In the rapidly evolving landscape of medical record management, the traditional methods often grapple with issues related to data security, integrity, and accessibility. This paper introduces a groundbreaking approach to pediatric medical data management by leveraging the robust capabilities of blockchain, Non-Fungible Tokens (NFTs), InterPlanetary File System (IPFS), and distributed ledgers. Our proposed model meticulously addresses the limitations of the conventional systems by ensuring data immutability, transparency, and decentralized control. Starting with the creation of a unique Global ID for children, we outline a detailed 10-step approach to data storage, query, and update, emphasizing the pivotal roles of smart contracts and NFTs in guaranteeing data authenticity and uniqueness. The implementation section delves deeper into the intricacies of transaction creation, data query, and update mechanisms, underscoring the importance of secure interfaces, rigorous verification processes, and seamless synchronization with decentralized storage solutions. With the confluence of these advanced technologies, our approach promises a transformative shift in pediatric healthcare, simplifying processes for healthcare professionals and ensuring data security and privacy for patients.
Fast and reliable identification of cyber attacks in network systems of smart cities is currently a critical and demanding task. Machine learning algorithms have been used for intrusion detection, but the existing data sets intended for their training are often imbalanced, which can reduce the effectiveness of the proposed model. Oversampling and undersampling techniques can solve the problem but have limitations, such as the risk of overfitting and information loss. Furthermore, network data logs are noisy and inconsistent, making it challenging to capture essential patterns in the data accurately. To address these issues, this study proposes using Generative Adversarial Networks to generate synthetic network traffic data. The results offer new insight into developing more effective intrusion detection systems, especially in the context of smart cities' network infrastructure.
The field of Artificial Intelligence (AI) has a significant impact on the way computers and humans interact. The topic of (facial) emotion recognition has gained a lot of attention in recent years. Majority of research literature focuses on improvement of algorithms and Machine Learning (ML) models for single data sets. Despite the impressive results achieved, the impact of the (training) data quality with its potential biases and annotation discrepancies is often neglected. Therefore, this paper demonstrates an approach to detect and evaluate annotation label discrepancies between three separate (facial) emotion recognition databases by Transfer Testing with three ML architectures. The findings indicate Transfer Testing to be a new promising method to detect inconsistencies in data annotations of emotional states, implying label bias and/or ambiguity. Therefore, Transfer Testing is a method to verify the transferability of trained ML models. Such research is the foundation for developing more accurate AI-based emotion recognition systems, which are also robust in real-life scenarios.
This study proposes an integrated approach to image similarity measurement by extending traditional methods that concentrate on local features to incorporate global information. Global information, including background, colors, spatial representation, and object relations, can leverage the ability to distinguish similarity based on the overall context of an image using natural process techniques. We employ Video-LLaMA model to extract textual descriptions of images through question prompts, and apply cosine similarity metrics, BERTScore, to quantify image similarities. We conduct experiments on images of the same and different topics using various pre-trained language model configurations. To validate the coherence of the generated text descriptions with the actual theme of the image, we generate images using DALL-E 2 and evaluate them using human judgement. Key findings demonstrate the effectiveness of pre-trained language models in distinguishing between images depicting similar and different topics with a clear gap in similarity.
This article presents singularization, a new family of Moving Target Defense (MTD) strategy that we propose to strengthen the robustness of sensitive applets on SIMs without needing a full replacement of SIMs.
A gait provides the characteristics of a person’s walking style and hence is classified as personal identifiable information. There have been several studies for personal identification using gait, including works using hardware such as depth sensors and studies using silhouette image sequences of gait. However, these methods were designed specialized for tracking a single walking person and the accuracy reduction when multiple people are simultaneously reflected in several angles of view is not clear yet. In addition, dependencies on hardware-based methods is not clarified yet. In this study, we focus on Kinect and OpenPose, the representative gait identification techniques with a function to detect multiple people simultaneously in real time. We investigate how many people can be identified for these devices and with the accuracy for tracking.
Recent advances in Artificial Intelligence (AI) have accelerated the adoption of AI at a pace never seen before. Large Language Models (LLM) trained on tens of billions of parameters show the crucial importance of parallelizing models. Different techniques exist for distributing Deep Neural Networks but they are challenging to implement. The cost of training GPU-based architectures is also becoming prohibitive. In this document we present a distributed approach that is easier to implement where data and model are distributed in processing units hosted on a cluster of machines based on CPUs or GPUs. Communication is done by message passing. The model is distributed over the cluster and stored locally or on a datalake. We prototyped this approach using open sources libraries and we present the benefits this implementation can bring.
Most widely-used protocols for end-to-end security, such as TLS and its datagram variant DTLS, are highly computation-intensive and introduce significant communication overheads, which makes them impractical for resource-restricted IoT devices. The recently-introduced Disco protocol framework provides a clean and well-documented basis for the design of strong end-to-end security with lower complexity than the (D)TLS protocol and no legacy baggage. Disco consists of two sub-protocols, namely Noise (known from e.g., WhatsApp) and Strobe, and is rather minimalist in terms of cryptography since it requires only an elliptic curve in Montgomery form and a cryptographic permutation as basic building blocks. In this paper, we present IoTDisco, an optimized implementation of the Disco protocol for 16-bit TI MSP430 microcontrollers. IoTDisco is based on David Wong’s EmbeddedDisco software and contains hand-written Assembly code for the prime-field arithmetic of Curve25519. However, we decided to replace the Keccak permutation of EmbeddedDisco by Xoodoo to reduce both the binary code size and RAM footprint. The experiments we conducted on a Zolertia Z1 device (equipped with a MSP430F2617 microcontroller) show that IoTDisco is able to perform the computational part of a full Noise NK handshake in 26.2 million clock cycles, i.e., 1.64 s when the MSP430 is clocked at 16 MHz. IoTDisco’s RAM footprint amounts to 1.4 kB, which is less than 17
In urban environments, traffic networks are characterized by fixed distances between nodes, representing intersections or landmarks. Efficiently identifying the shortest path between any two nodes is crucial for various applications, such as route optimization for emergency services, ride-sharing algorithms, and general traffic management. Traditional methods like Dijkstra's algorithm are computationally intensive, especially for large-scale networks. To address this challenge, we propose a novel approach that precomputes and stores the shortest paths in a dedicated database hosted on a server system. Our methodology leverages the RAO algorithm, an advanced optimization technique, to solve the shortest path problem. Unlike conventional methods, the RAO algorithm adapts to varying conditions and constraints, making it highly suitable for dynamic urban traffic networks. We construct a comprehensive database that contains pre-calculated shortest paths between any two nodes, thereby significantly reducing real-time computational load. To validate the effectiveness of our approach, we conducted experiments on networks of varying complexities: 6-node, 8-node, and 20-node configurations. These experiments serve to emulate different scales of urban traffic networks. We compared the performance of our RAO-based solution with the Particle Swarm Optimization (PSO) algorithm, using Dijkstra's algorithm as a baseline for evaluation. Our results indicate a marked improvement in computational efficiency and accuracy when using the RAO algorithm. Specifically, the RAO-based solution outperformed the PSO algorithm across all test cases, thereby confirming its suitability for real-world applications. Our research introduces a scalable and efficient solution for precomputing shortest paths in urban traffic networks using the RAO algorithm.
The domain of animal healthcare mandates robust mechanisms for maintaining the sanctity, reachability, and security of medical record. This paper delineates a cutting-edge methodology to overhaul traditional animal medical record handling by utilizing blockchain techniques. Through the strategic incorporation of Non-Fungible Tokens (NFTs), the InterPlanetary File System (IPFS), and Smart Contracts, we propose a versatile system that refines data retrieval and modification processes, bolstering both accountability and dependability. At the heart of our strategy lies a pioneering decentralized framework, empowering veterinary professionals with the tools to input, retrieve, and edit medical records, all the while being enveloped by rigorous access and identity validation measures. The inherent decentralized properties of IPFS furnish steadfast and immutable data retention capabilities, whilst the NFTs encapsulate the distinct medical trajectories of each animal. Through the symbiotic relationship of Smart Contracts, a fluid and unalterable lineage of medical logs is preserved. As a marked departure from traditional paradigms, our blueprint promises augmented safety, streamlined data operations, and unparalleled lucidity, marking the dawn of a transformative phase in animal healthcare.
Deep Learning (DL) techniques are effective for designing network intrusion detection systems (NIDS) but they lack leveraging IoT network topology. In the meanwhile, Graph Neural Networks (GNNs) consider both statistical properties and topological dependencies outperforming DL in complex IoT systems. However, three improvements are required: 1) Scalability as GNNs are more suitable for offline analysis with a static dependency graph. 2) Current GNNs focus on homogeneous graphs with topological dependencies; thus, including temporal aspects in heterogeneous graphs would improve the overall performance. 3) IoT time and resource constraints require optimized resource usage for efficient intrusion detection. To address these challenges, we propose StrucTemp-GNN a dynamic heterogeneous GNN-based NIDS for IoT networks. The method leverages both structural and temporal dependencies, giving rise to its name, Structural-Temporal GNN. Real-time intrusion detection is enabled by constructing a dynamic graph from incoming IoT data flows, incorporating structural and temporal information. The lightweight GNN model achieves fast and accurate intrusion detection. It has been evaluated on four new IoT datasets and has proven efficient in both binary and multiclass classification.
In recent years, there has been an alarming increase in cyberattacks targeting connected medical devices. Distributed denial of service (DDoS) and botnet attacks are particularly common, and many vulnerabilities in IoT systems make these devices particularly vulnerable. Traditional intrusion detection techniques often fall short in addressing these threats. To overcome this challenge, we propose a deep learning-based intrusion detection system (IDS) for connected medical devices that utilizes four different architectures: multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and hybrid CNN-LSTM. We evaluated our system on the UNSW-NB15 and Edge-IIoTset datasets, and achieved a classification accuracy of 99.8
To address the increasing complexity of network management and the limitations of data repositories in handling the various network operational data, this paper proposes a novel repository design that uniformly represents network operational data while allowing for a multiple abstractions access to the information. This smart repository simplifies network management functions by enabling network verification directly within the repository. The data is organized in a knowledge graph compatible with any general-purpose graph database, offering a comprehensive and extensible network repository. Performance evaluations confirm the feasibility of the proposed design. The repository's ability to natively support 'what-if' scenario evaluation is demonstrated by verifying Border Gateway Protocol (BGP) route policies and analyzing forwarding behavior with virtual Traceroute.
Large-scale mainframe applications written in outdated languages such as COBOL still form the core of the enterprise IT in many organizations, even though their flexibility and maintainability declines continuously. Their manual re-implementation in modern languages like Java is usually economically not feasible. Automated code conversion of legacy programs usually produces poor quality code in the target language, even with recent AI tools such as ChatGPT. In addition, code conversion recovers dead or unnecessary code artifacts in the new language. Therefore, in this paper we explore a novel approach, which does not convert the legacy code, but instead uses the existing input/output data to generate program tokens through program synthesis. These tokens are subsequently translated into input tokens and submitted to ChatGPT to produce the target code. The approach is illustrated and evaluated by means of a semi-realistic example program. The obtained results look promising, but need to be further investigated.
Privacy enhancing technologies (PETs) have been proposed as a way to protect the privacy of data while still allowing for data analysis. In this work, we focus on Fully Homomorphic Encryption (FHE), a powerful tool that allows for arbitrary computations to be performed on encrypted data. FHE has received lots of attention in the past few years and has reached realistic execution times and correctness. More precisely, we explain in this paper how we apply FHE to tree-based models and get state-of-the-art solutions over encrypted tabular data. We show that our method is applicable to a wide range of tree-based models, including decision trees, random forests, and gradient boosted trees, and has been implemented within the Concrete-ML library, which is open-source at https://github.com/zama-ai/concrete-ml. With a selected set of use-cases, we demonstrate that our FHE version is very close to the unprotected version in terms of accuracy.
Wireless sensor networks and Internet of Things (IoT) are part of dynamic networks as new nodes can join while existing members can leave the system at any time. These networks mainly suffer from severe resource constraints like energy, storage and computation, which makes securing communications between nodes a real challenge. Several key establishment protocols have been proposed in the literature. Some of them are based on symmetric polynomials. However, the latter solutions have some limitations, such as the resilience to node capture attacks as well as the storage and computation overheads that are high for constrained nodes. In this paper, we propose a lightweight polynomial-based key management scheme for dynamic networks. The proposed scheme allows nodes to be able to establish secure communications between them, and ensures dynamism by supporting node addition and deletion after the setup phase. It also resists to node capture attack. The performance evaluation shows that our scheme reduces both the storage and computation overheads when compared to other related polynomial-based protocols.
Over the past few years, we have seen the emergence of a wide range of banking architectures, technologies, and applications made possible by the significant improvements in hardware, software, and networking technologies. Nowadays, innovative solutions are being developed by banks to leverage the benefits of blockchain, to improve their business agility and performance, and to make their business operations more efficient and secure. However, there are still cases where regular access to Internet is impossible or unreliable due to saturated networks or harsh environments, hence limiting the deployment of typical blockchain based solutions. In this context, an approach using a new connectivity technology is needed in order to increase mobile Internet services for any device to reach nearly 95% of the world population, instantly, simply by drawing on existing mobile phone networks, with no additional infrastructure development. We aim to give the user full bank access from their device, even if the device is not a smart one, using ordinary mobile phone networks. However, providing efficient and secure communications over lossy and low bandwidth networks remains a challenge. The main objective of this paper will be to design an end-to-end and low overhead secure solution for the communications between mobile devices and their corresponding remote application servers that using blockchain via ordinary mobile networks .
Nowadays, medical healthcare always plays a vital role for humans in society, especially problems related to personal health records due to its security and sensitivity. For each patient, personal health records are critical and vital assets, so how to manage them effectively is becoming exciting research to solve. Many types of research in aspects of managing and operating personal health records have been introduced; however, dealing with patients’ data in emergency cases remains an uncertain issue. When emergencies happen in reality, using a traditional access system is very hard for patients to give consent to staff to access their data. Besides, there is no secured record management of patient’ data, which reveals highly confidential personal information, such as what happened, when, and who has access to such information. Therefore, in this paper, an emergency access control management system is proposed to protect the patients’ data. This system is built based on permissioned Blockchain Hyperledger fabric. The proposed system will define several rules and regulations by using smart contracts and time duration to deal with emergencies. The patients also restrict the time to access the data in such urgent cases. Several algorithms that represent how the system works are also provided to make readers understand about the proposed management system.
One of the most challenging problems in Cybersecurity is the identification and prevention of port scanning, which is the primary phase of further system or data exploitation. This paper proposes a new statistical method for port scan detection, in addition to preventive and corrective counter-measures. The suggested solution is intended to be implemented at the Internet Service Provider (ISP) side. The proposed solution consists of aggregating NetFlow statistics and using the Z-score and co-variance measures to detect port scan traffic as a deviation from normal traffic. The experimental results show that the proposed method achieves a high detection rate (up to 100%) within a time frame of 60 s.
Users pay to use resources in cloud systems which makes them more demanding on performance and costs. Optimizing the response time of the applications and meeting user's budget needs are therefore critical requirements when scheduling applications.The approach presented in this work is a scheduling based-HEFT algorithm, which aims to optimize the makespan of tasks workflow that is constrained by the budget. For this, we propose a new budget distribution strategy named Estimated task budget that we integrate in our budget-aware HEFT algorithm. We use a multiple datacenters cloud as a real platform model, where data transfer costs are considered. The results obtained by our algorithm relative to recent work, show an improvement of makespan in the case of a restricted budget, without exceeding the given budget.
