Resource-constrained Internet Of Things Devices Research Articles

Uncovering botnets in IoT sensor networks: a hybrid self-organizing maps approach

The integration of the internet of things (IoT) has revolutionized diverse industries, introducing interconnected devices and IoT sensor networks for improved data acquisition. However, this connectivity exposes IoT ecosystems to emerging threats, with botnets posing significant risks to security. This research aims to develop an innovative solution for detecting botnets in IoT sensor networks. Leveraging insights from existing research, the study focuses on designing a hybrid self-organization map (SOM) Approach that integrates lightweight deep learning (DL) techniques. The objective is to enhance detection accuracy by exploring various DL architectures. Proposed methodology aims to balance computational efficiency for resource-constrained IoT devices while improving the discriminatory power of the detection system. The study advancing IoT cybersecurity and addresses critical challenges in botnet detection within IoT sensor networks. The testing of the artificial neural networks (ANN) classifier involves three models, each represented based on parameters related to the construction of the training models. The most effective ANN achieves 86%, works on anomaly intrusion detection systems (AIDS).

A systematic review on elliptic curve cryptography algorithm for internet of things: Categorization, application areas, and security

Cyberattacks on the Internet of Things (IoT) may be avoided by implementing cybersecurity protocols on IoT devices, platform packages, and Internet tools. A significant problem is developing a simple security mechanism suitable for resource-constrained IoT devices. Elliptic curve cryptography (ECC) is extensively utilized and well-suited for use in low-resource environments like smartphone devices. ECC utilizes short crypto keys with the same security strength as conventional cryptosystems, decreasing power consumption and enhancing device efficiency. This study comprehensively evaluates ECC data privacy and security techniques in IoT-based devices. The research also looked into other cryptography protocols that ECC may use to increase security and performance. The inclusion procedures were papers published between 2018 and 2023, written in English, and published in a peer-reviewed scholarly outlet. This research eventually reviewed 61 suitable publications in total. Compared to other cryptographic methods, the study discovered that ECC has received greater attention or is frequently employed in IoT-based systems. However, additional issues and concerns must be addressed, including efficient IoT device security, maintaining privacy, and device security against vulnerability assaults such as man-in-the-middle attacks. For the efficient security of the IoT-based system, an improved ECC-based algorithm should be developed to enhance the security and privacy issues of the IoT system.

Decentralized Zone-Based PKI: A Lightweight Security Framework for IoT Ecosystems

The advent of Internet of Things (IoT) devices has revolutionized our daily routines, fostering interconnectedness and convenience. However, this interconnected network also presents significant security challenges concerning authentication and data integrity. Traditional security measures, such as Public Key Infrastructure (PKI), encounter limitations when applied to resource-constrained IoT devices. This paper proposes a novel decentralized PKI system tailored specifically for IoT environments to address these challenges. Our approach introduces a unique “zone” architecture overseen by zone masters, facilitating efficient certificate management within IoT clusters while reducing the risk of single points of failure. Furthermore, we prioritize the use of lightweight cryptographic techniques, including Elliptic Curve Cryptography (ECC), to optimize performance without compromising security. Through comprehensive evaluation and benchmarking, we demonstrate the effectiveness of our proposed solution in bolstering the security and efficiency of IoT ecosystems. This contribution underlines the critical need for innovative security solutions in IoT deployments and presents a scalable framework to meet the evolving demands of IoT environments.

MULTI-BLOCK: A novel ML-based intrusion detection framework for SDN-enabled IoT networks using new pyramidal structure

The ever-expanding Internet of Things (IoT) landscape faces significant security challenges due to limitations in traffic monitoring, device heterogeneity, and weak security practices, leaving networks vulnerable to multi-target and coordinated attacks like large-scale botnets and Distributed Denial-of-Service (DDoS). Existing intrusion detection systems can be computationally expensive and resource-intensive. This can be problematic for resource-constrained IoT devices with limited processing power, memory, and battery life. This paper proposes MULTI-BLOCK, a novel, multi-module framework that leverages machine learning, stateful P4 processing, and a Software-Defined Networking (SDN)-based multi-controller architecture. MULTI-BLOCK tackles critical management tasks within IoT networks, including synchronized communication, traffic monitoring, intrusion detection, and attack mitigation. This comprehensive framework comprises four modules. The first, the proposed pyramidal conceptually decentralized multi-controller structure (PCDMCS), introduces Decentralized Control Interfaces (DCIs) for real-time threat identification through a Decentralized Warning Conduit (DWC). The second module provides comprehensive network monitoring using P4-enabled 24-state tables. The third module leverages 30 new P4-extracted/calculated features and the Enhanced Weighted Ensemble Algorithm (EWEA) for enhanced anomaly detection. The final module presents a novel mitigation approach with 22 steps distributed across multiple controllers for scalability. Extensive evaluation using established IoT datasets (X-IIoTID, TON_IoT, and Edge-IIoTset) and diverse test scenarios (including single-victim and multi-victim attacks) demonstrates MULTI-BLOCK's exceptional performance, achieving high accuracy (99.75 %), precision (99.32 %), F1-score (99.53 %), recall (99.67 %), specificity (99.60 %), low false positive rates (FPR) (0.40 %), and low Average Detection Time (ADT) (2.11 ms). By offering a robust and adaptable solution against evolving threats, MULTI-BLOCK represents a significant advancement in IoT network security.

A novel trusted hardware-based scalable security framework for IoT edge devices

The Internet of Things (IoT) devices are pervasively deployed and embedded into our daily lives. Over several years, the massive assimilation of IoT devices has given rise to smart cities, smart factories, smart farms, etc. While IoTs can collect immense amounts of user and environmental information, the lack of adequate security measures is among the major concerns about information security and privacy. With the ever-increasing presence of IoTs, information flow between IoT devices in a single or multi-layered network is becoming increasingly vulnerable to attacks. Although several software-based solutions have been developed over the years that improve the security of resource-constrained IoT devices, there are limited efforts on hardware-oriented IoT security solutions. This paper proposes a Trusted HArdware-based Scalable Secure Architecture (THASSA) for IoT Edge systems. Leveraging standard cryptographic algorithms, THASSA provides a secure mechanism for data transfer and off-loads cryptography operations to trusted hardware. The modularized THASSA architecture allows devices to be added or removed without user intervention. Also, THASSA could be scaled to multiple subsystems using multiple trusted hardware to form IoT clusters with secure data path(s) for mission-critical applications. An experimental study has verified the correctness and effectiveness of THASSA.

Wireless IoT Networks Security and Lightweight Encryption Schemes- Survey

— IoT networks suffered from different kinds of attacks. The main issue of IoT security is the limitation of its node’s energy ability to perform big tasks such as encryption. This paper provides a comprehensive overview of the dynamic landscape of lightweight cryptography research, particularly in the context of real-time traffic security and Internet of Things (IoT) networks. The aim of this research is to identify the gaps in IoT research efforts and draw up ways to address it. The objectives are highlighting the global collaboration and standardization initiatives that have shaped its development. Also, it emphasizes the importance of balancing cryptographic strength with practical considerations such as encryption latency, energy efficiency, and memory constraints, particularly crucial in the context of resource constrained IoT devices. Furthermore, it highlights innovative approaches proposed by researchers to enhance the security of cryptographic operations while minimizing overhead and ensuring compatibility with evolving wireless environments. It calls for sustained research efforts to address these challenges and foster the development of robust cryptographic solutions capable of meeting the stringent security requirements of modern digital ecosystems.

Enabling privacy-aware interoperable and quality IoT data sharing with context

Sharing Internet of Things (IoT) data across different sectors, such as in smart cities, becomes complex due to heterogeneity. This poses challenges related to a lack of interoperability, data quality issues and lack of context information, and a lack of data veracity (or accuracy). In addition, there are privacy concerns as IoT data may contain personally identifiable information. To address the above challenges, this paper presents a novel semantic technology-based framework that enables data sharing in a GDPR-compliant manner while ensuring that the data shared is interoperable, contains required context information, is of acceptable quality, and is accurate and trustworthy. The proposed framework also accounts for the edge/fog, an upcoming computing paradigm for the IoT to support real-time decisions. We evaluate the performance of the proposed framework with two different edge and fog–edge scenarios using resource-constrained IoT devices, such as the Raspberry Pi. In addition, we also evaluate shared data quality, interoperability and veracity. Our key finding is that the proposed framework can be employed on IoT devices with limited resources due to its low CPU and memory utilization for analytics operations and data transformation and migration operations. The low overhead of the framework supports real-time decision making. In addition, the 100% accuracy of our evaluation of the data quality and veracity based on 180 different observations demonstrates that the proposed framework can guarantee both data quality and veracity.

A Lightweight Image Cryptosystem for Cloud-Assisted Internet of Things

Cloud computing and the increasing popularity of 5G have greatly increased the application of images on Internet of Things (IoT) devices. The storage of images on an untrusted cloud has high security and privacy risks. Several lightweight cryptosystems have been proposed in the literature as appropriate for resource-constrained IoT devices. These existing lightweight cryptosystems are, however, not only at the risk of compromising the integrity and security of the data but also, due to the use of substitution boxes (S-boxes), require more memory space for their implementation. In this paper, a secure lightweight cryptography algorithm, that eliminates the use of an S-box, has been proposed. An algorithm termed Enc, that accepts a block of size n divides the block into L n R bits of equal length and outputs the encrypted block as follows: E=L⨂R⨁R, where ⨂ and ⨁ are exclusive-or and concatenation operators, respectively, was created. A hash result, hasR=SHA256P⨁K, was obtained, where SHA256, P, and K are the Secure Hash Algorithm (SHA−256), the encryption key, and plain image, respectively. A seed, S, generated from enchash=Enchashenc,K, where hashenc is the first n bits of hasR, was used to generate a random image, Rim. An intermediate image, intimage=Rim⨂P, and cipher image, C=Encintimage,K, were obtained. The proposed scheme was evaluated for encryption quality, decryption quality, system sensitivity, and statistical analyses using various security metrics. The results of the evaluation showed that the proposed scheme has excellent encryption and decryption qualities that are very sensitive to changes in both key and plain images, and resistance to various statistical attacks alongside other security attacks. Based on the result of the security evaluation of the proposed cryptosystem termed Hash XOR Permutation (HXP), the study concluded that the security of the cryptography algorithm can still be maintained without the use of a substitution box.

Wireless IoT Networks Security and Lightweight Encryption Schemes- Survey

— IoT networks suffered from different kinds of attacks. The main issue of IoT security is the limitation of its node’s energy ability to perform big tasks such as encryption. This paper provides a comprehensive overview of the dynamic landscape of lightweight cryptography research, particularly in the context of real-time traffic security and Internet of Things (IoT) networks. The aim of this research is to identify the gaps in IoT research efforts and draw up ways to address it. The objectives are highlighting the global collaboration and standardization initiatives that have shaped its development. Also, it emphasizes the importance of balancing cryptographic strength with practical considerations such as encryption latency, energy efficiency, and memory constraints, particularly crucial in the context of resource constrained IoT devices. Furthermore, it highlights innovative approaches proposed by researchers to enhance the security of cryptographic operations while minimizing overhead and ensuring compatibility with evolving wireless environments. It calls for sustained research efforts to address these challenges and foster the development of robust cryptographic solutions capable of meeting the stringent security requirements of modern digital ecosystems.

A Survey on Lightweight Cryptographic Algorithms in IoT

Abstract The Internet of Things (IoT) will soon penetrate every aspect of human life. Several threats and vulnerabilities are present due to the different devices and protocols used in an IoT system. Conventional cryptographic primitives or algorithms cannot run efficiently and are unsuitable for resource-constrained devices in IoT. Hence, a recently developed area of cryptography, known as lightweight cryptography, has been introduced, and over the years, numerous lightweight algorithms have been suggested. This paper gives a comprehensive overview of the lightweight cryptography field and considers various popular lightweight cryptographic algorithms proposed and evaluated over the past years for analysis. Different taxonomies of the algorithms and other associated concepts were also provided, which helps new researchers gain a quick overview of the field. Finally, a set of 11 selected ultra-lightweight algorithms are analyzed based on the software implementations, and their evaluation is carried out using different metrics.

Analysis and Performance Comparison of IoT Message Transfer Protocols Applying in Real Photovoltaic System

AbstractThe adoption of reliable and real-time communication technology is an absolute necessity for the advancement of Internet of Things (IoT) applications. Messaging protocols such as MQTT, AMQP, and HTTP are frequently used for communication with resource-constrained IoT devices. However, choosing a suitable and effective messaging protocol presents a daunting challenge for organizations, as it depends on the specific characteristics and messaging requirements of the IoT system. Therefore, it is crucial to have a comprehensive understanding of three established messaging protocols, such as the Hypertext Transfer Protocol (HTTP), the Message Queuing Telemetry Transport (MQTT), and the Advanced Message Queuing Protocol (AMQP), to appropriately apply them in practical projects. In this paper, information technology solutions are provided for a chain of solar farms to improve harvest productivity, facilitate warning notifications, and enable remote control. Subsequently, a detailed comparative analysis is performed, considering various interconnected criteria, to gain valuable insight into the strengths and limitations of these protocols. The results show that MQTT and AMQP play a role in enhancing overall efficiency and speed within the framework of our suggested photovoltaic system.

Novel image pixel scrambling technique for efficient color image encryption in resource-constrained IoT devices

AbstractIn the digital age, where data is a valuable commodity, securing sensitive information has become a growing concern. Image encryption techniques play an essential role in protecting visual data from unauthorized access and ensuring privacy. However, with limited computing capacity in Internet of Things (IoT) devices, standard encryption algorithms are not feasible, rendering lightweight methods mandatory. This study proposes a novel Corner Traversal algorithm, an alternative to existing pixel scrambling techniques. The proposed algorithm demonstrably outperforms its counterparts in both higher confusion and lower time complexity, making it remarkably efficient. Integrated with chaos-based diffusion methods, this algorithm forms a comprehensive encryption scheme. The proposed lightweight image encryption scheme utilizing the Corner Traversal algorithm successfully passed rigorous statistical and differential security analysis. Compared to similar schemes, the proposed encryption scheme employing the Corner Traversal algorithm in the confusion phase distinguishes itself through exceptional NPCR (99.6093 for Lenna) and UACI (33.4648 for Lenna) values. Combined with other evaluation criteria, this method demonstrably meets the stringent security requirements of IoT systems.

Lightweight Strong PUF for Resource-Constrained Devices

Physical Unclonable Functions are security primitives that exploit the variation in integrated circuits’ manufacturing process, and, as a result, each instance processes applied stimuli differently. This feature can be used to provide a unique fingerprint of the electronic device, or as an interesting alternative to classic key storage methods. Due to their nature, they are often considered an element of the Internet of Things nodes. However, their application heavily depends on resource consumption. Lightweight architectures are proposed in the literature but are technology-dependent or still introduce significant hardware overhead. This paper presents a lightweight, Strong PUF based on ring oscillator architecture, which offers small hardware overhead and sufficient security levels for resource-constrained Internet of Things devices. The PUF design utilizes a Linear Feedback Shift Register-based scramble module to generate many challenge–response pairs from a small number of ring oscillators and a control module to manage the response generation process. The proposed PUF can be used as a Weak PUF for key generation or a Strong PUF for device authentication.

Efficient privacy preserving communication protocol for IOT applications

The proliferation of Internet of Things (IoT) devices has led to an unprecedented increase in data generation and communication, raising concerns about the security and privacy of transmitted information. This paper presents a novel Message Authentication Protocol (MAP) specifically designed for IoT devices, addressing the dual challenges of ensuring data integrity and preserving user privacy. The proposed protocol employs advanced cryptographic techniques to authenticate messages securely while incorporating privacy-preserving mechanisms to safeguard sensitive user information.The protocol utilizes a hybrid approach, combining symmetric and asymmetric cryptographic primitives to achieve efficient and robust message authentication. Symmetric key algorithms are employed for fast and lightweight authentication, ensuring minimal computational overhead for resource-constrained IoT devices. Additionally, asymmetric key techniques are integrated to facilitate secure key exchange and protect against key compromise.Privacy preservation is a paramount concern in IoT ecosystems, where devices often handle sensitive data. The protocol incorporates anonymization and differential privacy techniques to conceal the identity of users and the specific content of transmitted messages. This ensures that even if an adversary intercepts communication, extracting meaningful information about the users or their data remains a formidable challenge.To evaluate the effectiveness of the proposed protocol, we conducted comprehensive simulations and real-world experiments. The results demonstrate that the protocol achieves a balance between security, efficiency, and privacy preservation. It outperforms existing solutions in terms of computational efficiency and provides a robust defence against various common attacks, such as replay attacks and message tampering.In conclusion, the presented Secure and Efficient Message Authentication Protocol for IoT Devices with Privacy Preservation offers a viable solution to the evolving security and privacy challenges in IoT environments. By leveraging cryptographic techniques and privacy-preserving mechanisms, the protocol provides a strong foundation for safeguarding sensitive information while ensuring the integrity of communication in the vast and interconnected landscape of IoT devices.

DRcipher: A pseudo-random dynamic round lightweight block cipher

The Internet of Things (IoT) has gained popularity in various fields, including components such as embedded devices and wireless sensors. However, ensuring the security of data transmission from these devices is of critical importance. In light of these challenges, a novel lightweight encryption method called DRcipher is proposed in this paper for resource-constrained IoT devices. DRcipher has a 64-bit block size and supports either 96-bit or 128-bit key sizes. In order to improve the security, DRcipher employs a pseudo-random number of encryption rounds determined by the primary key. DRcipher adopts the structure of Generalized Feistel Network (GFN) with 4 branches, and its round functions consist of F-function, FF-function and RP permutation components. In particular, there is a negative feedback mechanism between the FF-function and the overall round of encryption functions. In addition, DRcipher is synthesised using Synopsys Design Compiler version A-2007.12-SP1 and the UMCL18G212T3 standard cell library. DRcipher-96 has an area footprint of 1546 Gate Equivalents (GE), while DRcipher-128 has a slightly larger area footprint of 1646 GE. Moreover, a comprehensive security analysis shows that the proposed DRcipher ensures high-level security redundancy against differential cryptanalysis, linear cryptanalysis, and so on.

Securing micro transactions in the Internet of Things with cryptography primitives

The growing number of IoT devices, microtransaction security in the Internet of Things (IoT) is a critical issue. In order to improve the security of microtransactions in Internet of Things ecosystems, this article investigates the usage of cryptographic primitives. Strong safeguards against possible risks including data manipulation, eavesdropping, and unauthorized access are provided by cryptography, which provides a foundation for guaranteeing the confidentiality, integrity, and validity of transaction data. We address the special problems of power and processing limits by delving into the implementation of lightweight cryptography algorithms and protocols designed for resource-constrained IoT devices. Our goal is to create a trust framework that guarantees the security and privacy of data exchange by incorporating cryptographic primitives into IoT microtransactions. By encouraging trust and dependability in microtransactions, which are essential to the future of networked smart devices, our research helps to build a more secure and robust Internet of Things environment.

Fog-Enabled Secure and Efficient Fine-Grained Searchable Data Sharing and Management Scheme for IoT-Based Healthcare Systems

In recent times, fog computing has emerged as a helpful extension of cloud computing. It can efficiently handle the prevalent issue of managing silos of data generated by today's digital healthcare services. Moreover, the application of the Internet of Things (IoT) in the development of smart healthcare systems further adds tons of data tirelessly to these silos, thus making the cloud congested. To manage such continuously growing data, the concept of adding a fog layer between the cloud and the end-users (EUs) proved to be beneficial. These intermediary fog nodes (FNs) can handle and store data, and thus facilitate the cloud and alleviate the burden from the EUs. Most of the existing search schemes for encrypted data have been developed for the cloud platform and ignored this helpful extension, which can improve the scheme's efficiency by delegating most of the heavy computations to the intermediary FNs. In this article, a fine-grained searchable data sharing scheme has been proposed using the fog computing platform. The resulting scheme is efficient and lightweight because the FN facilitates EUs by performing computationally intensive tasks on their behalf. A significant reduction in storage and computational cost has been achieved by the proposed scheme at the data owner's end, representing the resource-constrained IoT devices. The storage cost has been reduced to two source group elements, and the computational cost has been reduced to three exponent operations in the source group and one hash operation. Furthermore, the proposed scheme is secure against the selectively chosen keyword attack in the generic bilinear group model.

A review of on-device machine learning for IoT: An energy perspective

Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications.

T2S-MAKEP and T2T-MAKEP: A PUF-based Mutual Authentication and Key Exchange Protocol for IoT devices

Nowadays, more constrained devices are becoming connected, building an extensive Internet of Things (IoT) network, but suffering from many security issues. In particular, authentication has become a severe research challenge for IoT systems. Furthermore, confidentiality, integrity, and availability are considered the core underpinnings of information security in general. Unfortunately, deploying conventional authentication protocols for IoT devices in practice is challenging for two main reasons. First, IoT devices have limited memory capacity, processing power, and energy resources. Second, these protocols store secret keys in the IoT devices’ volatile memory, making them vulnerable to physical attacks. Luckily, Physical Unclonable Functions (PUF) has emerged as promising low-cost security primitive. A PUF eliminates the need to store secret keys in device memory, making it a potential alternative to deploying more secure and low-cost authentication protocol schemes for IoT systems. Thing-to-Thing (T2T) or direct connection between IoT devices represents a promising technique to enable things to communicate directly without the need for a trusted third party. This paper proposes two novel lightweight Mutual Authentication and Key Exchange Protocols (MAKEP) for IoT devices using PUF. The first scheme, called T2S-MAKEP, ensures secure communication for Thing-to-Server (T2S). The second, called T2T-MAKEP, allows two endpoints of resource-constrained IoT devices, each with an embedded PUF circuit, to communicate securely. Both proposed protocols, T2T-MAKEP and T2S-MAKEP, allow for robust authentication without storing any information on the device’s memory and simultaneously establish the session key exchange. Our proposed protocols have been verified and validated using the automatic security analysis checker, Verifpal.

Behavioral fingerprinting to detect ransomware in resource-constrained devices

The Internet of Things (IoT), a network of interconnected devices, has grown and gained traction over the last few years. This paradigm can impact our lives while also providing significant economic benefits. However, although resource-constrained IoT devices offer numerous advantages, they are also vulnerable to cyberattacks. As a result, ransomware severely threatens IoT devices managing sensitive and relevant information. Solutions based on Machine and Deep Learning (ML/DL) that consider behavioral data have been identified as promising. However, most detection solutions have been developed for Windows-based systems, which generally have more resources than IoT devices. As a result, these solutions are not suitable for resource-constrained components. In addition, no solution compares the pros and cons of different behavioral dimensions of resource-constrained devices. Thus, this work presents a framework that combines three different behavioral sources with supervised and unsupervised ML/DL algorithms to detect and classify heterogeneous ransomware impacting resource-constrained spectrum sensors. A pool of experiments has demonstrated the suitability of the proposed solution and compared its performance with a rule-based system. In conclusion, the usage of resources combined with local outlier factor and decision tree are the most promising combinations to detect anomalies and classify ransomware while consuming CPU, RAM, and time of devices in a reduced manner.