Resource-constrained IoT Devices Research Articles

Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) enables fine-grained access control on ciphertexts, making it a promising approach for managing data stored in the cloud-enabled Internet of Things. But existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute content. Additionally, resource-constrained IoT devices, especially those adopting wireless communication, frequently encounter affordability issues regarding decryption costs. In this paper, we propose an efficient and fine-grained access control scheme with fully hidden policies (named FHAC). FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them. A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access policy. Dictionary attacks are thwarted by providing unauthorized users with invalid values. The heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud servers. Additionally, users can verify the correctness of multiple outsourced decryption results simultaneously. Security analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

Class imbalance and concept drift invariant online botnet threat detection framework for heterogeneous IoT edge

Heterogeneous networks (HetIoT) of high-capacity and resource-constrained IoT devices and their edge associations for on-device distributed critical workloads—called the edge-of-things (EoT)—attract short-burst, botnet-based zero-day attacks that exploit latent vulnerabilities due to heterogeneous device properties, dynamic operational contexts, and insufficient security scrutiny of the constituent proprietary devices. Such a scenario necessitates a device-specific network intrusion detection (NID) technique for localizing the threat space and updated rule learning through online (real-time) model retraining. Furthermore, scarce labeled knowledge base and high levels of class imbalance of NID datasets complicate the ID system design process for EoT environments, as online detection cannot afford computationally expensive data balancing techniques; this necessitates a class imbalance invariant traffic inference technique for data preprocessing. Therefore, we propound the ONIDS online NID technique, which consists of a two-fold solution for the above problems. First, we propose a Beta distribution-based inference technique for efficient traffic behavior approximation—invariant of class imbalance and capable of non-cumulative traffic processing of smaller sample sizes. Then, we put forth an online ID technique called ELMO for class imbalance invariant time-bound training of smaller sample sizes on resource-constrained device-specific network traffic. Together, they are invariant of traffic class imbalances and adaptable to resultant concept drift categories exhibited by HetIoT attack behaviors. ONIDS has low memory and compute footprints and can efficiently process large and small amounts of traffic, making it suitable for online and offline NID. It also exhibits qualitative and quantitative superiority—particularly on smaller data samples.

Attack Analysis on Hybrid-SIMON-SPECKey Lightweight Cryptographic Algorithm for IoT Applications

Objective: To perform attack analysis on new developed hybrid-SIMON-SPECKey lightweight cryptographic algorithms and compare its strength with existing SIMON and SPECK Lightweight cryptographic algorithm. Methods: A hybrid-SIMON-SPECKey algorithm is the combination of round function of SIMON and key scheduling of SPECK algorithm. Both SIOMN & SPECK algorithm are used for securing resource constrained devices. In this research work, avalanche effect method is used to analyze attack resistance property of algorithm. Findings: Newly developed Hybrid algorithm shows better results in terms of execution time and memory consumption. As compared to SIMON, hybrid version of algorithm consumes 50% less time and 20% less memory, which makes it efficient. Strict Avalanche criteria for SIMON is 89%, that of SPECK is 90% and in case of hybrid algorithm, it is 90% at start position but when the character is flipped or changed at the end position of plain text then SAC is more (87%) in case of hybrid algorithm as compared as SIMON and SPECK algorithms. Hence, newly developed algorithm showed improved results with equally resistance to the attack as compared to SIMON & SPECK. Novelty and applications: The novelty lies in the creation of a hybrid lightweight cryptographic algorithm that combines the feistel structure of SIMON with the key scheduling function of SPECK. This hybrid approach aims to leverage the strengths of both algorithms, potentially providing a more robust and efficient solution for resource-constrained IoT devices. In section 3.1 comparative analysis is done which show that hybrid algorithm outperforms in term of time and memory consumption as well a strength of newly developed hybrid algorithm is evaluated using avalanche effect which shows that it is at par with base algorithms. Keywords: Attack Analysis, Cipher Code, Decryption, Encryption, Lightweight Cryptography, Iot Devices, And Resource Constraint Devices

Statistical Fault Analysis of TinyJambu

The resource-constrained IoT devices have limited resources such as processing power, memory, and battery capacity. Therefore it is challenging to adopt traditional cryptographic algorithms on them. In order to find a solution, the National Institute of Standards and Technology (NIST) initiated the Lightweight Cryptography (LWC) competition to standardize cryptographic algorithms for resource-constrained devices. The primary aim of our work is to implement and analyse selected finalist algorithms from the NIST competition using modern cryptanalysis techniques, with a focus on statistical fault attacks. Traditional analysis methods, such as linear and differential analysis, were not prioritized as most finalist algorithms have established defences against these methods. We implemented six of the selected finalist algorithms from the competition: Ascon, Elephant, GIFT-COFB, ISAP, TinyJambu, and Xoodyak. We chose TinyJumbu for statistical fault analysis because of its attractiveness, compact block size, and provision of a more lightweight keyed permutation.

Towards Remotely Verifiable Software Integrity in Resource-Constrained IoT Devices

Towards Remotely Verifiable Software Integrity in Resource-Constrained IoT Devices

Variant Parallelism: Lightweight Deep Convolutional Models for Distributed Inference on IoT Devices

Two major techniques are commonly used to meet real-time inference limitations when distributing models across resource-constrained IoT devices: (1) model parallelism () and (2) class parallelism (). In, transmitting bulky intermediate data (orders of magnitude larger than input) between devices imposes huge communication overhead. Although solves this problem, it has limitations on the number of sub-models. In addition, both solutions are fault intolerant, an issue when deployed on edge devices. We propose variant parallelism (), an ensemble-based deep learning distribution method where different variants of a main model are generated and can be deployed on separate machines. We design a family of lighter models around the original model, and train them simultaneously to improve accuracy over single models. Our experimental results on six common mid-sized object recognition datasets demonstrate that our models can have 5.87.1× fewer parameters, 4.331× fewer multiply-accumulations (MACs), and 2.513.2× less response time on atomic inputs compared to MobileNetV2 while achieving comparable or higher accuracy. Our technique easily generates several variants of the base architecture. Each variant returns only 2k outputs 1≤k≤#classes2, representing Topk classes, instead of tons of floating point values required in . Since each variant provides a full-class prediction, our approach maintains higher availability compared with and in presence of failure.

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

Light-HHB: A Lighter Version of HHB for Resource-Constrained IoT Devices. Signature

This paper offers a lighter version of the harder HB protocol (hHB) denoted Light-hHB. This proposal uses the same framework as hHB, that is a two stages protocol: the first one for the establishment of a session key between the reader and the tag and the second one similar to HB+ which was proposed in Crypto ’05 by Hopper and Blum. We also introduce in this paper a novel and lightweight key exchange protocol inspired by the BB84 protocol named the non-quantum key exchange protocol. With the use of a practical implementation of the latter protocol in the first stage of Light-hHB, the transmission cost is drastically reduced compared to the one of hHB, which is its main drawback. In the context of resource-constrained IoT devices, Light- hHB is significantly more practical than hHB and achieves the same security goals.

A Lightweight Security Model using Delta Probabilistic Hashing Technique for Secured Data Transmission in IoT Systems

Secure data transmission is one of the most pressing concerns for resource constrained IoT devices. There is a need for an efficient and lightweight solution for data security in IoT applications. The proposed study is primarily concerned with the creation of a lightweight data security model for an IoT data transmission-based application for data transmission and storage in smart cities. The fundamental contribution of the proposed security model is the creation of a distinct hashing algorithm based on the specific pattern of the previous sequential change in value. The Delta Probabilistic Hashing (DPH) based architecture of key extraction technique for data security will perform this. Based on the hash key signature approach, this kind of safe data handling procedure provides a random key for data encryption, which will simplify the model and speed up the algorithm's operation. Our approach's main features are enhanced efficiency and throughput, lightweight data security, and adaptive key generation.

SRFL: A Secure & Robust Federated Learning framework for IoT with trusted execution environments

Federated learning has gained popularity as it enables collaborative training without sharing local data. Despite its advantages, federated learning requires sharing the model parameters during model aggregation which poses security risks. In addition, existing secure federated learning frameworks cannot meet all the requirements of resource-constrained IoT devices and non-independent and identically distributed (non-IID) setting. This paper proposes a novel secure and robust federated learning framework (SRFL) with trusted execution environments (TEEs). The framework provides security and robustness for federated learning on IoT devices under non-IID data by leveraging TEEs to safeguard sensitive model components from being leaked. Simultaneously, we introduce a shared representation training approach to enhance the accuracy and security under non-IID setting. Furthermore, a multi-model robust aggregation method using membership degree is proposed to enhance robustness. This method uses membership degree generated by soft clustering to categorize clients for better aggregation performance. Additionally, we evaluate SRFL in a simulation environment, confirming that it improves accuracy by 5%–30% over FedAVG in non-IID setting and protects the model from membership inference attack and Byzantine attack. It also reduces backdoor attack success rate by 4%–10% more compared to other robust aggregation algorithms.

Efficient Hardware Implementation of Elliptic-Curve Diffie–Hellman Ephemeral on Curve25519

Hardware architecture optimized for implementing the elliptic-curve Diffie–Hellman ephemeral (ECDHE) on 256-bit Montgomery elliptic curves presents unique challenges, particularly for resource-constrained IoT and mobile devices. This work aims to provide an efficient hardware implementation of ECDHE on Curve25519, including a dedicated finite state machine (FSM) designed to handle point multiplication and ECDHE operations, utilizing constant-time algorithms and a unified memory block for resource management. Additionally, we introduce an optimized modular computation unit that covers modular addition, subtraction, multiplication, and inversion. Our proposed hardware architecture enhances the efficiency of ECDHE operations while maintaining low resource utilization, considerably reduced latency, and low power consumption. Synthesized on the Xilinx Artix-7 platform, our design boasts 64,000 Slices and a clock speed of 102 MHz, and it computes an ECDHE scalar multiplication operation in 1.1 ms, consuming 117 mW. The proposed hardware design can be applied to various platforms, including mobile devices and IoT systems.

Tiny, Always-on, and Fragile: Bias Propagation through Design Choices in On-device Machine Learning Workflows

Billions of distributed, heterogeneous, and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast, and offline inference on personal data. On-device ML is highly context dependent and sensitive to user, usage, hardware, and environment attributes. This sensitivity and the propensity toward bias in ML makes it important to study bias in on-device settings. Our study is one of the first investigations of bias in this emerging domain and lays important foundations for building fairer on-device ML. We apply a software engineering lens, investigating the propagation of bias through design choices in on-device ML workflows. We first identify reliability bias as a source of unfairness and propose a measure to quantify it. We then conduct empirical experiments for a keyword spotting task to show how complex and interacting technical design choices amplify and propagate reliability bias . Our results validate that design choices made during model training, like the sample rate and input feature type, and choices made to optimize models, like light-weight architectures, the pruning learning rate, and pruning sparsity, can result in disparate predictive performance across male and female groups. Based on our findings, we suggest low effort strategies for engineers to mitigate bias in on-device ML.

Modified lightweight GIFT cipher for security enhancement in resource-constrained IoT devices

Modified lightweight GIFT cipher for security enhancement in resource-constrained IoT devices

Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems.

With the proliferation of IoT devices, ensuring the security and privacy of these devices and their associated data has become a critical challenge. In this paper, we propose a federated sampling and lightweight intrusion-detection system for IoT networks that use K-meansfor sampling network traffic and identifying anomalies in a semi-supervised way. The system is designed to preserve data privacy by performing local clustering on each device and sharing only summary statistics with a central aggregator. The proposed system is particularly suitable for resource-constrained IoT devices such as sensors with limited computational and storage capabilities. We evaluate the system's performance using the publicly available NSL-KDD dataset. Our experiments and simulations demonstrate the effectiveness and efficiency of the proposed intrusion-detection system, highlighting the trade-offs between precision and recall when sharing statistics between workers and the coordinator. Notably, our experiments show that the proposed federated IDS can increase the true-positive rate up to 10% when the workers and the coordinator collaborate.

Modified Rijndael Algorithm for Resource-Constrained IoT-Based Wireless Sensor Networks

INTRODUCTION: IoT devices are small, have limited battery and computing capabilities, and cannot tolerate complex encryption. OBJECTIVES: Many encryption algorithms struggle to strike a balance between security and complexity. Increasing the level of security necessitates increasing complexity, which leads to higher encryption and decryption times. This research aims to provide a novel encryption method, Modified Rijndael Algorithm that balances security and complexity. METHODS: The classic Rijndael encryption technique has been modified to make it lighter and more secure, suitable for resource-constrained IoT devices. RESULTS: These modifications include adding the novel pre-processing step SISA-Crossover based on the two-point crossover operation of genetic algorithms, removing the Sub Byte step, changing the shift column phase, and decreasing the rounds. CONCLUSION: The proposed technique provides speed and higher security in encryption and decryption, increasing efficiency and randomness while reducing encryption and decryption times by 21.06% and CPU consumption by 13.2% compared to the traditional method.

Heterogeneous Sensor Data Acquisition and Federated Learning for Resource Constrained IoT Devices—A Validation

This paper focused on applying federated learning to process heterogeneous sensor data in a fog computing-enabled smart home environment. The fusion of sensor data from vision sensor, digital ambient sensor and passive infrared sensor is done in a distributed manner and the data processing using federated learning on Raspberry Pi edge nodes. The system uses edge nodes to train the respective machine learning models using federated learning without sending data to a centralized server. Instead, results from the training on multiple edge nodes are aggregated at the central node to produce a final machine-learning model. Each edge node deploys the aggregated model to recognize the subjects in the smart home environment and trigger an alert in case unknown subjects are identified. Linear regression model for temperature prediction and logistic regression model for humidity prediction using the ambient parameters are also trained in a federated way on the same edge nodes. The developed system uses the federated learning Flower framework for training the models. The obtained validation accuracy was 66% for the machine learning models that were built on the resource constraint server and edge clients through the federated learning. The developed system can be used in environments where communication bandwidth is limited, or data privacy concerns exist.

DC-PUF: Machine learning-resistant PUF-based authentication protocol using dependency chain for resource-constraint IoT devices

The Physical Unclonable Function (PUF) is a hardware block which exploits the inherent process variations introduced during manufacturing to assign a unique fingerprint for each physical entity. This feature enables a low-cost authentication mechanism for connected devices, which is easy to evaluate but hard to predict. In this paper, we develop a low-cost PUF-based authentication mechanism called DC-PUF for resource-constrained IoT devices which can resist against machine-learning attacks. A new strategy known as the dependency chain (DC) is employed by which the response in each clock is dependent not only on the current challenge but also on the previous responses. This mechanism which takes advantage of both randomization and dependency chain hardens the ability to clone or predict the PUF instance by obfuscating the correlation between challenge-response pairs. Moreover, we propose a CNN-based attack scenario by which the existing PUF structures cannot resist while yielding more than 95% of prediction accuracy. The experimental results signify that unlike the existing PUFs, more than 58% of prediction accuracy cannot be reached when a sophisticated CNN-based attack is conducted to the DC-PUF, even with a large data-set. Also, the False-Negative rate is about 1%, in average, for three-bit mismatches in the receiving response in the presence of channel variations, whereas the False-Positive rate remains zero. Moreover, the proposed DC-PUF incurs lower hardware complexity and reasonable reliability and randomness compared to the existing PUF structures.

Obfuscated Memory Malware Detection in Resource-Constrained IoT Devices for Smart City Applications.

Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware. Moreover, their large memory size makes them unsuitable to be executed in resource-constrained embedded/IoT devices. To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. For this, the method considers a hybrid model by combining the feature-learning capabilities of convolutional neural networks with the temporal modeling advantage of bidirectional long short-term memory. The proposed architecture exhibits compact size and fast processing speed, making it suitable for deployment in IoT devices that constitute the major components of smart city systems. Extensive experiments with the recent CIC-Malmem-2022 OMM dataset demonstrate that our method outperforms other machine learning-based models proposed in the literature in both detecting OMM and identifying specific attack types. Our proposed method thus offers a robust yet compact model executable in IoT devices for defending against obfuscated malware.

IoTSL: Toward Efficient Distributed Learning for Resource-Constrained Internet of Things

Recently proposed Split Learning (SL) is a promising distributed machine learning paradigm that enables machine learning without accessing the raw data of the clients. SL can be viewed as one specific type of serial federation learning. However, deploying SL on resource-constrained IoT devices still has some limitations, including high communication costs and catastrophic forgetting problems caused by imbalanced data distribution of devices. In this paper, we design and implement IoTSL, which is an efficient distributed learning framework for efficient cloudedge collaboration in IoT systems. IoTSL combines generative adversarial networks (GANs) and differential privacy techniques to train local data-based generators on participating devices, and generate data with privacy protection. On the one hand, IoTSL pre-trains the global model using the generative data, and then fine-tunes the model using the local data to lower the communication cost. On the other hand, the generated data is used to impute the missing classes of devices to alleviate the commonly seen catastrophic forgetting phenomenon. We use three common datasets to verify the proposed framework. Extensive experimental results show that compared to the conventional SL, IoTSL significantly reduces communication costs, and efficiently alleviates the catastrophic forgetting phenomenon.

Distributed and Efficient Slot Assignment–Alignment Protocol for Resource-Constrained Wireless IoT Devices

Distributed and Efficient Slot Assignment–Alignment Protocol for Resource-Constrained Wireless IoT Devices