Secure Internet Of Things Systems Research Articles

A Security-Enabled Safety Assurance Framework for IoT-Based Smart Homes

The exponential growth of the Internet of Things (IoT) has paved the way for safety-critical cyber-physical systems to enter our everyday activities. While such systems have changed the way of our life, they brought new challenges that can adversely affect our life and the environment. Safety and security are two such challenges that can hamper the widespread adoption of new IoT applications. Due to a large number of connected devices and their ability to control critical physical assets, intended attacks on them and/or unintended failure events such as mechanical failure of devices, communication failure, and unforeseen bad interactions between connected devices may cause an IoT-based system to enter into unsafe and dangerous physical states. By considering the importance of safety and security of IoT systems, in this article, we present a security-enabled safety monitoring framework for IoT-based systems. In the proposed framework, we utilize design-time system analysis to create an executable monitoring model that enables run-time safety assurance provision for a system via collecting and analyzing operational data and evidence to determine the safety status of the system and then taking appropriate actions and securely communicating the safety status and recommended actions to the system users to minimize the risk of the system entering into an unsafe state.

Managing Information Security Risks in the Age of IoT

The advent of the Internet of Things (IoT) has led to the proliferation of connected devices, creating numerous security challenges. With billions of devices generating vast amounts of data, managing information security risks in the age of IoT has become increasingly complex. Traditional security approaches are not sufficient to mitigate the risks posed by IoT devices. Machine learning (ML) provides a promising approach to enhance the security of IoT systems. This paper proposes a machine learning approach for managing information security risks in the age of IoT. The proposed approach utilizes ML algorithms to identify and mitigate security threats in IoT systems. The approach involves collecting and analyzing data from IoT devices, and applying ML algorithms to detect patterns and anomalies that may indicate security threats. The ML algorithms are trained using both supervised and unsupervised learning techniques to enable them to identify known and unknown threats. The paper describes a case study in which the proposed approach is applied to an IoT system for home security. The results demonstrate that the ML approach can effectively detect security threats in the IoT system and mitigate them in real-time.

Comparative study of the Security Analysis of IoT systems using attack trees algorithm

The Internet of Things (IoT) is a rapidly evolving environment that allows users to use and control a wide variety of connected objects. The 20 billion IoT devices that will be employed by 2020 are only the top of the iceberg. According to IDC, the overall amount of connected devices will rise to 41.6 billion over the next five years, producing over 80 Zettabytes of data by 2025 which will impact environment severely. These connected environments increase the attack surface of a system since; the risks are multiplied by the number of connected devices. These devices are responsible for more or less critical tasks, and can therefore be the target of users malicious, in this paper we present a methodology to evaluate the security of IoT systems. We propose a way to represent IoT systems, coupled with attack trees in order to assess the chances of success of an attack on a given system.

Machine-Learning-Based Attestation for the Internet of Things Using Memory Traces

The advent of 4G and 5G mobile networks has made the Internet of Things (IoT) devices an essential part of smart nation drives. Firmware integrity is crucial to the security of IoT systems. Most of the existing techniques for firmware attestation require a legitimate copy of an IoT device’s firmware. However, firmware is considered an intellectual property (IP) of the manufacturer and may not be available. To solve this issue, this article proposes a software-based attestation technique where remote verifiers use machine learning (ML) classifiers on an IoT device’s memory dump to verify the integrity of an IoT device’s internal state. The experimental results from an actual prototype show that the proposed technique not only successfully detects attacks with high accuracy but also results in about 96% lower latency as compared to existing techniques. All this is achieved with high availability, low computational complexity, and without requiring a legitimate copy of the device’s original firmware.

IoT Best Practices and their components: A Systematic Literature Review

Internet of Things (IoT) is a technology that consists of interconnected entities (intelligent physical objects, services and software systems) via the Internet, which serve automatically and in a coordinated way in data management to achieve greater efficiency and productivity in industrial processes and contribute to improve the quality of people life. As the use of devices grows, so do the challenges for creating secure, operable, scalable and cost-effective IoT systems. The specialized literature shows interest in the application of best practices for the construction of IoT systems and so improving the compatibility of hardware and software systems, communication problems, security and privacy. This paper searchs in indexed journals in order to identify best practices and their current components to create IoT systems. The study applies a systematic literature review method implemented in six phases (planning, primary search, preliminary selection, selection, data extraction and analysis) that allows information to be extracted consistently. We obtain 97 papers applying the method to carry out the analysis. The results show there are different proposals that use some mechanism of knowledge representation for the creation or improvement IoT systems. The paper is a theoretical basis for creating better IoT systems in organizations and reducing costs.

Hybrid lightweight cryptography with attribute-based encryption standard for secure and scalable IoT system

ABSTRACT Internet of Things (IoT) devices require lower power consumption with higher security, which can be achieved by using lightweight cryptography (LWC) approaches. Attribute-based encryption (ABE) provides a fine-grained access control policy over encrypted data, making it useful in IoT-based cloud storage for allowed data protection. However, the conventional ABE approaches resulted in poor security performance against various attacks in the IoT environment. So, in this paper, the LWC-ABE method is proposed to enhance the security performance against various attacks in the IoT environment. The proposed LWC-ABE contains only multiple trusted authority environments, which is a bottleneck in IoT servers and IoT devices. The proposed LWC-ABE method supports high expressiveness, access policy updates, large attribute domains, and white box traceability properties. The simulation results shows that the proposed LWC-ABE resulted in reduced encryption and decryption times for multi users, different message sizes scenarios as compared to conventional approaches. The numerical outcomes of the proposed method are much better based on performance of encryption and decryption times as 0.000835 and 0.000310 respectively.

DDoS Attack Prevention for Internet of Thing Devices Using Ethereum Blockchain Technology.

The Internet of Things (IoT) has widely expanded due to its advantages in enhancing the business, industrial, and social ecosystems. Nevertheless, IoT infrastructure is susceptible to several cyber-attacks due to the endpoint devices’ restrictions in computation, storage, and communication capacity. As such, distributed denial-of-service (DDoS) attacks pose a serious threat to the security of the IoT. Attackers can easily utilize IoT devices as part of botnets to launch DDoS attacks by taking advantage of their flaws. This paper proposes an Ethereum blockchain model to detect and prevent DDoS attacks against IoT systems. Additionally, the proposed system can be used to resolve the single points of failure (dependencies on third parties) and privacy and security in IoT systems. First, we propose implementing a decentralized platform in place of current centralized system solutions to prevent DDoS attacks on IoT devices at the application layer by authenticating and verifying these devices. Second, we suggest tracing and recording the IP address of malicious devices inside the blockchain to prevent them from connecting and communicating with the IoT networks. The system performance has been evaluated by performing 100 experiments to evaluate the time taken by the authentication process. The proposed system highlights two messages with a time of 0.012 ms: the first is the request transmitted from the IoT follower device to join the blockchain, and the second is the blockchain response. The experimental evaluation demonstrated the superiority of our system because there are fewer I/O operations in the proposed system than in other related works, and thus it runs substantially faster.

An Efficient Spam Detection Technique for IoT Devices Using Machine Learning

Abstract: The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked over wired or wireless channel for data transmission. The volume of data released from these devices will increase many-fold in the years to come. In such an environment, machine learning algorithms can play an important role in ensuring security and authorization based on biotechnology, anomalous detection to improve the usability and security of IoT systems. On the other hand, attackers often view learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from these, in this project, we propose the security of the IoT devices by detecting spam using machine learning. In this framework, five machine learning models are evaluated using various metrics with a large collection of inputs features sets. Each model computes a spam score by considering the refined input features. This score depicts the trustworthiness of IoT device under various parameters. The results obtained proves the effectiveness of the proposed scheme in comparison to the other existing schemes.

Safety, Security and Privacy in Machine Learning Based Internet of Things

Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems.

A Systematic Review of Fault Injection Attacks on IoT Systems

The field of the Internet of Things (IoT) is growing at a breakneck pace and its applications are becoming increasingly sophisticated with time. Fault injection attacks on IoT systems are aimed at altering software behavior by introducing faults into the hardware devices of the system. Attackers introduce glitches into hardware components, such as the clock generator, microcontroller, and voltage source, which can affect software functioning, causing it to misbehave. The methods proposed in the literature to handle fault injection attacks on IoT systems vary from hardware-based attack detection using system-level properties to analyzing the IoT software for vulnerabilities against fault injection attacks. This paper provides a systematic review of the various techniques proposed in the literature to counter fault injection attacks at both the system level and the software level to identify their limitations and propose solutions to address them. Hybrid attack detection methods at the software level are proposed to enhance the security of IoT systems against fault injection attacks. Solutions to the identified limitations are suggested using machine learning, dynamic code instrumentation tools, hardware emulation platforms, and concepts from the software testing domain. Future research possibilities, such as the use of software fault injection tools and supervised machine learning for attack detection at the software level, are investigated.

A Secure IoT and Cloud Computing-Enabled e-Health Management System

Utilization of the Internet of Things (IoT) technology is virtually ubiquitous across various life disciplines. When the IoT is integrated into e-health system to enable more real-time on-demand services, it brings significant convenience to the physicians and patients. However, the risk of encountering unreliable information and potential security threat is promptly raised at the same time. Therefore, in this research, we establish a secure IoT and cloud computing-enabled e-health management system in which a distinctive authentication scheme is adopted. The proposed system ensures all major security requirements, such as confidentiality, entity anonymity, repudiation tracking, data integrity, and innate resistance to man-in-the-middle, location spoofing, and replay attacks. Moreover, our system can effectively be executed on low-powered processing units and simultaneously guarantees greater security than historically available alternatives.

On Wide-Area IoT Networks, Lightweight Security and Their Applications—A Practical Review

The Internet of Things (IoT) allows users to collect sensor data, control devices, and analyze collected data over the Internet. IoT devices are located in diverse environments and support many applications. To protect IoT systems from cyber threats, Confidentiality, Integrity, and Authentication—the CIA triad—must be supported. However, IoT devices have limited energy and computational resources. Lightweight encryption algorithms have been proposed for IoT, and have been reviewed by previous studies. Some cover communication protocols, while others cover lightweight security or review the challenges in implementing a secure IoT system. The aim of this literature review is to combine the first two topics: communication protocols and lightweight security. They will be approached from a practitioner’s standpoint. Several applications are provided that help readers with a minor background in security to understand these technologies and which elements of the CIA triad have more priority. This paper describes wide-area IoT networks, such as LoRAWAN, Sigfox, and NB-IoT, and their security. It also describes applications throughout the world, and how to enhance their security by implementing emerging lightweight security—specifically, approaches to make well-known ciphers such as Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) more lightweight.

An Efficient Framework for Detection and Classification of IoT Botnet Traffic

The Internet of Things (IoT) has become an integral requirement to equip common life. According to IDC, the number of IoT devices may increase exponentially up to a trillion in near future. Thus, their cyberspace having inherent vulnerabilities leads to various possible serious cyber-attacks. So, the security of IoT systems becomes the prime concern for its consumers and businesses. Therefore, to enhance the reliability of IoT security systems, a better and real-time approach is required. For this purpose, the creation of a real-time dataset is essential for IoT traffic analysis. In this paper, the experimental testbed has been devised for the generation of a real-time dataset using the IoT botnet traffic in which each of the bots consists of several possible attacks. Besides, an extensive comparative study of the proposed dataset and existing datasets are done using popular Machine Learning (ML) techniques to show its relevance in the real-time scenario.

An Efficient Spam Detection Technique for IoT Devices Using Machine Learning

Abstract: The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked over wired or wireless channel for data transmission. IoT has grown rapidly over the past decade with more than 25 billion devices are expected to be connected by 2020. The volume of data released from these devices will increase many-fold in the years to come. In addition to an increased volume, the IoT devices produces a large amount of data with a number of different modalities having varying data quality defined by its speed in terms of time and position dependency. In such an environment, machine learning algorithms can play an important role in ensuring security and authorization based on biotechnology, anomalous detection to improve the usability and security of IoT systems. On the other hand, attackers often view learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from these, in this paper, we propose the security of the IoT devices by detecting spam using machine learning. Keywords: SVM - Support Vector Machine , KNN - K-NN Algorithm , NNM - Neural Network Model, RFE - Recursive Feature Elemination.

Critical analysis of the layered and systematic approaches for understanding IoT security threats and challenges

As an emerging technology, the Internet of Things (IoT) is revolutionizing the global economy and society. The wide adoption of IoT opens up new security and privacy challenges as well. Building efficient, secure IoT systems need a thorough understanding of the potential threats and vulnerabilities of the system. The non-uniformity in the presentation of IoT architecture poses a significant challenge in understanding security issues. Motivated by this, we present a systematic study of substantial threats and attacks based on four primary elements of the IoT ecosystem: devices, internal network services, external network services, and users. Besides, we include a detailed study of IoT malware. We examine the major security requirements and challenges to confront the devised attack categories. We believe that an IoT ecosystem can be designed efficiently and securely by adhering to the proposed requirements, including identity management, access control, end to end communication security and trust management systems.

3-D Stochastic Geometry-Based Modeling and Performance Analysis of Efficient Security Enhancement Scheme for IoT Systems

Internet of Things (IoT) systems are becoming core building blocks for different services and applications supporting every day’s life. The heterogeneous nature of IoT devices and the complex use scenarios make it hard to build secure and private IoT systems. Physical-layer security (PLS) can lead to efficient solutions reducing the impact of the increasing security threats. In this work, we propose a new PLS-based IoT transmission scheme that offers a highly secured transmission probability, low-computational complexity, and reduced power consumption. We utilize 3-D stochastic geometry to model a more realistic IoT system and test our proposed scheme in different practical scenarios, where sensors, access points (APs), and eavesdroppers are randomly located in 3-D space. We focus on the system performance, in terms of secrecy outage probability (SOP) and secured successful transmission probability (SSTP), using tight closed-form expressions. An optimization problem is developed to deduce the optimal sensors’ transmit power, the APs’ density, and the maximum number of transmission tentative, when maximizing the SSTP. The proposed scheme outperforms the baseline retransmission scheme, in terms of SOP and SSTP based on analytical and simulation results.

Enhancing Secrecy Performance of Cooperative NOMA-Based IoT Networks via Multiantenna-Aided Artificial Noise

With the increasing demand for security in many sectors, such as defense and health systems, developing secure Internet of Things (IoT) networks is a matter of great urgency. Looking at a potential solution for secure IoT systems, we investigate the physical layer security of cooperative nonorthogonal multiple access (NOMA) systems. After decoding information signal, the idea that a strong IoT node can serve as a relay node for other weak IoT nodes in enhancing their signal reception reliability, is known as cooperative NOMA. We consider both single-antenna and multiantenna aided transmission scenarios, where the base station (BS) communicates with two IoT nodes of different strengths. In the multiantenna scenario, artificial noise (AN) is generated at the BS and the strong IoT node for improving the security of the system. In order to characterize the secrecy performance, we derive new exact expressions of the security outage probability for both the IoT nodes under both the single-antenna and multiantenna aided scenarios. For the single-antenna scenario, we show that the power optimization at the BS and the strong IoT node can enhance the secrecy performance to some extent. For this case, we further study the secrecy diversity order of the overall system, which is mainly determined by the IoT node with the worse channel condition. For the multiantenna scenario, we derive the asymptotic secrecy outage probability (SOP) when the number of antennas tends to infinity. Extensive simulations have been conducted to verify the accuracy and effectiveness of the proposed analytical derivations. The presented results verify that the security performance of the cooperative NOMA-based IoT network can be improved through an appropriate power control scheme and by generating AN at the BS and the strong IoT node. The simulation results further illustrate that the asymptotic SOP is close to the exact one.

IoT Security Using AES Encryption Technology based ESP32 Platform

The Internet of Things (IoT) is one of the most important modern technologies that have attracted the most interesting areas of life, whether industrial, academic, or other, in recent years. The main goal is to integrate the physical world with the digital world through a seamless ecosystem, and this constitutes a new era for the Internet. This technology provides high commercial value to enterprises as it provides many opportunities in many applications such as energy, health, and other sectors. However, this technology suffers from many security problems, as it is considered the biggest challenge due to its complex environment and the limited resources of its devices. There is a lot of research to find successful security solutions in IoT, in this research, a proposed solution to secure IoT systems using Advanced Encryption Standard (AES) technology is achieved. Some sensors were linked as an example of the Internet of Things. The data is received by the card created and developed by Espressif Systems (ESP32) module, where its encrypted then sends to the internet site through an authorized person to be received from anywhere, and it is also possible to receive it via a published IP which is announced within the internal network of the ESP32 device module. The decryption part is proposed at last to find out the true values of the sensors. The proposed approach shows good secured and balanced results at the end

Physical Unclonable Function Based Identity Management for IoT with Blockchain

As a result of the increasingly pervasive deployment of the Internet of Things(IoT), the cybersecurity of IoT has already attracted more and more research efforts. Identity management is believed to be the fundamental keystone to build security mechanisms. The traditional centralized identity management scheme suffers from a single point of failure and identity forgery. A secure IoT system framework was proposed leveraging blockchain as the basic infrastructure with Physical Unclonable Functions (PUFs) identifying the sensors uniquely. We brought up a scheme to improve the identity authentication protocol. Experiments showed that our approach was more effective and secure against attacks.

A Study of Modelling IoT Security Systems with Unified Modelling Language (UML)

The Internet of Things (IoT) has emerged as a technology with the application in different areas. Hence, security is one of the major challenges that has the potential to stifle the growth of the IoT. In fact, IoT is vulnerable to several cyber attacks and needs challenging techniques to achieve its security. In this paper, the use of a UML (Unified Modelling Language) aims at modeling IoT systems in various views. The purpose of this study is to discuss the need for more modeling in terms of security. For this reason, this paper focuses on modeling security of IoT systems. The objective is to make a comparison in terms of layers by describing the IoT architecture and presenting its components. In other words, the research question is to look for the modeling of security in the IoT layers. There is no standard that takes into account the security of the IoT architecture, there are different proposals of IoT levels, which means that each author has his own vision and own proposition. Moreover, there is a lack of modelling languages for IoT security systems. The main interest of this study is to choose the layer on which we should be interested. The question then is as follows: “which is the layer whose modeling is relevant?” The obtained results were conclusive and provided the best insight into all the specifications of each layer of the IoT architecture studied.