on the smart-home personal assistant systems, with which the user can control the IoT sensors. The article presents experimental results for individual attacks performed in the case study and demonstrates the resistance of real IoT sensors against DDoS attack. The main novelty of the article is that the implementation of a personal assistant into the smart home environment increases the resistance of the user’s communication with the sensors. This study is a pilot testing the selected sensor sample to show behavior of smart home under DDoS attack.

A Scalable Platform for Enabling the Forensic Investigation of Exploited IoT Devices and Their Generated Unsolicited Activities

The analysis of large-scale cyber attacks, which utilized millions of exploited Internet of Things (IoT) devices to perform malicious activities, highlights the significant role of compromised IoT devices in enabling evasive and effective attacks at scale. Motivated by the shortage of empirical data related to the deployment of IoT devices, and the lack of understanding about compromised devices and their unsolicited activities, in this paper, we leverage a big data analytics framework (Apache Spark) to design and develop a scalable system for automated detection of compromised IoT devices and characterization of their unsolicited activities. The system utilizes IoT device information and passive network measurements obtained from a large network telescope, while implementing an array of data-driven methodologies rooted in data mining and machine learning techniques, to provide a macroscopic view of IoT-generated malicious activities. We evaluate the system with more than 4TB of passive network measurements and demonstrate its effectiveness in the network forensic investigation of compromised devices and their activities, in near real-time. In addition, we empirically analyze and elaborate on the capabilities of the developed system as a scalable infrastructure, which can support a number of applications that enable IoT-centric forensics.

IoT scheduling for higher throughput and lower transmission power

The massive increase in the Internet of Things (IoT) has brought a diverse long-range, low-power, and low bit-rate wireless network technologies. The LoRa a low-power wide area network (LPWAN) gained popularity as radio technology for the realization of many IoT applications. LoRa is typically employed together with LoRaWAN MAC protocol and operates in the license-free ISM bands. LoRa networks have an issue with scalability when the number of end nodes connected to one network is larger than the shared number of channels, that causes a collision and packets loss through receiving a wide range of different message sizes from various application. In this paper, we describe an accurate and efficient way confirmed by simulation to calculate the probability of collision rate and packet loss in LPWANs under various circumstances. Moreover, based on the LoRaWAN specification, we consider a dense network deployment of IoT devices. In the event of collisions, our proposed algorithms is classified to two approaches. Firstly a time scheduling algorithm is proposed for LoRaWAN networks that consist of devices supporting LoRaWAN class C mode for synchronization in the beginning between the gateway and the end nodes. Afterwards these devices switch to class A to significantly decrease the collision and to enhance scalability by assigning a Guard Time to each end node. Gateway acknowledgment (ACK) messages to the end nodes are used through class C. Secondly, we also propose a distance spreading factor algorithm according to the distance of the end nodes from the gateway to reduce the probability of collision. Furthermore, many of these devices are battery powered, therefore low power consumption is required.

Comments on “Toward Secure and Provable Authentication for Internet of Things: Realizing Industry 4.0”

Internet of Things (IoT) is the next era of communication networks. The concept of IoT is that everything within the global communication network is interconnected and accessible. Since IoT has various applications, including Industry 4.0. Therefore, upcoming and existing IoT applications are highly auspicious to enhance the level of automation, efficiency, and comfort for the users. However, to a certain extent, there are numerous challenges while deploying IoT devices in the Industry 4.0, like IoT devices are assumed to have inadequate resources to support security solutions. Therefore, in order to protect the communication environment, an efficient and lightweight security solution is needed. Recently, on the basis of a hierarchical approach, Garg et al. presented a lightweight, robust key agreement, and provably secure authentication protocol for the IoT environment. Their introduced protocol relies on lightweight operations, including XOR operation, concatenation, hash function, physically unclonable function (PUF), and elliptic curve cryptography. However, in this comment, we point out the security loopholes of Garg et al. ’s protocol and show that it is vulnerable to the IoT-node impersonation attack. Moreover, it has irrelevant generation and usage of some parameters. Therefore, we put forward some valuable suggestions for attack resilience.

Trust-Oriented IoT Service Placement for Smart Cities in Edge Computing

Smart city is gradually forming a large scope of Internet of Things (IoT) networks with diffusely deployed IoT devices that produce quantities of services. Considering the large-scale and widely distributed features of IoT networks, edge computing is emerged as a powerful and suitable paradigm to provide computing abilities for the IoT devices at the edge of the networks. In edge computing, the IoT services could be placed on the edge computing units (ECUs) for execution, which provides low latency and eases the burden of bandwidth. However, it is still challenging to improve the overall ECU execution performance (i.e., the resource usage, the load balance levels, and the power consumption of ECUs) and meanwhile prevent privacy leakage of the IoT devices for service placement. To tackle this challenge, a trust-oriented IoT service placement method, abbreviated as TSP, is proposed for smart cities in edge computing. Technically, improving the strength Pareto evolutionary algorithm (SPEA2) is leveraged to acquire the balanced placement strategies for the tradeoffs among the execution performance metrics with privacy preservation. Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) and multicriteria decision-making (MCDM) techniques are employed to identify the optimal placement strategy among the obtained service placement strategies. Eventually, systematic experiments are conducted to verify the efficiency and reliability of TSP.

LSDAR: A light-weight structure based data aggregation routing protocol with secure internet of things integrated next-generation sensor networks

Wireless Sensor Network (WSN) gains a lot of research devotion because of their unique features as compared to traditional networks. The resource constraints next-generation sensors are interconnected in billions for the foundation of the large-sized Internet of Things (IoT). Due to the large-sized deployment of IoT devices and their dynamic conditions, existing security mechanisms do not meet the requirements of protection against malicious adversaries. Moreover, because of the unreliable and open-access Internet medium, traditional routing protocols are vulnerable to a variety of malicious threats and compromised to data security. Thus, the aim of this research article is to present a novel light-weight structure based Data Aggregation Routing (LSDAR) protocol for IoT integrated Next-generation Sensor Networks for the improvement of energy routing performance with node level data protection against malicious threats. Firstly, the network nodes are decomposed into independent clusters based on varying radiuses and preventing energy holes around the locality of the base station (BS). Secondly, based on the A-star heuristics algorithm, efficient and loop-free routing paths are constructed. Furthermore, the end-to end communication links are protected against malicious nodes by using mathematically unbreakable one time pad (OTP) encryption scheme to provide data security. The simulation results indicate significant improvements with the comparison of the state-of-the-art in terms of energy consumption, network lifetime, an end to end delay and packet drop ratio.

An Economical Incorporation of IoT and Edge/ Cloud Computing for Dynamic Distribution of IoT Analytics and Organized Utilization of Network Resources

As the Internet of Things (IoT) keeps on picking up application in telecommunication networks, an enormous num-ber of devices are relied upon to be associated and utilised sooner rather than later. Wide scale Internet of Things (IoT) systems with several deployed IoT devices, such as sensors, actuators and so on, that generate a high volume of data is expected. This means that the volume of data is foretold to increase substantial-ly. Customarily, cloud services have been executed in huge data-centers in the central network. Be that as it may, this is definitely not a long-haul adaptable choice as an exceptionally enormous number of gadgets are required to be associated and utilised sooner rather than later. An adaptable and productive arrange-ment is to disperse the IoT analytics between the core cloud and the network edge. This paper uses edge IoT analytics to viably and economically convey the information from the IoT between the core cloud and the network edge. First analytics can be com-pleted on the edge cloud and just the essential information or results are sent for further investigation. We have identified an approach to make this operation increasingly affordable.

Toward Secure and Provable Authentication for Internet of Things: Realizing Industry 4.0

The Internet of Things (IoT) has many applications, including Industry 4.0. There are a number of challenges when deploying IoT devices in the Industry 4.0 setting, partly due to the low-cost IoT devices/nodes with limited capacity to run/support security solutions. Hence, there is a need for a lightweight and efficient security solution to protect the environment. Thus, in this article, we present a robust, lightweight, and provably secure authentication and key agreement protocol specifically for the IoT environment based on a hierarchical approach. The proposed protocol relies on lightweight operations, such as elliptic curve cryptography, physically unclonable functions, hash functions, concatenation, and XOR operations. We then evaluate the security of the designed protocol, including the widely used automated validation of Internet security protocols and applications (AVISPA), and demonstrate that it supports mutual authentication between IoT nodes and server, and is resilient against a number of common security attacks [denial of service (DoS), replay, spoofing, etc.]. The computational and communication overhead analysis shows that the proposed protocol is comparatively less expensive than three other recently published, competing protocols.

IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks.

The Fifth Generation of Mobile Communications (5G) will lead to the growth of use cases demanding higher capacity and a enhanced data rate, a lower latency, and a more flexible and scalable network able to offer better user Quality of Experience (QoE). The Internet of Things (IoT) is one of these use cases. It has been spreading in the recent past few years, and it covers a wider range of possible application scenarios, such as smart city, smart factory, and smart agriculture, among many others. However, the limitations of the terrestrial network hinder the deployment of IoT devices and services. Besides, the existence of a plethora of different solutions (short vs. long range, commercialized vs. standardized, etc.), each of them based on different communication protocols and, in some cases, on different access infrastructures, makes the integration among them and with the upcoming 5G infrastructure more difficult. This paper discusses the huge set of IoT solutions available or still under standardization that will need to be integrated in the 5G framework. UAVs and satellites will be proposed as possible solutions to ease this integration, overcoming the limitations of the terrestrial infrastructure, such as the limited covered areas and the densification of the number of IoT devices per square kilometer.

A multi‐attack resilient lightweight IoT authentication scheme

AbstractMajority of the devices in the Internet of Things (IoT) are deployed in an environment that is susceptible to cyber‐attacks. Due to the resource‐constraint nature of IoT, it is very hard to meet the security challenges that arise due to the deployment of IoT devices in the unsecure environment. In this context, the authentication of IoT devices is one of the core challenges. Many protocols have been designed to address and overcome the security issues that stem from the authentication failure. However, many of these protocols are designed using the complex cryptographic techniques that may not be supported by IoT devices. In this paper, we propose a lightweight and secure mutual authentication scheme for resource constraint IoT devices. The proposed scheme is robust against cyber‐attacks, such as impersonation, modification, session key disclosure, and eavesdropping attacks. The security of the proposed scheme is formally tested using the Automated Validation of Internet Security Protocols and Applications tool and found the scheme to be secure in the Dolev‐Yao attack model. Moreover, the performance features such as communication overhead, computation time, and the turnaround time of the proposed scheme are evaluated and compared with the recent schemes of same category, where the proposed scheme shows a balance of performance without compromising the security features.

Comprehending the IoT cyber threat landscape: A data dimensionality reduction technique to infer and characterize Internet-scale IoT probing campaigns

The resource-constrained and heterogeneous nature of Internet-of-Things (IoT) devices coupled with the placement of such devices in publicly accessible venues complicate efforts to secure these devices and the networks they are connected to. The Internet-wide deployment of IoT devices also makes it challenging to operate security solutions at strategic locations within the network or to identify orchestrated activities from seemingly independent malicious events from such devices. Therefore, in this paper, we initially seek to determine the magnitude of IoT exploitations by examining more than 1 TB of passive measurement data collected from a/8 network telescope and by correlating it with 400 GB of information from the Shodan service. In the second phase of the study, we conduct in-depth discussions with Internet Service Providers (ISPs) and backbone network operators, as well as leverage geolocation databases to not only attribute such exploitations to their hosting environment (ISPs, countries, etc.) but also to classify such inferred IoT devices based on their hosting sector type (financial, education, manufacturing, etc.) and most abused IoT manufacturers. In the third phase, we automate the task of alerting realms that are determined to be hosting exploited IoT devices. Additionally, to address the problem of inferring orchestrated IoT campaigns by solely observing their activities targeting the network telescope, we further introduce a theoretically sound technique based on L1-norm PCA, and validate the utility of the proposed data dimensionality reduction technique against the conventional L2-norm PCA. Specifically, we identify “in the wild” IoT coordinated probing campaigns that are targeting generic ports and campaigns specifically searching for open resolvers (for amplification purposes). The results reveal more than 120,000 Internet-scale exploited IoT devices, some of which are operating in critical infrastructure sectors such as health and manufacturing. We also infer 140 large-scale IoT-centric probing campaigns; a sample of which includes a worldwide distributed campaign where close to 40% of its population includes video surveillance cameras from a single manufacturer, and another very large inferred coordinated campaign consisting of more than 50,000 IoT devices. The reported findings highlight the insecurity of the IoT paradigm at large and thus demonstrate the importance of understanding such evolving threat landscape.

Full-Duplex-Based Control Channel Establishment for Cognitive Internet of Things

The Internet of Things (IoT) is a novel network that transforms our future by interconnecting everything. With the rapid deployment of IoT devices and applications, a large amount of data is introduced into the network. Hence, we may suffer spectrum scarcity issue due to the massive data. Cognitive radio (CR) is a potential solution for addressing this issue, which can improve spectrum efficiency by dynamic spectrum access. The IoT device with CR capability has become a new research interest. In this article, we propose a full-duplex-based control channel establishment (FDCCE) scheme for cognitive IoT (CIoT). In the FDCCE scheme, CR-based IoT devices (CRDs) can establish control channels on both licensed spectrum and unlicensed spectrum without a central entity. By combining the licensed spectrum and the unlicensed spectrum, the control channel between CRDs can be established successfully even though there is no common available licensed channel between them. Moreover, CRDs with FD radios can sense and transmit data on the same channel simultaneously. Hence, the interference with licensed primary users can be effectively avoided while the time for dedicated spectrum sensing can be saved. Rendezvous on the same channel is the necessary process for the control channel establishment between CRDs. Performance analysis demonstrates the upper bound of the maximum time to rendezvous, channel loading, and degree of rendezvous for the FDCCE scheme. Simulation results show the superiority of the FDCCE scheme in terms of time to rendezvous.

An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings

Scalability of control algorithms used for savings energy in commercial building Heating, Ventilation and Air-Conditioning (HVAC) system and their implementation on low cost resource constrained hardware is a challenging problem. This paper presents the Internet of Things (IoT) prototype which implements a smart and scalable control approach called the Smart-Token Based Scheduling Algorithm (Smart-TBSA) to minimize energy in commercial building HVAC systems. The IoT prototype is formalized with an architecture that encapsulates the different components (hardware, software, networking, and their integration) along with their interactions. A detailed description of the different components, hardware design, deployment issues, and their integration with legacy systems as well as cloud-connectivity is presented. In addition, simple modifications required for transforming the optimization models to an active control technique is also presented. While scalability is provided by the decentralized control, recursive zone thermal model identification, prediction occupant’s thermal sensation, and embedding them within the optimization models enhances the smartness. Consequently, due to the implementation of Smart-TBSA using IoT devices, an otherwise centralized control architecture of the legacy building automation system is transformed to a more scalable and smart decentralized one. The proposed Smart-TBSA and IoT prototype are illustrated on a pilot building in Nanyang Technological University, Singapore having 85 zones. Our results shows that by combining IoT with decentralized control, energy savings up to 20% can be derived. Moreover, we show that legacy building automation system can be transformed into a more smart, adaptable, scalable, and decentralized control by deploying IoT devices without incurring significant costs.

Understanding and Statically Detecting Synchronization Performance Bugs in Distributed Cloud Systems

In such an information society, the Internet of Things (IoT) plays an increasingly important role in our daily lives. With such a huge number of deployed IoT devices, Cyber-Physical System (CPS) calls for powerful distributed infrastructures to supply big data computing, intelligence, and storage services. With the increasingly complex distributed software infrastructures, new intricate bugs continue to manifest, causing huge economic loss. Synchronization performance problems, which means that improper synchronizations may degrade the performance and even lead to service exception, heavily influence the entire distributed cluster, imperiling the reliability of the system. As one kind of performance problems, the synchronization performance problems are acknowledged as difficult to diagnosis and fix. We collect 26 performance issues in three real-world distributed systems: HDFS, Hadoop MapReduce, and HBase, and do analysis on their root cause, fix strategy, and algorithm complexity in order to understand these synchronization performance bugs better. Then, we implement a static detection tool including critical section identifier, loop identifier, inner loop identifier, expensive loop identifier, and pruning component. After that, we evaluate our detection tool on these three distributed systems with sampled bugs. In the evaluation, our detection tool accurately finds out all the target bugs. Besides, it points out more new potential performance problems than the previous works. With the strict performance overhead, our detection tool is proved to be greatly efficient.

When IoT Meets DevOps: Fostering Business Opportunities

The Internet of Things (IoT) is the new digital revolution for the near-future society, the second after the creation of the Internet itself. The software industry is converging towards the large-scale deployment of IoT devices and services, and there’s broad support from the business environment for this engineering vision. The Development and Operations (DevOps) project management methodology, with continuous delivery and integration, is the preferred approach for achieving and deploying applications to all levels of the IoT architecture. In this paper we also discuss the promising trend of associating devices with microservices, which are further encapsulated into functional packages called containers. Docker is considered the market leader in container-based service delivery, though other important software companies are promoting this concept as part of the technology solution for their IoT customers. In the experimental section we propose a three-layer IoT model, business-oriented, and distributed over multiple cloud environments, comprising the Physical, Fog/Edge, and Application layers.&#x0D; &#x0D; &#x0D; Keywords: Internet-of-Things, software technologies, project management, business environment Headings