1,720,992 research outputs found
Automatic adaptation of Proper Noun Dictionaries through cooperation of machine learning and probabilistic methods
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A Role of RMAS, Blockchain, and Zero-Knowledge Proof in Sustainable Supply Chains
No full textBlockchain technologies and paradigms can provide new ground for the management of complex socio-technical problems like supply chains. The contribution of this work is a work-in-progress experimental setting to demonstrate the effectiveness of a novel distributed business process management scheme that integrates the RMAS (Relational-model Multi Agent System) and the Blockchain frameworks. The research work is conducted in a food supply chain case study aiming to sustainability and social good through a purposeful organisation of the autonomous behaviour of the actors in the system
Will Very Large Corpora Play For Semantic Disambiguation The Role That Massive Computing Power Is Playing For Other AI-Hard Problems?"
No full textParadigms for database-centric application interfaces
Full text linkThe database-centric approach for industrial applications in the fourth industrial revolution has been proposed as a viable possibility in view of new trends towards distributed, autonomic, and intelligent control systems. In particular, with the RMAS architecture and its compliance to the new directions envisioned by the IEC 61499 standard, a suitable advanced instance of the database-centric paradigm was achieved. In this work, the focus is on the aspects that concern the impact of the database-centric paradigm in the realm of design of human-machine and machine-to-machine interfaces. A discussion of the implications and an example are provided in order to let the industrial informatics community start with an assessment of the proposed vision
Feature selection in ML-based SDN intrusion detection system
No full textWithin the branch of Software-Defined Networking (SDN), research in Cyber Security has underscored the pressing need to combat cyber-attacks. These crimes include the unauthorized access and manipulation of critical data, jeopardizing user confidentiality, authenticity, and system integrity. To address these challenges, the deployment of Intrusion Detection Systems (IDS) has become paramount. These systems play a crucial role in safeguarding both the SDN infrastructure and its users. IDSs operate much like classification systems, making them suitable for the application of machine learning techniques in identifying intrusions. These techniques rely on labeled datasets to train the system to differentiate between benign and malicious events based on various features. Once trained, the system can categorize new events as benign or malicious. Therefore, identifying which features are relevant for classification purposes is crucial. In the current literature, few studies have focused on the effectiveness of IDSs applied to SDNs. The performance evaluation of IDSs based on machine learning techniques within SDN environments involves the development of specialized datasets, comprising network traffic features essential for discerning attack patterns. Moreover, as the landscape of network attacks within SDN evolves, there arises a need for continuously updated datasets to evaluate IDS effectiveness. This paper aims to investigate which features are relevant to detect the most common attack types in an SDN. To do this, labeled datasets of network traffic in an SDN must be available. Unfortunately, to the best of our knowledge, there is only one publicly available dataset for SDN traffic: InSDN. In this paper, we present the result of a feature selection process on the InSDN dataset, based on the SHAP toolset, aimed at identifying the most relevant features for different types of attacks. We also compare the performances of different classification algorithms trained on both the full dataset and the reduced one, showing that, for many attack types, the classifiers performances are comparable
Leveraging n-gram neural embeddings to improve deep learning DGA detection
No full textSeveral families of malware are based on the need to establish a connection with a Command and Control (C&C) server. In addition, to avoid detection, these servers "hide" behind domain names that are periodically changed according to a specific Domain Generation Algorithm (DGA). Hence, the malware that has infected a particular host uses the same DGA to make DNS queries in order to establish a connection with the C&C server. The identification of "malicious" domain names used in DNS queries is therefore crucial for their detection. For this purpose, various machine learning techniques have been used, in particular, recently, deep learning techniques have proved especially effective. However, to get good results, these techniques require very large and labelled training datasets. Nevertheless, the construction of such datasets, decidedly with regard to the collection of malicious domain names, is a very difficult and nonscalable task. In this paper, therefore, we explore the possibility of exploiting unsupervised character n-gram embeddings to improve the performance of a Deep Learning DGA classifier. Embeddings are trained using a large dataset of benign names, opening up the possibility of using a small classifier training dataset requiring a small number of malicious names. A series of experiments, which use the same embedding for classifiers trained with datasets of increasing size, are then presented. These experiments show how the embedding is particularly effective for classifiers trained with small datasets having a small number of malicious names
Measurement of stride time by machine learning: sensitivity analysis for the simplification of the experimental protocol
No full textLimited stride-time variability is considered a marker of safe walking. Thus, the measurement of stride time is a meaningful information for gait analysis. The use of machine-learning (ML) techniques has been proven to be useful to this aim, even if the amount of data provided as input influences the computation process. The present study is aiming to analyze the sensitivity of the experimental protocol (number of sensors and signals) on the performance of a stride-time measurement system based on ML interpretation of surface EMG signals (sEMG). To this purpose, sEMG signals from ten leg muscles of 30 volunteers are used to train a single-layer neural network. Five experimental protocols (from five to one sEMG sensors per leg) are comparatively tested. Results show that reducing the sEMG-protocol complexity (less sensors utilized) is decreasing the prediction performances. Based on the test results, this study proposes an experimental protocol composed of two sEMG sensors per leg (over gastrocnemius lateralis and tibialis anterior), as the best compromise between the need of a simplified experimental set-up and the necessity of high performances (F1-score±SD = 99.0±1.2%; mean absolute value, MAE±SD = 17.9±4.3 ms). The use of only two sEMG probes is going to have a great impact on gait analysis, improving patient comfort and reducing clinical costs and time consumption. A possible, further reduction of experimental protocol to a single muscle (gastrocnemius lateralis) is feasible accepting a less efficient prediction of the stride-time
A Comparative Analysis of Datasets for Intrusion Detection in Software-Defined Networks
No full textSoftware-Defined Networking (SDN) offers centralized management, programmability, flexibility and scalability but has significant security risks, especially DDoS attacks against the SDN controller, threatening network availability. Machine learning (ML) and deep learning (DL) show promise in mitigating these threats, but their success depends on available datasets quality. Existing SDN datasets often focus narrowly on specific DDoS scenarios or synthetic environments, limiting their real-world applicability. This paper analyzes SDN threats datasets, evaluating their methodologies, features and ML applications. It highlights strengths like realistic traffic emulation and accessibility, alongside limitations such as narrow attack coverage and synthetic biases. A roadmap is proposed to guide the generation of new datasets, emphasizing diverse attacks, richer features, realistic augmentation and public access to enable robust ML/DL-based SDN security solutions
A Soulbound Token-based Reputation System in Sustainable Supply Chains
No full textThis paper aims to provide novel insights in the use of recent advances about non-fungible and Soulbound tokens, as they are a growing reality in the context of the DLT framework. In particular, this work discusses how these technological provisions can enable a renovated and strengthened role of the Internet of Everything concept in the complex processes of the Industry 5.0, where the social, societal, and technical dimensions merge into an irreducible applicative context. The potentialities of the approach are expressed by means of a simple but meaningful example on an industrial case study concerning the food supply chain
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