Security Challenges In Internet Of Things Research Articles

DQ-NN and phantom routing for enhanced source location privacy for IoT under multiple source and destination

The Internet of Things (IoT) is now an essential component of our day-to-day lives. In any case, the association of various devices presents numerous security challenges in IoT. In some cases, ubiquitous data or traffic may be collected by certain smart devices which threatens the privacy of a source node location. To address this issue, a hybrid DL technique named Deep Q Learning Neural network (DQ-NN) is proposed for the Source Location Privacy (SLP) in IoT networks based on phantom routing. Here, an IoT network with multiple sources and destinations is considered first, and then the phantom node is chosen by analyzing neighbor list, energy, distance, and trust heterogeneity parameters. After that, multiple routes are created from the source node to the sink node via the phantom node. Finally, path selection is performed by the proposed DQ-NN. Moreover, DQ-NN is obtained by merging the Deep Q Learning Network (DQN) and Deep Neural Network (DNN). A simulation environment consisting of 150 nodes is created to study the effectiveness of performance and scalability. The proposed novel DQ-NN outperforms other existing algorithms, by recording a high network lifetime is 111.912, a safety period of 664970.7 m, an energy is 0.034 J, and a distance is 56.594 m.

IoT Security Challenges and Solutions for Data at Rest: A Systematic Literature Review

The rapid expansion of the Internet of Things (IoT) has significantly transformed both consumer and industrial domains, driving the urgent need for robust security measures to protect data at rest. SLR investigates into the challenges associated with securing IoT devices and data, exploring the limitations of existing security frameworks and the intricate requirements imposed by global data protection regulations such as GDPR. The researcher review current approaches, including privacy-by-design principles and the deployment of symmetrical data protection frameworks, as highlighted in recent studies. Through a comprehensive analysis of literature and existing technologies, we identify critical gaps in the protection strategies and propose enhanced methods for ensuring data security and privacy in IoT systems. The findings emphasize the role of developers in integrating privacy considerations early in the development process and the impact of regulatory complexities on the practical implementation of data protection measures. Furthermore, the paper evaluates innovative security solutions, such as full stack security architectures and adversarial training models, assessing their effectiveness in real-world applications. This study aims to provide a deeper understanding of the IoT security landscape and to suggest actionable strategies for improving data protection practices across the IoT ecosystem

PIoT-blockchain-enabled security framework for strengthening IoT device identity and data access

The Internet of Things (IoT) connects billions of devices, but concerns about data security and device identification have grown. This study introduces Proof of IoT (PIoT), a novel consensus method using blockchain-based smart contracts to ensure secure data transfer and device authentication in IoT networks. PIoT employs NuCypher-based security computations for authentication and data updating, ensuring only authorized parties access blockchain-recorded information. A reward-based filtering mechanism identifies malicious nodes, bolstering consensus integrity. Simulations demonstrate PIoT's superior performance compared to conventional methods, showing a 74.7% and 96.8% improvement over LPBFT and DAC4SH Consensus, respectively. PIoT enhances blockchain security under IoT by 15% compared to DAC4SH. Beyond facilitating secure IoT device communication, this approach mitigates various IoT attacks, offering a comprehensive solution to emerging IoT security challenges. ABBREVIATION: DAC4SH, Data access control scheme for smart home; DDoS, Distributed denial of service; DoS, Denial of Service; DTLS, Datagram Transport Layer Security; IoT, Internet of Things; LPBFT, Lightweight Particle Byzantine fault tolerance; MAC, Message authentication code; MITM, Man in the Middle; PBFT, Practical Byzantine Fault Tolerance; PIoT, Proof of IoT; TLS, Transport Layer Security

Theoretical study and analysis of advanced wireless sensor network techniques in Internet of Things (IoT)

Wireless sensor networks those based on the Internet of Things (IoT) have evolved fast in numerous areas in recent years. Devices and sensors may interact with each other without human involvement in the IoT network. An IoT-based system where people and things are connected by wired and wireless technology including Wireless Sensor Networks (WSNs), ZigBee, NFC, RFID, GPRS, LTE, and Bluetooth is now one of the most popular technical ideas. The potential of the Internet of Things (IoT) has received enormous attention from the corporate and scientific communities during the past 10 years. The IoT can provide a number of significant benefits to application spaces. Researchers may be able to show crucial facts about IoT-based smart environmental monitoring systems. IoT has faced a variety of challenges, including authentication, differentiating evidence, accessibility and security and protection. Sociotechnical trust systems have also been examined in the past in various studies (STTS). Existing smart settings are now dealing with serious IoT security, protection, and STTS challenges. Trust-the-board will be highlighted and characterized, as well as how it should be utilized in the Internet of Things. As a result, the potential benefits of data aggregation solutions that use less energy while increasing the organization's lifespan are enormous. IoT-WSN data aggregation methods and computations have been reimagined. Specifically, this article examines the writing with reference to the views of sensor systems administrators in order to preserve energy and gather data.

Blockchain for Enhancing IoT Privacy and Security

The technological advancements in the network technologies that deal with interconnected computing devices has led to the dramatic increase in the number of Internet of Things (IoT) devices. It promises to improve the convenience of our lives by leveraging to modernize the way day to day items are connected to various elements of existence by converting it to smart object. However, with the unprecedented convenience, accessibility, and efficiency, IoT has caused acute security and privacy threats in recent years, and it must be addressed properly. Due to the massive scale and distributed nature of IoT networks, overlooking these privacy and security issues will negatively impact many elements of our existence that involves our surroundings, and these effects will influence our own masses too. The national or jurisdictional boundaries are not restricted to the centralized cloud infrastructure that provides interconnectivity of IoT items, hence maintaining robustness, authentication and security becomes crucial. Recently, the development of blockchain technology is a plausible solution to offer such features. Security, authentication, and maintenance issues can be solved by the secure decentralization of blockchain and overcome the current IoT ecosystem's drawbacks. This paper highlights the IoT features and presents a IoT security and privacy problems are examined and classified in accordance with the IoT layered architecture. Further, we explore the IoT security and privacy challenges and examine blockchain technology towards a solution that help solve many of the IoT privacy and security issues

Innovative Machine Learning Model Design for Predictive IoT Security Attacks

The research focuses on designing a predictive model for Internet of Things (IoT) attack identification using historical IoT data from the Global Cyber Alliance’s (GCA) Automated IoT Defense Ecosystem (AIDE). This research goes into the design of an enhanced machinelearning model, to predict potential security breaches. The process involved a thorough data science lifecycle, overall data preprocessing, feature selection, and engineering. The study’s main objective is to design a model to classify IoT activities and events, distinctive among normal operations and indicators of potential cyber-attacks. The model design incorporates distinct features like command frequency, login success, geo-distance calculations, credentials tried, and protocol encodings to enhance predictive accuracy. The model uses algorithms like logistic regression and random forests to explore their efficacy in binary and multiclass classifications. The research emphasizes the critical role of the model’s capability to proactively address IoT security challenges. Offering early alerts is crucial in allowing timely countermeasures, herein strengthening IoT ecosystems against cyber threats. The model’s accuracy in predicting IoT attacks, reduces the likelihood of successful breaches, thus safeguarding sensitive data and infrastructure. Furthermore, it assists as a key tool for policymakers and security professionals, providing insight into occurring threat patterns and enabling the development of robust defense strategies.

Security in the Internet of Things (Iot): A Review, Block Chain Applications

The rise of smart technologies in various domains such as homes, cities, and other interconnected systems has propelled the Internet of Things (IoT) into a realm of immense influence, potential, and expansion. Cisco Inc. has forecasted a staggering 50 billion connected devices by 2020, underscoring the rapid proliferation of IoT. Despite this growth, a significant concern looms over the security of these IoT devices, as many are susceptible to hacking and compromise. Their limited computational, storage, and networking capabilities render them more vulnerable compared to traditional endpoint devices like smartphones, tablets, and computers.This paper examines and categorizes the major security challenges facing IoT devices. It reviews prevalent security issues within the layered architecture of IoT, encompassing networking, communication, and management protocols. The paper also delineates the security prerequisites for IoT and surveys current attacks, threats, and state-of-the-art solutions. Additionally, it presents a comparative analysis of IoT security challenges and existing solutions found in the literature.Furthermore, the paper delves into the potential of blockchain, the foundational technology behind bitcoin, as a pivotal tool for addressing numerous security issues in IoT. It discusses how blockchain can enhance security in IoT ecosystems. The paper concludes by outlining open research problems and ongoing challenges in IoT security, underscoring the need for continued innovation in this domain.

IoT Security Challenges and Emerging Solutions: A Comprehensive Review

The proliferation of Internet of Things (IoT) devices has revolutionized the way we interact with technology, embedding connectivity into every facet of modern life. However, this rapid growth has introduced a host of security challenges that threaten the integrity, privacy, and functionality of IoT ecosystems. This paper presents an abstract review of the complex landscape of IoT security challenges, ranging from vulnerabilities in devices to data breaches and sophisticated cyberattacks. Amid these challenges, emerging solutions offer promising avenues to fortify the security posture of IoT environments. Blockchain technology holds the potential to enhance data integrity and establish trust, while hardware-based security mechanisms bolster the resistance of devices against intrusions. Novel approaches such as AI-powered anomaly detection contribute to real-time threat identification, and privacy-preserving techniques strive to strike a balance between data utility and confidentiality. Additionally, the concept of zero trust architectures is transforming the fundamental approach to securing IoT networks. This paper not only underscores the gravity of IoT security challenges but also highlights the proactive measures that are shaping the future of IoT security. Through an exploration of case studies and a discussion of the inherent challenges and trade-offs, this review elucidates the delicate International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 09 | September - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM25662 | Page 2 balance between fortifying IoT systems and maintaining usability. As IoT continues its rapid evolution, the imperative to ensure robust security measures becomes paramount, necessitating collaborative efforts and innovative solutions to safeguard the promises of an interconnected world. Keywords: IoT,DDoS, blockchain, Cyberattacks

Engineering the advances of the artificial neural networks (ANNs) for the security requirements of Internet of Things: a systematic review

Internet of Things (IoT) driven systems have been sharply growing in the recent times but this evolution is hampered by cybersecurity threats like spoofing, denial of service (DoS), distributed denial of service (DDoS) attacks, intrusions, malwares, authentication problems or other fatal attacks. The impacts of these security threats can be diminished by providing protection towards the different IoT security features. Different technological solutions have been presented to cope with the vulnerabilities and providing overall security towards IoT systems operating in numerous environments. In order to attain the full-pledged security of any IoT-driven system the significant contribution presented by artificial neural networks (ANNs) is worthy to be highlighted. Therefore, a systematic approach is presented to unfold the efforts and approaches of ANNs towards the security challenges of IoT. This systematic literature review (SLR) is composed of three (3) research questions (RQs) such that in RQ1, the major focus is to identify security requirements or criteria that defines a full-pledge IoT system. This question also focusses on pinpointing the different types of ANNs approaches that are contributing towards IoT security. In RQ2, we highlighted and discussed the contributions of ANNs approaches for individual security requirement/feature in comprehensive and detailed fashion. In this question, we also determined the various models, frameworks, techniques and algorithms suggested by ANNs for the security advancements of IoT. In RQ3, different security mechanisms presented by ANNs especially towards intrusion detection system (IDS) in IoT along with their performances are comparatively discussed. In this research, 143 research papers have been used for analysis which are providing security solutions towards IoT security issues. A comprehensive and in-depth analysis of selected studies have been made to understand the current research gaps and future research works in this domain.

LIGHTWEIGHT CRYPTOGRAPHY METHOD IN THE INTERNET OF THINGS USING ELLIPTIC CURVE AND CROW SEARCH ALGORITHM

The Internet of Things (IoT) as an important technology consists of a heterogeneous and huge number of devices that generates an enormous amount of data in various applications. However, generating and transmitting huge amount of data in the IoT makes it crucial to implement a secure and safe data transmission scheme. Cryptography methods can secure the confidentiality, data integrity, access control, and authentication. Due to constrained resources in IoT devices, providing classical cryptography schemes isn’t efficient for IoT applications, so a lightweight cryptography scheme is one of the most important solutions to overcome security challenges in IoT. In this paper, a new security scheme called ECCCSASHA256 based on Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm (SHA-256) using Crow Search Algorithm (CSA) has been proposed for secure data transmission in IoT devices. The ECCCSASHA256 model uses the CSA for generating a private key to encode the elliptic curve. Furthermore, the proposed scheme uses SHA-256 model for hashing the incoming encoded data using ECC. The simulation results indicate that the average throughput of the proposed model was about 8.22% and 8.97% higher in encryption and 8.72% and 9.81% higher in decryption compared to 3DES&ECC&SHA-256 and RC4&ECC&SHA-256, respectively.

Security concerns over IoT routing using emerging technologies: A review

AbstractThe Internet of Things (IoT) provides a way to reach every hidden corner of the universe and provides valuable information for exploring the universe. With the advancement of hardware and computing technologies, IoT systems are developed based on systematically designed architectures. This paper discussed the routing protocol for low power lossy network (RPL) protocol's security vulnerabilities and proposed solutions. This review provides the main aspects of an IoT stack model with application, perception, and network layers. Then, the details and security concerns of the RPL protocol are given with the comparison of different frameworks. Different kinds of attacks, such as Wormhole, Sinkhole, and Denial of service attacks, are provided with the solution of defense‐based, threshold‐based, and trust‐supported based cryptography. Also, reviewed IoT security literature and focused on IoT security challenges and their solutions using emerging technologies like Machine Learning (ML), Blockchain, and Cloud Storage. A comparison and analysis of emerging technologies in the context of IoT security are also discussed. Due to the vulnerability of IoT devices, the scale of deployment, the attacks and suggested solutions are increased with scope and scale. In addition, related RPL‐based protocols are compared regarding energy efficiency, flexibility, scalability, robustness, and security. In the end, research challenges are identified, and future research direction for IoT security is suggested.

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website's content as a technical resource and reference.

Cloud and internet-of-things secure integration along with security concerns

<span lang="EN-US">Cloud computing is a new technology which refers to an infrastructure where both software and hardware application are operate for the network with the help of internet. Cloud computing provide these services with the help of rule know as you pay as you go on. Internet of things (IoT) is a new technology which is growing rapidly in the field of telecommunications. The aim of IoT devices is to connect all things around us to the Internet and thus provide us with smarter cities, intelligent homes and generally more comfortable lives. The combation of cloud computing and IoT devices make rapid development of both technologies. In this paper, we present information about IoT and cloud computing with a focus on the security issues of both technologies. Concluding we present the contribution of cloud computing to the IoT technology. Thus, it shows how the cloud computing technology improves the function of the IoT. Finally present the security challenges of both technologies IoT and cloud computing.</span>

Revolution in IoT with 5G Network

Abstract: Day by day Internet of Things (IoT) is gaining a huge popularity. With increase in popularity the technological advancement is required to fulfil its high data rate demand. The IoT devices will become primary need to next generation. The latest development of devices will build smaller but smart devices in field of IoT. With increase in demand of smart devices IoT architecture will become more complex. The 5G network will fulfil the need to these complex architectures. The 5G networks are expected to massively expand today’s IoT that can boost cellular operations, IoT security, and network challenges and drive the Internet future to the edge. This paper will review the revolution in IoT with introduction of 5G network along with its concept, use cases and security concerns.

Cryptography Algorithms for Enhancing IoT Security

In today's fast-paced world, a new technology paradigm known as the Internet of Things (IoT) is advancing every business. It provides communication between the digital and physical worlds, transforming the way people do business quickly. The IoT is a network of physical objects (things) equipped with sensors, intelligent networking, radio-frequency identification (RFID), and other technologies that communicate and exchange data with other systems and devices online. IoT applications are now significantly more prevalent worldwide. IoT is driving the real world to become more intelligent in several industries, including smart grids for electricity, intelligent traffic, and smart homes and buildings. As a result, predictions and research point to over 50 billion connected devices by 2020. It will be required to safeguard this growing volume of data during exchange because the amount of data being transmitted will be quite enormous. Data security is represented by cryptography, an essential tool (algorithms) in security, which is why many researchers are developing cryptographic algorithms to improve the Security of IoT. This survey paper introduced an overview of IoT technology, architecture, and applications and a detailed analysis comparing all cryptographic algorithms and their use in day-to-day life activities. This paper discusses lightweight block ciphers, stream ciphers, and hybrid ciphers. The report evaluates security algorithms, comparing performance and robustness with the computational complexity of these techniques. Finally, the survey presents IoT security challenges, threats, and attacks with their mitigation techniques.

An Intelligent Detection of Malicious Intrusions in IoT Based on Machine Learning and Deep Learning Techniques

The devices of the Internet of Things (IoT) are facing various types of attacks, and IoT applications present unique and new protection challenges. These security challenges in IoT must be addressed to avoid any potential attacks. Malicious intrusions in IoT devices are considered one of the most aspects required for IoT users in modern applications. Machine learning techniques are widely used for intelligent detection of malicious intrusions in IoT. This paper proposes an intelligent detection method of malicious intrusions in IoT systems that leverages effective classification of benign and malicious attacks. An ensemble approach combined with various machine learning algorithms and a deep learning technique, is used to detect anomalies and other malicious activities in IoT. For the consideration of the detection of malicious intrusions and anomalies in IoT devices, UNSW-NB15 dataset is used as one of the latest IoT datasets. In this research, malicious and normal intrusions in IoT devices are classified with the use of various models. %Moreover, improved results are provided and compared with CorrAuc [1] for training accuracies, cross-validation accuracies, execution time, precision, recall and F1 score.

Blockchain Technology: A Panacea for IoT Security Challenge

The Internet of Things (IoT) platforms, despite the wide range of application is not without loop holes of which cyberattackers can take advantage. In order to improve the platform's security while also increasing other features, it has been proposed that blockchain technology be implemented in any IoT system. However, while blockchain technology has many advantages, it is important to consider other options because they all have their own drawbacks that may not be ideal for every use case situation. IoT network devices have limited computer power, storage space, and bandwidth. As a result, these systems are easily prone to assault than other network connected devices, such PCs, cell phones and tablets. With focus on IoT security challenges and the countermeasures offered by the blockchain technology, consensus algorithm, data encryption and smart contracts were discovered to be the common and effective algorithm employed by the blockchain technology in securing Iot systems over time.

Investigating the IoT Security and Privacy Challenges: Summary and Recommendations

With the development of innovative technology transformation like cloud and Internet of Things (IoT), more technology companies are pursuing research in employing such innovations. Smart homes and cities are just two examples of the many systems and technologies that the IoT can endorse. IoT - based smart objects communicate with other parts, such as proxies, portable devices, as well as data collectors. Although these components help to tackle a number of societal issues and offer users new, cutting-edge services, their confined processing power makes them susceptible to well-known privacy and security attacks. This in turn highlights the demand for a strong technical as well as legislative foundation and asserts the significance of validity and reliability in IoT. This paper provides an insight of the IoT, security, as well as privacy challenges, and also discusses the recommendations for IoT solutions. Further we also highlighting some unresolved problems that require further study.

Internet of things based wireless sensor network: a review

Recently, Internet of Things (IoT) technologies are developing technology with a variety of applications. The Internet of Things (IoTs) is defined as a network of ordinary objects such as Internet TVs, smartphones, actuators and sensors that are smartly connected together to enable new types of communication between people and things as well as between things themselves. Wireless sensor networks (WSNs) play an important part in Internet of Things (IoT) technology. A contribution to wireless sensor networks and IoT applications is wireless sensor nodes’ construction with high-speed CPUs and low-power radio links. The IoT-based wireless Sensor network (WSN) is a game-changing smart monitoring solution. ZigBee standard is an important wireless sensor network (WSN) and Internet of Things (IoT) communication protocol in order to facilitate low-power, low-cost IoT applications and to handle numerous network topologies. This paper presented a review on the energy efficient and routing topologies of ZigBee WSN, applications of IoT enabled Wireless Sensor Network as well IoT WSN security challenges.

Strategies for Internet of Things data privacy and security using systematic review

The Internet of Things (IoT) now referend to as the Internet of Everything (IoE) has been in existence long before it was identified as a concept. It was introduced with the emergence of the Fourth Industrial Revolution and was aimed at improving people’s lives and economies across the globe by connecting physical items to the internet so they can be able to deliver specific services implicitly. The nature of IoT requires that all the systems ensure data privacy and security because much of data that is uploaded into and used by the system is personal and private. Thus, the aim of this research was to identify the tools and strategies that can be used for IoT data privacy and security while also providing a brief but intensive understanding of the concept of IoT and data privacy and security challenges faced by IoT systems. This qualitative research study utilised a pragmatic paradigm and data was collected and analysed using text-based secondary data sources and a PRISMA protocol through systematic review. A PRISMA flow diagram was utilised to assess the eligibility of the sources used for this research. The findings showed that hacking is a major challenge that affects IoT systems and that there are strategies that can be used to protect data such as authentication, encryption technology, and anonymisation amongst many. Additional findings found that the strategies have not yet been found effective, but standards have been set upon the results expected from them. The conclusion is that for the identified strategies to be proven effective, they must be implemented and tested in IoT systems, so further investigation can be conducted if they prove to be ineffective.