A Secured Industrial Internet-of-Things Architecture Based on Blockchain Technology and Machine Learning for Sensor Access Control Systems in Smart Manufacturing

In this paper, a layered architecture incorporating Blockchain technology (BCT) and Machine Learning (ML) is proposed in the context of the Industrial Internet-of-Things (IIoT) for smart manufacturing applications. The proposed architecture is composed of five layers covering sensing, network/protocol, transport enforced with BCT components, application and advanced services (i.e., BCT data, ML and cloud) layers. BCT enables gathering sensor access control information, while ML brings its effectivity in attack detection such as DoS (Denial of Service), DDoS (Distributed Denial of Service), injection, man in the middle (MitM), brute force, cross-site scripting (XSS) and scanning attacks by employing classifiers differentiating between normal and malicious activity. The design of our architecture is compared to similar ones in the literature to point out potential benefits. Experiments, based on the IIoT dataset, have been conducted to evaluate our contribution, using four metrics: Accuracy, Precision, Sensitivity and Matthews Correlation Coefficient (MCC). Artificial Neural Network (ANN), Decision Tree (DT), Random Forest, Naive Bayes, AdaBoost and Support Vector Machine (SVM) classifiers are evaluated regarding these four metrics. Even if more experiments are required, it is illustrated that the proposed architecture can reduce significantly the number of DDoS, injection, brute force and XSS attacks and threats within an advanced framework for sensor access control in IIoT networks based on a smart contract along with ML classifiers

A Secured Industrial Internet-of-Things Architecture Based on Blockchain Technology and Machine Learning for Sensor Access Control Systems in Smart Manufacturing

In this paper, a layered architecture incorporating Blockchain technology (BCT) and Machine Learning (ML) is proposed in the context of the Industrial Internet-of-Things (IIoT) for smart manufacturing applications. The proposed architecture is composed of five layers covering sensing, network/protocol, transport enforced with BCT components, application and advanced services (i.e., BCT data, ML and cloud) layers. BCT enables gathering sensor access control information, while ML brings its effectivity in attack detection such as DoS (Denial of Service), DDoS (Distributed Denial of Service), injection, man in the middle (MitM), brute force, cross-site scripting (XSS) and scanning attacks by employing classifiers differentiating between normal and malicious activity. The design of our architecture is compared to similar ones in the literature to point out potential benefits. Experiments, based on the IIoT dataset, have been conducted to evaluate our contribution, using four metrics: Accuracy, Precision, Sensitivity and Matthews Correlation Coefficient (MCC). Artificial Neural Network (ANN), Decision Tree (DT), Random Forest, Naive Bayes, AdaBoost and Support Vector Machine (SVM) classifiers are evaluated regarding these four metrics. Even if more experiments are required, it is illustrated that the proposed architecture can reduce significantly the number of DDoS, injection, brute force and XSS attacks and threats within an advanced framework for sensor access control in IIoT networks based on a smart contract along with ML classifiers

An Intra-algorithm Comparison Study of Complete Search FSM Implementations in Centralized Graph Transaction Databases

International audienc

System-Level Performance Evaluation of Very High Complexity Media Applications : A H264/AVC Encoder Case Study

International audienc

LDA+: An Extended LDA Model for Topic Hierarchy and Discovery

International audienceThe success of topic modeling algorithms depends on their ability to analyze, index and classify large text corpora. These algorithms could be classified into two groups where the first one is oriented to classify textual corpus according to their dominant topics such as LDA, LSA and PLSA which are the most known techniques. The second group is dedicated to extract the relationships among the generated topics like HLDA, PAM and CTM. However, each algorithm among these groups is dedicated to a single task and there is no technique that makes it possible to carry out several analyses on textual corpus at the same time. In order to cope with this problem, we propose here a new technique based on LDA topic modeling to automatically classify a large text corpora according to their relevant topics, discover new topics (sub-topics) based on the extracted ones and hierarchy the generated topics in order to analyse data more deeply. Experiments have been conducted to measure the performance of our solution compared to the existing techniques. The results obtained are more than satisfactory

A Model-Driven Approach for Hybrid Power Estimation in Embedded Systems Design

International audienceAs technology scales for increased circuit density and performance, the management of power consumption in system-on-chip (SoC) is becoming critical. Today, having the appropriate electronic system level (ESL) tools for power estimation in the design flow is mandatory. The main challenge for the design of such dedicated tools is to achieve a better tradeoff between accuracy and speed. This paper presents a consumption estimation approach allowing taking the consumption criterion into account early in the design flow during the system cosimulation. The originality of this approach is that it allows the power estimation for both white-box intellectual properties (IPs) using annotated power models and black-box IPs using standalone power estimators. In order to obtain accurate power estimates, our simulations were performed at the cycle-accurate bit-accurate (CABA) level, using SystemC. To make our approach fast and not tedious for users, the simulated architectures, including standalone power estimators, were generated automatically using a model driven engineering (MDE) approach. Both annotated power models and standalone power estimators can be used together to estimate the consumption of the same architecture, which makes them complementary. The simulation results showed that the power estimates given by both estimation techniques for a hardware component are very close, with a difference that does not exceed 0.3%. This proves that, even when the IP code is not accessible or not modifiable, our approach allows obtaining quite accurate power estimates that early in the design flow thanks to the automation offered by the MDE approach