Reliable Internet Of Things Research Articles

IoT 가상환경 플랫폼에서의 무결성 보장 시스템

In this paper, we propose a system that improves the data integrity of IoT(Internet of Things) devices in the virtual environment by combining Hyperledger Indy and MQTT(Message Queuing Telemetry Transport). The system complements the limitations of the centralized system by realizing a DPKI(Decentralized Public Key Infrastructure) structure that utilizes a distributed network in publish-subscribe(pub/sub) pattern communication. Digital signature technology was applied to ensure the data integrity of IoT devices and communication scenarios between the four core components of the client, IoT device, broker, and blockchain, as well as a topic structure using a decentralized identifier to ensure safety in the virtual environment. We present a systematic method for transparent data exchange. To prove the performance of the proposed system, this paper conducted experiments on four scenarios and evaluated communication performance in a virtual environment. The experimental results confirmed that the proposed system provides a reliable IoT data communication structure in a virtual environment.

Trustworthy-Based Authentication Model with Intrusion Detection for IoT-Enabled Networks with Deep Learning Algorithm

In the burgeoning field of the Internet of Things (IoT), ensuring secure and trustworthy communication between devices is paramount. This paper proposes a novel Trustworthy-Based Authentication Model (TBAM) integrated with Intrusion Detection Systems (IDS) leveraging deep learning algorithms to secure IoT-enabled networks. The proposed model addresses the dual challenges of authenticating legitimate devices and detecting malicious intrusions. Specifically, we employ a Convolutional Neural Network (CNN) to analyse network traffic patterns for intrusion detection, leveraging its prowess in feature extraction and classification. Additionally, a Long Short-Term Memory (LSTM) network is utilized for continuous monitoring and anomaly detection, capturing temporal dependencies in data flows that are indicative of potential security threats. The authentication mechanism integrates a trust evaluation system that assigns trust scores to devices based on their behaviour, enhancing the model's capability to distinguish between trusted and malicious entities. Our extensive experiments on real-world IoT datasets demonstrate that the TBAM significantly outperforms traditional security models in terms of detection accuracy, false-positive rate, and computational efficiency. Specifically, our model achieves a detection accuracy of 98.7%, a false-positive rate of 1.2%, and a processing time reduction of 30% compared to baseline models. This work contributes a robust, scalable, and efficient solution to the pressing security concerns in IoT networks, paving the way for more secure and reliable IoT applications.

Enhancing Security of 5G-Enabled IoT Systems through Advanced Authentication Mechanisms: A Multifaceted Approach

The Internet of Things (IoT) has revolutionized communication among devices, offering unprecedented efficiency and convenience. However, the widespread adoption of IoT has raised significant security concerns, emphasizing the need for robust security measures. This study focuses on the crucial aspect of authentication within the layered architecture of IoT systems. Authentication is foundational to the architecture, ensuring the availability, security, and integrity of IoT services and data. The research evaluates the current state of IoT authentication techniques, highlighting limitations in conventional solutions. To address these shortcomings, an advanced authentication framework is proposed, incorporating cutting-edge technologies such as blockchain, artificial intelligence, and biometrics. The framework employs biometric data for a dynamic and adaptive authentication process, enhancing security and accuracy in user and device identification. Blockchain technology is integrated to establish a decentralized and tamper-resistant identity management system, reducing the risk of unauthorized access and data manipulation. Artificial intelligence continuously adapts authentication processes based on behavioural patterns, bolstering the system's resilience against evolving cyber threats. The study also discusses the practical application of the proposed authentication system, considering resource limitations in IoT devices. It provides insights into the efficiency, scalability, and interoperability of the suggested solution within various IoT ecosystems. The research contributes to the ongoing discourse on IoT security by thoroughly examining enhanced authentication procedures. Organizations can fortify their IoT deployments against a growing array of cyber threats by prioritizing advanced authentication within a layered design, fostering a more secure and reliable IoT ecosystem. Additionally, the study presents a comprehensive overview of the state-of-the-art security in IoT, exploring various designs, enabling technologies, and protocols. It delves into security challenges at each architectural tier, providing an in-depth analysis of attack taxonomies and advanced defenses. The article serves as a valuable resource for researchers and academics in the IoT sector, offering a detailed survey of architectural security, identification of challenges, resolution strategies, and insights into the evolving landscape of IoT. The study provides a comprehensive survey of IoT architectural security, identifying challenges, proposing resolutions, and highlighting changes in the IoT domain. This research aims to enhance the accuracy by 80% of IoT security, fostering a more secure and reliable IoT ecosystem.

A formal approach to specify and verify Internet of Things architecture

The Internet of Things (IoT) aims to interconnect the physical world with the digital world, presenting complex challenges that require accurate models to ensure implementation accuracy and minimize errors. In this paper, we propose a general approach for modeling, specifying, and verifying Internet of Things architectures using the Unified Modeling Language (UML), the Event-B formal method, and the Rodin platform. We propose a model-to-text transformation that generates semi-automatic formal specifications from UML component diagrams profiled with IoT concepts and UML sequence diagrams. The resulting Event-B code allows for formal verification of system properties by employing the powerful model checker and animator tools within the Rodin platform. Additionally, we evaluate the effectiveness of various IoT patterns within our approach. Our results demonstrate the benefits of employing IoT patterns, including cost reduction related to software development. The proposed approach contributes to the development of reliable and efficient IoT systems by ensuring valid models and verifying system properties.

IoT Device Authentication Using Self-Organizing Feature Map Data Sets

Sensors and actuators connected via the Internet of Things (IoT) have now become embedded within our critical infrastructure offering improved observation and control as well as reduced costs. Given that software defined radios (SDRs) can be readily programmed to imitate IoT devices, there is now a greater risk that assets can be spoofed or compromised. This necessitates an urgent need for IoT device authentication, avoiding the need to upgrade the many thousands of individual devices. However, the lack of publicly available data sets severely hampers the development of effective authentication algorithms and mechanisms. In this regard, this article introduces a technique for facilitating IoT device authentication when the radio frequency (RF) characteristics are highly correlated using self-organizing feature maps (SOFMs), thus aiming to promote state-of-the-art research in this field. The associated techniques demonstrated in this article exploit a novel data set of RF fingerprints and are, in particular, suitable for low-cost and long-range wireless application scenarios of the IoT, e.g., LoRa. Here, a well trained convolutional neural network (CNN) based on the SOFM data set can rapidly profile apparently correlated RF fingerprint patterns and thereby ascertain the nature of a specific device (friend or foe). In this way, a reliable and efficient IoT device authentication strategy for LoRa devices can be established. The experimental results presented in this article substantiate the effectiveness and efficiency of the SOFM based approach, and the data sets are introduced in detail and shared with the research community.

ENHANCING SECURITY OF RFID-ENABLED IOT SUPPLY CHAIN

In addition to its benefits, the popular Internet of Things (IoT) technology has also opened the way to novel security and privacy issues. The basis of IoT security and privacy starts with trust in the IoT hardware and its supply chain. Counterfeiting, cloning, tampering of hardware, theft, and lost issues in the IoT supply chain have to be addressed, in order to ensure reliable IoT industry growth. In four previous works, radio-frequency identification (RFID)-enabled solutions have been proposed by the same authors, aimed to bring security to the entire IoT supply chain. The works propose a new RFID-traceable hardware architecture, device authentication, and supply chain tracing procedure. In each of these works, a variant of the same is proposed. However, the same variant of lightweight RFID authentication protocol coupled with the offline supply chain proposed in these works has such security vulnerabilities that make the whole supply chain unsafe. In our present work, an online supply chain hop-tracking procedure supported by a novel RFID mutual authentication protocol, based on the strong matching of the RFID readers-their operators-central database present at the transfer hops is proposed. Our proposed Strong RFID Authentication Protocol (STRAP) has been verified by two well-accepted formal protocol analyzers Scyther and AVISPA. The verification results demonstrate that STRAP overcomes the previous works’ vulnerabilities. Furthermore, our proposed novel online supply chain tracing procedure supporting STRAP removes the previous offline supply chain tracing procedure weaknesses.

An Overview of Safety and Security Analysis Frameworks for the Internet of Things

The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework.

Ivycide: Smart Intrusion Detection System Against E-IoT Driver Threats

The rise of Internet of Things (IoT) devices has led to the proliferation of smart environments worldwide. Although commodity IoT devices are employed by ordinary end-users, complex environments such as smart buildings, government, or private offices, or conference rooms require customized and highly reliable IoT solutions. Enterprise Internet of Things (E-IoT) connect such environments to the Internet and are professionally managed solutions usually offered by dedicated vendors As E-IoT systems require specialized training, closedsource software, and proprietary equipment to deploy. In effect, E-IoT systems present an unprecedented, under-researched, and unexplored threat vector for an attacker. In this work, we focus on E-IoT drivers, software modules used to integrate devices into E-IoT systems, as an attack mechanism. We first present PoisonIvy, a series of generalized proof-of-concept attacks used to demonstrate that an attacker can use a malicious driver to perform denial-of-service attacks, gain remote control, and abuse E-IoT system resources. To defend against E-IoT driverbased threats, we introduce IVYCIDE, a novel intrusion detection system used to detect unexpected E-IoT network traffic from an E-IoT system. IVYCIDE operates as a passive monitoring system within an E-IoT system using machine learning and signature-based classification to detect POISONIVY attacks. We evaluated the performance of IVYCIDE in a realistic E-IoT deployment. Our detailed evaluation results show that IVYCIDE achieves an average accuracy of 97% in classifying the type of POISONIVY attack and operates without modifications or operational overhead to existing E-IoT systems.

A Multi-Criteria Decision-Making Process for the Selection of an Efficient and Reliable IoT Application

Saudi Arabia initiated its much-anticipated Vision 2030 campaign, a long-term economic roadmap aimed at reducing the country’s reliance on oil. The vision, which is anticipated to be accomplished in the future, underlines compliance, fiscal, and strategy adjustments that will significantly affect all the important features of Saudi economic growth. Technology will be a critical facilitator, as well as controller, of the initiative’s significant transformation. Cloud computing, with the Internet of things (IoT), could make significant contributions to Saudi Vision 2030’s efficient governance strategy. There are multiple IoT applications that cover every part of everyday life, as well as enabling users to use a variety of IoT applications. Choosing the best IoT applications for specific customers is a difficult task. This paper concentrates on the Kingdom’s advancement towards a fresh, as well as enhanced, method of advancing the development phases pertaining to digital transformation, through implementing and adopting modern communications infrastructure and ICT technology. In addition, this study proposes a recommendation system that relies on a multi-criteria decision-making investigation focusing on the fuzzy TOPSIS method for selecting highly efficient IoT applications. The prototype, as well as the hierarchy, was created to assess and correlate critical criteria based on specialist preferences and recommendations. The T5 IoT application alternative was shown to be the most highly effective and reliable choice according to the findings of both fuzzy TOPSIS and TOPSIS.

SAgric-IoT: An IoT-Based Platform and Deep Learning for Greenhouse Monitoring

The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%.

An application of meta‐heuristic and nature‐inspired algorithms for designing reliable networks based on the Internet of things: A systematic literature review

SummaryThe widespread use of Internet of Things (IoT) in various wireless sensor networks applications has increased their importance in recent years. IoT is a smart technology that connects anything anywhere at any time. These smart objects, which connect the physical world with the world of computing infrastructure, are expected to permeate all aspects of our daily lives and revolutionize a number of application domains such as healthcare, energy conservation, and transportation. As wireless networking expands, the disadvantage of wireless communication is clearly obvious. People's apprehension over the IoT's dependability has therefore skyrocketed. IoT networks' key requirements are dependability, channel security, fault tolerance, and reliability. Monitoring the IoT networks depends on the availability and correct functioning of all the network nodes. Recent research has proposed promising solutions to address these challenges. This article systematically examines recent articles that use meta‐heuristic and nature‐inspired algorithms to establish reliable IoT networks. Eighteen articles were analyzed in four groups. Results showed that reliable enhancement mechanisms in IoT networks increase fault node detection, network efficiency, and lifetime and attain energy optimization results in the IoT concept. Additionally, it was discovered in the literature that the current studies focus on how to effectively use edge network capabilities for IoT application executions and support, along with the related needs.

Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT

Internet of Things (IoT) devices are becoming increasingly ubiquitous in daily life. They are utilized in various sectors like healthcare, manufacturing, and transportation. The main challenges related to IoT devices are the potential for faults to occur and their reliability. In classical IoT fault detection, the client device must upload raw information to the central server for the training model, which can reveal sensitive business information. Blockchain (BC) technology and a fault detection algorithm are applied to overcome these challenges. Generally, the fusion of BC technology and fault detection algorithms can give a secure and more reliable IoT ecosystem. Therefore, this study develops a new Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) technique for IoT Fault Detection purposes. The presented BDEV-CAML technique integrates the benefits of blockchain, IoT, and ML models to enhance the IoT network’s trustworthiness, efficacy, and security. In BC technology, IoT devices that possess a significant level of decentralized decision-making capability can attain a consensus on the efficiency of intrablock transactions. For fault detection in the IoT network, the deep directional gated recurrent unit (DBiGRU) model is used. Finally, the African vulture optimization algorithm (AVOA) technique is utilized for the optimal hyperparameter tuning of the DBiGRU model, which helps in improving the fault detection rate. A detailed set of experiments were carried out to highlight the enhanced performance of the BDEV-CAML algorithm. The comprehensive experimental results stated the improved performance of the BDEV-CAML technique over other existing models with maximum accuracy of 99.6%.

LoRa and Rotating Polarization Wave: Physical Layer Principles and Performance Evaluation

Link reliability and enhanced coverage are the primitive concerns of Low-Power Wide-Area Networks (LPWANs) for suitability to critical Internet of Things (IoT) applications. Reliability is limited by the destructive multipath propagation, data rate and sensitivity, that ultimately limits the coverage range. LoRa by far is the predominant LPWAN operating on unlicensed spectrum. Despite its robust Chirp Spread Spectrum (CSS) modulation, there is a severe degradation in its error performance particularly in hostile propagation environments, and an excessive reduction in coverage. Rotating Polarization Wave (RPW) is a potential LPWAN recently emerged to achieve a highly reliable IoT and Machine-to-Machine (M2M) communication. This is the first paper to provide comprehensive error performance comparison between LoRa and RPW. Okumura-Hata model is used for median path loss calculation. Shadowing and fast fading margins of RPW and LoRa are estimated. Effective gain of RPW is computed from error performance. Results have shown that LoRa offers a sensitivity of 23 dB higher than RPW under AWGN conditions. However, under fading conditions, RPW exhibits a sensitivity of 15 dB higher than LoRa. At a reference distance of 100 m, the maximum received signal strength of RPW is -39 dBm, which is 29 dB above LoRa. The maximum coverage distance attained by RPW is 15 km, which is 1.5 times of LoRa.

An Integrated Collaborative Filtering Framework With Location-Aware Graph Embedding in Intelligent Internet of Things Systems

Reliable Internet of Things (IoT) service discovery is a significant task in intelligent service-oriented IoT systems. Collaborative filtering (CF) turns out to be an effective solution to IoT service discovery. However, the traditional CF framework is facing the following challenges: inefficiency in learning the high-order interactions between users and IoT services and ineffectively making use of geographical location information. Moreover, deploying a CF framework in real-world distributed IoT systems poses another significant challenge in terms of reliability assurance and privacy protection. For bridging this gap, we propose an innovative integrated collaborative filtering framework (ICF) with incorporating the location-aware quality of IoT services into a heterogeneous graph embedding model. Meanwhile, a Geohash-based privacy-preservation mechanism is introduced to encoding the location information into a short string for protecting the sensitive location information. The proposed ICF framework is an integrated architecture combining advanced graph embedding learning and heterogeneous side information. And a joint objective optimization function is designed in the graph embedding learning to qualify automatic IoT service features. In this way, the hidden user-service interaction information and location-aware quality semantic features can be explored in an effective and reliable way. Furthermore, the learned location-aware quality embedding vector is incorporated to discover the reliable services among all IoT services with the mini-batch online clustering-based CF algorithm. The experimental findings illustrate the significant efficacy and reliability of our proposed ICF framework in large scale IoT service discovery scenarios.

Blockchain-Based Internet of Things Security and Reliability via SDN-Enabled 5G-VANETs

New opportunities for vehicular Internet of Things (IoT) services using current and modern rational transportation system, along with newer challenges for vehicular ad-hoc networks (VANETs). Along with improved network performance, a workable and trustworthy security system is required to handle trust management while maintaining user privacy. A key technology for extremely dependable, low-latency wireless communication services is the newly developed 5G mobile communication system. Additionally, the software-defined network (SDN) design allows for network control and worldwide data collection within the 5G-VANET. As a result, real-time IoT services for monitoring and reporting on transportation can be effectively supported. Both open the door for a cutting-edge automobile security system.This paper investigates the security and privacy issue in the transportation system and the vehicular IoT environment in SDN-enabled 5G-VANET. A blockchain-based security framework is created to support the vehicular IoT services, such as real-time cloud-based video reporting and trust management on vehicular messaging, due to the decentralised and immutable nature of blockchain. The 5G-VANET model with SDN support and the framework's scheduling processes are specifically illustrated in this study. The outcomes of the numerical simulation also demonstrate that it is possible to detect malicious vehicular nodes or communications while maintaining acceptable overhead and network performance impacts in large-scale scenarios. We show how our design considerably ensures a secure and reliable vehicle IoT environment with user privacy preserved through case studies and theoretical analysis.

Massive MIMO Empowered Wireless Powered Sensor Networks: An Optimal Design With Statistical CSI

Wireless energy transfer (WET) has been anticipated as a viable solution to combat the challenges of scarce energy supply in Internet-of-Things (IoT) applications. To reap the benefit brought by large scale antenna arrays, massive multiple-input-multiple-out (MIMO) empowered wireless powered sensor networks (WPSNs) is investigated in this letter to meet the urgent demand for reliable and self-sustainable IoT applications. Considering a large-scale antenna array deployed at the access point for energy transfer to and information collection from the multiple-antenna wireless powered sensors, the hardening property in energy transfer is generalized and theoretically proved based on random matrix theory. Based on the hardening properties, the harvesting energy and the uplink sum-rate for sensor data collection are found to be asymptotically deterministic and depend on the statistical channel state information (CSI) only. The hardening properties in both energy and information transfers enable a novel optimal design on the WPSNs based on statistical CSI only. Particularly, the designs of the downlink energy beamforming and multiple uplink information beamforming can be decoupled, and global optimal solutions for the beamforming and time allocation to maximize the uplink sum-rate given downlink power constraint are found in semi-closed forms. Different from prior WPSNs designs, statistical CSI but not instantaneous CSI is exploited and frequent channel estimation and heavy communication overhead can be avoided with a much lower computation complexity, thus improving the practicability and sustainability of the WPSNs. The effectiveness of the proposed design is finally demonstrated by numerical simulations.

Flexible and Reliable Multiuser SWIPT IoT Network Enhanced by UAV-Mounted Intelligent Reflecting Surface

Intelligent reflecting surface (IRS) cooperated with the simultaneous wireless information and power transfer (SWIPT) can reinforce the desired signal and deal with the energy supply problem effectively. By leveraging the on-demand mobility of unmanned aerial vehicles (UAVs), the IRS cooperated SWIPT can be deployed in more flexible and reliable scenarios. In this article, we investigate the UAV-mounted IRS-assisted SWIPT for Internet of Things (IoT) networks. In particular, a UAV-mounted IRS is deployed to assist the information transmission and power transfer from the access point to several IoT devices simultaneously. Taking full advantage of the UAV-mounted IRS in attending multiple IoT devices flexibly, a time division multiple access (TDMA)-based scheduling protocol is proposed to serve different IoT devices alternatively during the UAV flying along an optimized trajectory with the information and power transfer executed. Then, an optimization problem of maximizing the minimum average achievable rate of multiple devices is formulated with the specific energy harvesting requirement guaranteed. To solve the nonconvex problem, we leverage the successive convex approximation and block coordinate descent methods to develop an iterative algorithm. Simulation results demonstrate that with the help of the more flexible and reliable UAV-mounted IRS, the minimum achievable rate of the IoT network can be significantly improved.

Self-driven real-time angle vector sensor as security dialer based on bi-directional backstop triboelectric nanogenerator

With the developments of Internet of Things (IoT), there is an endless demand for various sensors that can maintain a certain level of functional performance while providing excellent energy consumption levels, preferably harvesting energy from the environment. In this work, we designed a self-driven angle vector sensor based on triboelectric nanogenerator (TENG) for constructing a reliable and low-cost IoT sensing system. Based on the high signal-to-noise (SNR) output of rotary TENG, a bi-directional backstop TENG (BB-TENG) is designed to realize direct quantization process of rotation angle and direction in real time. The homemade BB-TENG can generate an output voltage up to 174.45 V to resist external noise and guarantee the measurement quality, furthermore, the maximum speed error of measured rotation speed does not exceed 0.2 rpm. Then, a digital signal processor was implemented to extract the angle vector in real time and presents its application as a passcode dialer. This work not only demonstrates a new strategy in the field of angle measurement, but also extends the application scenario of TENG in the IoT field.

Extensive review on IoT security challenges and LWC implementation on tiny hardware for node level security evaluation

Internet-of-Things (IoT) offers a novel intelligent ecosystem that consists of various physical objects interconnected via the internet, which draws the attention of researchers, academicians, and industrialists. Various physical objects are embedded systems that perform dedicated operations that include sensing, monitoring, and controls. Such connectivity of embedded system devices over the internet creates an intelligent mesh worldwide and makes city, industry, and human life entirely automated and intelligent. However, the existing embedded system with radio modules is battery operated, referred to as a low-resourced device. Further, it is expected that the device should consume less operational power. Tiny size devices are offering less memory which creates a resource-constrained environment. An efficient hardware implementation of security algorithms is challenging in a constrained environment that satisfies all performance metrics. Standard internet connectivity of all devices with new wireless paradigms (e.g. ZigBee, LoRa, Wi-Fi, SigFox, etc.) essentially needs to be scrutinized for secured data communication and other security flaws. The universal connection allows an adversary to access secured technology via vulnerable systems. Many researchers are analyzing IoT technologies in every possible aspect to provide an economically secured solution. Importing software-tested encryption standards on hardware with efficient results can produce reliable IoT nodes. In this paper, we present the overview of IoT infrastructure with supporting data communication protocols. Also, we discussed essential cryptographic design rationale to minimize overall structure with the importance of metrics. Environmental and implementation based challenges, trade-off, and importance of cryptography towards the development of secured IoT node with Light-Weight Cipher (LWC) ANU and PRESENT proof-of-concept for generic application is provided in this research.

An automated context-aware IoT vulnerability assessment rule-set generator

While introducing unprecedented applications, Internet of Things (IoT) has simultaneously provoked acute security challenges, in the form of the vulnerabilities. Mainly because manufacturers overlook the security considerations and produce devices that could be exploited easily. Security systems used for the protection of IoT environment usually deploy traditional rulesets which lack distinct IoT vulnerability assessment elements and therefore are inadequate for providing security to IoT eco-system. Hence, due to the variety and volume of such devices, traditional security solutions need to be more robust for IoT settings. Contrary to the traditional rule-set, IoT device vulnerability identification requires distinct understanding of IoT-specific vulnerability vectors, based on their architecture, resource constrained nature, communication primitives and context awareness. This research work has proposed an automated context-aware IoT vulnerability assessment rule-set framework. Proposed system dynamically identifies IoT devices along with the services running on them, gathers their respective vulnerabilities, transform them into rules and enforce them into the security solutions. The proposed framework has been evaluated on a dataset of 49 IoT devices. According to the results, proposed framework automatically generated rules against all the vulnerabilities present in the network under consideration. Additionally, this research has proposed IoT vulnerability assessment rule-set elements which are necessary to be considered while designing any IoT vulnerability assessment rule-set. With the proposed mechanism, this research work intends to fill the missing lines of defense against rising IoT vulnerabilities. The proposed framework will benefit researchers, security analysts and manufacturers to devise reliable IoT security solutions.