Internet Of Things Operating Systems Research Articles

Machine Learning Based Vulnerability Detection in Internet of Things Operating System’s Codes

Machine Learning Based Vulnerability Detection in Internet of Things Operating System’s Codes

Advancing Healthcare Networks: Optimizing Multi-Objective RPL for Diverse Traffic in Low-Power, Lossy Environments

RPL: Powering modern healthcare IoT Networks. The Routing Protocol for Low Power and Lossy Networks(RPL) is a protocol specifically designed for routing in networks characterized by low power and lossy connections built on IPv6. It is specifically designed for the growing use of Instantaneous IoT (Internet of Things) applications. These networks cater to the specialized requirements of Low Power (resource-constrained) and Lossy Networks(unstable networks), also known as LLNs, where routing data smoothly and prioritizing traffic types are the significant challenges. Studies have shown that standard RPL, which makes use of a set of routing rules, struggles to provide the level of performance that many IoT applications in modern-day healthcare demand. This limits RPL’s potential in scenarios where we have a mix of data traffic, which is very common in healthcare settings. To handle the diverse data traffic found in healthcare settings, our method involves distributing several instances of RPL settings throughout the network. The main objective of this strategy is to improve the effectiveness of important and critical healthcare applications. We have developed and thoroughly tested our system using Contiki, which is an open-source IoT operating system. We have three instances in our MultiInstance RPL setup, each of which is intended to handle different kinds of network traffic and provide varying Quality of Service (QoS) measures. Different sets of Objective Function (OF) rules are used by each instance to decide which route is optimal for the data it processes. We have made use of OF0 (Objective Function 0) and MRHOF (Minimum Rank with Hysteresis Objective Function). We have rigorously tested our solution. Our focus has been on metrics like delay, average energy consumption and Packet Delivery Ratio (PDR). Compared to standard RPL, our approach improved packet delivery rates and significantly reduced delays for high priority data packets. This research shows how to better address the complex data traffic needs of real-time healthcare IoT networks.

IDetect for vulnerability detection in internet of things operating systems using machine learning

Internet of Things (IoT) 's devices are ubiquitous and operate in a heterogonous environment with potential security breaches. IoT Operating Systems (IoT OSs) are the backbone software for running such devices. If IoT OSs are vulnerable to security breaches, higher-level security measures may not help. This paper aims to use Machine Learning (ML) to create a tool called iDetect for detecting vulnerabilities in C/C++ source code of IoT OSs. The source code for 16 releases of IoT OSs (RIOT, Contiki, FreeRTOS, Amazon FreeRTOS) and the Software Assurance Reference Dataset (SARD) were used to create a labeled dataset of vulnerable and benign code with the reference being the Common Weakness Enumeration (CWE) vulnerabilities present in IoT OSs. Studies showed that only a subset of CWEs is present in the C/C++ source code of low-end IoT OSs.The labeled dataset was used to train three ML models for vulnerability detection: Random Forest (RF), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The three models were used independently and RF; compared to CNN and RNN, gave the highest accuracy during the testing phase for binary and multiclass classification. RF was chosen as iDetect's ML classifier. Further evaluation was done on an unseen dataset of 322 code snippets taken from TinyOS. iDetect achieved a macro-averaged F1 score (mF1) of 98.5% and weighted-average F1 score (wF1) of 98% for multiclass classification, F1 score (F1) of 97.8% for binary classification, and superior results compared to all three Static Analysis Tools (SATs) used to collect the training dataset.

Technique for IoT malware detection based on control flow graph analysis

The Internet of Things (IoT) refers to the millions of devices around the world that are connected to the Internet. Insecure IoT devices designed without proper security features are the targets of many Internet threats. The rapid integration of the Internet into the IoT infrastructure in various areas of human activity, including vulnerable critical infrastructure, makes the detection of malware in the Internet of Things increasingly important. Annual reports from IoT infrastructure cybersecurity companies and antivirus software vendors show an increase in malware attacks targeting IoT infrastructure. This demonstrates the failure of modern methods for detecting malware on the Internet of things. This is why there is an urgent need for new approaches to IoT malware detection and to protect IoT devices from IoT malware attacks. The subject of the research is the malware detection process on the Internet of Things. This study aims to develop a technique for malware detection based on the control flow graph analysis. Results. This paper presents a new approach for IoT malware detection based on control flow graph analysis. Control flow graphs were built for suspicious IoT applications. The control flow graph is represented as a directed graph, which contains information about the components of the suspicious program and the transitions between them. Based on the control flow graph, metrics can be extracted that describe the structure of the program. Considering that IoT applications are small due to the simplicity and limitations of the IoT operating system environment, malware detection based on control flow graph analysis seems to be possible in the IoT environment. To analyze the behavior of the IoT application for each control flow graph, the action graph is to be built. It shows an abstract graph and a description of the program. Based on the action graph for each IoT application, a sequence is formed. This allows for defining the program’s behavior. Thus, with the aim of IoT malware detection, two malware detection models based on control flow graph metrics and the action sequences are used. Since the approach allows you to analyze both the overall structure and behavior of each application, it allows you to achieve high malware detection accuracy. The proposed approach allows the detection of unknown IoT malware, which are the modified versions of known IoT malware. As the mean of conclusion-making concerning the malware presence, the set of machine learning classifiers was employed. The experimental results demonstrated the high accuracy of IoT malware detection. Conclusions. A new technique for IoT malware detection based on control flow graph analysis has been developed. It can detect IoT malware with high efficiency.

A Novel Insider Attack and Machine Learning Based Detection for the Internet of Things

Due to the widespread functional benefits, such as supporting internet connectivity, having high visibility and enabling easy connectivity between sensors, the Internet of Things (IoT) has become popular and used in many applications, such as for smart city, smart health, smart home, and smart vehicle realizations. These IoT-based systems contribute to both daily life and business, including sensitive and emergency situations. In general, the devices or sensors used in the IoT have very limited computational power, storage capacity, and communication capabilities, but they help to collect a large amount of data as well as maintain communication with the other devices in the network. Since most of the IoT devices have no physical security, and often are open to everyone via radio communication and via the internet, they are highly vulnerable to existing and emerging novel security attacks. Further, the IoT devices are usually integrated with the corporate networks; in this case, the impact of attacks will be much more significant than operating in isolation. Due to the constraints of the IoT devices, and the nature of their operation, existing security mechanisms are less effective for countering the attacks that are specific to the IoT-based systems. This article presents a new insider attack, named loophole attack , that exploits the vulnerabilities present in a widely used IPv6 routing protocol in IoT-based systems, called RPL (Routing over Low Power and Lossy Networks). To protect the IoT system from this insider attack, a machine learning based security mechanism is presented. The proposed attack has been implemented using a Contiki IoT operating system that runs on the Cooja simulator, and the impacts of the attack are analyzed. Evaluation on the collected network traffic data demonstrates that the machine learning based approaches, along with the proposed features, help to accurately detect the insider attack from the network traffic data.

A Guideline on Pseudorandom Number Generation (PRNG) in the IoT

Random numbers are an essential input to many functions on the Internet of Things (IoT). Common use cases of randomness range from low-level packet transmission to advanced algorithms of artificial intelligence as well as security and trust, which heavily rely on unpredictable random sources. In the constrained IoT, though, unpredictable random sources are a challenging desire due to limited resources, deterministic real-time operations, and frequent lack of a user interface. In this article, we revisit the generation of randomness from the perspective of an IoT operating system (OS) that needs to support general purpose or crypto-secure random numbers. We analyze the potential attack surface, derive common requirements, and discuss the potentials and shortcomings of current IoT OSs. A systematic evaluation of current IoT hardware components and popular software generators based on well-established test suits and on experiments for measuring performance give rise to a set of clear recommendations on how to build such a random subsystem and which generators to use.

The Presence, Trends, and Causes of Security Vulnerabilities in Operating Systems of IoT's Low-End Devices.

Internet of Things Operating Systems (IoT OSs) run, manage and control IoT devices. Therefore, it is important to secure the source code for IoT OSs, especially if they are deployed on devices used for human care and safety. In this paper, we report the results of our investigations of the security status and the presence of security vulnerabilities in the source code of the most popular open source IoT OSs. Through this research, three Static Analysis Tools (Cppcheck, Flawfinder and RATS) were used to examine the code of sixteen different releases of four different C/C++ IoT OSs, with 48 examinations, regarding the presence of vulnerabilities from the Common Weakness Enumeration (CWE). The examination reveals that IoT OS code still suffers from errors that lead to security vulnerabilities and increase the opportunity of security breaches. The total number of errors in IoT OSs is increasing from version to the next, while error density, i.e., errors per 1K of physical Source Lines of Code (SLOC) is decreasing chronologically for all IoT Oss, with few exceptions. The most prevalent vulnerabilities in IoT OS source code were CWE-561, CWE-398 and CWE-563 according to Cppcheck, (CWE-119!/CWE-120), CWE-120 and CWE-126 according to Flawfinder, and CWE-119, CWE-120 and CWE-134 according to RATS. Additionally, the CodeScene tool was used to investigate the development of the evolutionary properties of IoT OSs and the relationship between them and the presence of IoT OS vulnerabilities. CodeScene reveals strong positive correlation between the total number of security errors within IoT OSs and SLOC, as well as strong negative correlation between the total number of security errors and Code Health. CodeScene also indicates strong positive correlation between security error density (errors per 1K SLOC) and the presence of hotspots (frequency of code changes and code complexity), as well as strong negative correlation between security error density and the Qualitative Team Experience, which is a measure of the experience of the IoT OS developers.

Formal Verification of a Hybrid IoT Operating System Model

The Internet of Things (IoT) is becoming an increasingly common paradigm. As IoT usage scenarios have increased, many challenges in IoT operating systems’ safety and adaptability have remained. According to the programming model, IoT operating systems can be categorized into three types: multithreading, event-driven, and hybrid. Different operating system models are applied in different scenarios depending on the real-time requirements or resource richness. The safety of IoT operating systems is critical; hence, formal verification is an important method of detecting potential vulnerabilities and providing safety guarantees. This paper proposes a hybrid model for an IoT operating system and employs the Event-B method for modeling and verification. We rewrite the requirements and divide the Event-Bus hybrid operating system model into eight levels for refinement. The safety and liveness properties of Event-Bus are guaranteed by generating and proving the proof obligations at each model level. A large proportion of the proof obligations (91%) are automatically proven on the Rodin platform to simplify the development process.

Vision, Challenges and Future Perspectives of Low Constrained Devices IOT Operating Systems: A Systematic Mapping Review

Now the far-fetched reality has become true with the prominence of IOT (Internet of Things) technology. Various individual devices get connected with each other to establish communication. These devices are built on a microcontroller which is responsible to receive and send information. These devices are very small and appropriate Operating Systems are required on the basis of particular device architecture, scheduling methods, network technologies and programming models. IOT Operating Systems are enormously facilitating low constrained devices to deliver their throughput efficiently in a timely manner. This concept helped a lot in emergence of IOT, which has translated our physical world into a digital cyber world. IOT devices consumes less power, less memory and less energy, therefore they need appropriate Operating Systems to act as interfaces. Low constrained Operating Systems are especially designed to provide support to these low constrained devices. Many researches have been conducted to discuss Operating Systems for these low constrained devices. In this study, capsulization of Internet of Things and its building blocks, architecture of IOT Operating System and network stack architecture of state-of -the art IOT Operating Systems such as Contiki, Tiny OS, Free RTOS, RIOT, Zephyr and Mbed OS is investigated. Moreover this, detailed overview of related work is presented with the comparative analysis of this study with the existing surveys. In addition, open research areas are discussed with recommendations.

Vision, Challenges and Future Perspectives of Low Constrained Devices IOT Operating Systems: A Systematic Mapping Review

Now the far-fetched reality has become true with the prominence of IOT (Internet of Things) technology. Various individual devices get connected with each other to establish communication. These devices are built on a microcontroller which is responsible to receive and send information. These devices are very small and appropriate Operating Systems are required on the basis of particular device architecture, scheduling methods, network technologies and programming models. IOT Operating Systems are enormously facilitating low constrained devices to deliver their throughput efficiently in a timely manner. This concept helped a lot in emergence of IOT, which has translated our physical world into a digital cyber world. IOT devices consumes less power, less memory and less energy, therefore they need appropriate Operating Systems to act as interfaces. Low constrained Operating Systems are especially designed to provide support to these low constrained devices. Many researches have been conducted to discuss Operating Systems for these low constrained devices. In this study, capsulization of Internet of Things and its building blocks, architecture of IOT Operating System and network stack architecture of state-of -the art IOT Operating Systems such as Contiki, Tiny OS, Free RTOS, RIOT, Zephyr and Mbed OS is investigated. Moreover this, detailed overview of related work is presented with the comparative analysis of this study with the existing surveys. In addition, open research areas are discussed with recommendations.

Reliable Real-Time Operating System for IoT Devices

Internet of Things (IoT) technologies have so deeply penetrated our daily lives that IoT devices have been everywhere. Not only do IoT devices provide basic means to convenient living but they also offer intelligent services tailored to diverse user needs. For instance, beyond remotely turning ON and OFF small gadgets, such as lamps and switches, users can now control appliances via smartphones and also interact with them verbally. In the backdrop of multifarious, complex IoT applications, their reliability becomes of paramount importance. This article focuses on achieving user-level reliability in an IoT operating system (OS) that executes both real-time (RT) and non-real-time (NRT) tasks concurrently. For example, smart appliances typically run RT tasks such as a motor controller, as well as NRT tasks such as Wi-Fi-based network functions, concurrently. In general, RT tasks are critical and should be protected from error-prone tasks. Otherwise, a motor controller failure may cause appliances to malfunction. To this end, we propose a novel, reliable IoT OS, called TizenRT that achieves user-level reliability through two key features, namely, fault isolation and fast recovery , while maintaining RT constraints. Our evaluation shows that TizenRT can isolate faulty tasks in memory space, while guaranteeing normal RT tasks to complete their missions within a required time, e.g., 50 $\mu \text{s}$ . Moreover, corrupted tasks can be recovered within a few milliseconds, e.g., 10 ms, without the need for a system reboot. This makes TizenRT a preferred choice for mission-critical IoT applications.

Enhancing Routing Security in IoT: Performance Evaluation of RPL’s Secure Mode Under Attacks

As the Routing Protocol for Low Power and Lossy Networks (RPL) became the standard for routing in the Internet of Things (IoT) networks, many researchers had investigated the security aspects of this protocol. However, no work (to the best of our knowledge) has investigated the use of the security mechanisms included in RPL standard, mainly because there was no implementation for these features in any IoT operating systems yet. A partial implementation of RPL security mechanisms was presented recently for the Contiki operating system (by Perazzo et al.), which provided us with an opportunity to examine RPL security mechanisms. In this paper, we investigate the effects and challenges of using RPL security mechanisms under common routing attacks. First, a comparison of RPL performance, with and without its security mechanisms, under four routing attacks (Blackhole, Selective-Forward, Neighbor, and Wormhole attacks) is conducted using several metrics (e.g., average data packet delivery rate, average data packet delay, average power consumption, etc.). This comparison is performed using two commonly used Radio Duty-Cycle protocols. Secondly, and based on the observations from this comparison, we propose two techniques that could reduce the effects of such attacks, without having added security mechanisms for RPL. An evaluation of these techniques shows improved performance of RPL under the investigated attacks, except for the Wormhole attack.

RPL routing protocol performance under sinkhole and selective forwarding attack: experimental and simulated evaluation

To make possible dream of connecting 30 billion smart devices assessable from anywhere, anytime and to fuel the engine growth of Internet of things (IoT) both in terms of physical and virtual things, Internet Engineering Task Force (IETF) came up with a concept of 6LoWPAN possessing characteristics like low power, bandwidth and cost. To bridge the routing gap and to collaborate between low power private area network and the outside world, IETF ROLL group proposed IPv6 based lightweight standard RPL (Routing protocol for low power and lossy networks). Due to large chunks of random data generated on daily basis security either externally or internally always remain bigger threat which may lead to devastation and eventually degrades the quality of service parameters affecting network resources. This paper evaluates and compare the effect of internal attacks like sinkhole and selective forwarding attacks on routing protocol for low power and lossy network topology. Widely known IoT operating system Contiki and Cooja as the simulator are used to analyse different consequences on low power and lossy network.

IoT Operating Systems: Perspective, Availability and Proposed New Architecture

Internet of Things (IoT) is penetrating our lives faster than earlier due to the fact that it can create valuable real time data without any human interference. As there is no human involved, data generation is certain irrespective of any situation. The accuracy depends upon the sensors used in the implementation. Operating System is a basic and important part of any computational system, so is the case with IoT. However, IoT is still in its infancy, proper standards are yet to be developed and formalized. There are many IoT Operating Systems available in the market, both open source and closed. Users select from the available OS as per their suitability and requirements. However, there is no standard Operating System which can suit all IoT installations/designs. This paper discusses the main features of existing IoT Operating Systems and parameters suitable for IoT Operating Systems. While using these existing Operating Systems, we have come across many issues, both technical and management. These issues are discussed in this paper with utmost care and we have tried to address the users' concern with these. Based on these issues, we tried to derive revised architecture of IoT Operating System which can take care of these issues if they are implemented. We have taken into account the existing architecture of Contiki and RIOT OS while deriving revised architecture. Since IoT is slowly but surely spreading its wings to industrial applications, security is utmost important. Many security issues are still to be addressed by existing Operating Systems; hence, our main concern is to address such issues.

The Internet of Things (IoT): a survey of techniques, operating systems, and trends

PurposeInternet has changed radically in the way people interact in the virtual world, in their careers or social relationships. IoT technology has added a new vision to this process by enabling connections between smart objects and humans, and also between smart objects themselves, which leads to anything, anytime, anywhere, and any media communications. IoT allows objects to physically see, hear, think, and perform tasks by making them talk to each other, share information and coordinate decisions. To enable the vision of IoT, it utilizes technologies such as ubiquitous computing, context awareness, RFID, WSN, embedded devices, CPS, communication technologies, and internet protocols. IoT is considered to be the future internet, which is significantly different from the Internet we use today. The purpose of this paper is to provide up-to-date literature on trends of IoT research which is driven by the need for convergence of several interdisciplinary technologies and new applications.Design/methodology/approachA comprehensive IoT literature review has been performed in this paper as a survey. The survey starts by providing an overview of IoT concepts, visions and evolutions. IoT architectures are also explored. Then, the most important components of IoT are discussed including a thorough discussion of IoT operating systems such as Tiny OS, Contiki OS, FreeRTOS, and RIOT. A review of IoT applications is also presented in this paper and finally, IoT challenges that can be recently encountered by researchers are introduced.FindingsStudies of IoT literature and projects show the disproportionate importance of technology in IoT projects, which are often driven by technological interventions rather than innovation in the business model. There are a number of serious concerns about the dangers of IoT growth, particularly in the areas of privacy and security; hence, industry and government began addressing these concerns. At the end, what makes IoT exciting is that we do not yet know the exact use cases which would have the ability to significantly influence our lives.Originality/valueThis survey provides a comprehensive literature review on IoT techniques, operating systems and trends.

Internet of Things (IoT) Operating Systems Management: Opportunities, Challenges, and Solution

Internet of Things (IoT) is rapidly growing and contributing drastically to improve the quality of life. Immense technological innovations and growth is a key factor in IoT advancements. Readily available low cost IoT hardware is essential for continuous adaptation of IoT. Advancements in IoT Operating System (OS) to support these newly developed IoT hardware along with the recent standards and techniques for all the communication layers are the way forward. The variety of IoT OS availability demands to support interoperability that requires to follow standard set of rules for development and protocol functionalities to support heterogeneous deployment scenarios. IoT requires to be intelligent to self-adapt according to the network conditions. In this paper, we present brief overview of different IoT OSs, supported hardware, and future research directions. Therein, we provide overview of the accepted papers in our Special Issue on IoT OS management: opportunities, challenges, and solution. Finally, we conclude the manuscript.

Process Management in IoT Operating Systems: Cross-Influence between Processing and Communication Tasks in End-Devices.

The emergence and spread of Internet of Things (IoT) technologies along with the edge computing paradigm has led to an increase in the computational load on sensor end-devices. These devices are now expected to provide high-level information instead of just raw sensor measurements. Therefore, the processing tasks must share the processor time with the communication tasks, and both of them may have strict timing constraints. In this work, we present an empirical study, from the edge computing perspective, of the process management carried out by an IoT Operating System (OS), showing the cross-influence between the processing and communication tasks in end-devices. We have conducted multiple tests in two real scenarios with a specific OS and a set of wireless protocols. In these tests, we have varied the processing and communication tasks timing parameters, as well as their assigned priority levels. The results obtained from these tests demonstrate that there is a close relationship between the characteristics of the processing tasks and the communication performance, especially when the processing computational load is high. In addition, these results also show that the computational load is not the only factor responsible for the communication performance degradation, as the relationship between the processing tasks and the communication protocols timing parameters also plays a role. These conclusions should be taken into account for future OSs and protocol developments.

Comprehensive survey of the IoT open‐source OSs

The Internet of things (IoT) has attracted a great deal of research and industry attention recently and is envisaged to support diverse emerging domains including smart cities, health informatics, and smart sensory platforms. Operating system (OS) support for IoT plays a pivotal role in developing scalable and interoperable applications that are reliable and efficient. IoT is implemented by both high-end and low-end devices that require OSs. Recently, the authors have witnessed a diversity of OSs emerging into the IoT environment to facilitate IoT deployments and developments. In this study, they present a comprehensive overview of the common and existing open-source OSs for IoT. Each OS is described in detail based on a set of designing and developmental aspects that they established. These aspects include architecture and kernel, programming model, scheduling, memory management, networking protocols support, simulator support, security, power consumption, and support for multimedia. They present a taxonomy of the current IoT open-source OSs. The objective of this survey is to provide a well-structured guide to developers and researchers to determine the most appropriate OS for each specific IoT devices/applications based on their functional and non-functional requirements. They remark that this is the first such tutorial style paper on IoT OSs.

VITAL-OS: An Open Source IoT Operating System for Smart Cities

The advent of the Internet of Things (IoT) has lead to increased possibilities for novel human-centric services in smart cities. Nevertheless, existing IoT platforms for smart cities do not provide the means for integrating data and services from different IoT deployments, which is a prerequisite for several smart city applications such as holistic urban planning, tracking of city-wide APIs and integration of deployments at the level of neighbourhoods or communities in the cities. Hence, smart city projects tend to be fragmented, leading to disaggregated application silos. In order to alleviate this fragmentation and enable more integrated applications, there is a need to enable city authorities and application developers to see IoT systems as part of an integrated whole, allowing optimal use of existing infrastructure and achieving more than what these independent systems can do in isolation. This article presents the VITAL-OS, an IoT operating system for smart cities that facilitates access and control of the city infrastructure, resources and sensor networks, with abstraction and tools that can additionally be used by developers to create new applications using available data to empower citizens. VITAL-OS facilitates the semantic interoperability of diverse vertical IoT deployments in smart cities and enables integrated management of IoT resources as well as rapid development of cross-platform and cross-context IoT applications for smart cities. In this way, VITAL-OS provides a unique value proposition when compared to conventional IoT platforms for smart cities.

Internal Interface Diversification as a Security Measure in Sensor Networks

More actuator and sensor devices are connected to the Internet of Things (IoT) every day, and the network keeps growing, while software security of the devices is often incomplete. Sensor networks and the IoT in general currently cover a large number of devices with an identical internal interface structure. By diversifying the internal interfaces, the interfaces on each node of the network are made unique, and it is possible to break the software monoculture of easily exploitable identical systems. This paper proposes internal interface diversification as a security measure for sensor networks. We conduct a study on diversifiable internal interfaces in 20 IoT operating systems. We also present two proof-of-concept implementations and perform experiments to gauge the feasibility in the IoT environment. Internal interface diversification has practical limitations, and not all IoT operating systems have that many diversifiable interfaces. However, because of low resource requirements, compatibility with other security measures and wide applicability to several interfaces, we believe internal interface diversification is a promising and effective approach for securing nodes in sensor networks.