Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of machine learning in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using machine learning models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a machine learning based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods

Selective demolition of masonry unit walls with a soundless chemical demolition agent

A soundless chemical demolition agent was applied for selective demolition to unit masonry [2 full-scale concrete brick walls in Type N mortar and 2 wallettes in lime mortar – 1 historic brick and 1 concrete brick]. Typically, cracking began shortly after 9 h and ultimately produced an average crack length of 418 mm per hole and an average maximum 5.22 mm crack width. Samples in Type-N mortar exhibited slower but significantly more cracks and wider cracking. Ninety-three percent of cracking occurred within 4 days. No masonry units were damaged and partial demolition was successful, although selective unit removal was not due to confinement.Science Foundation Ireland -- replac

Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods. - 2014 IEEE.Manuscript received January 16, 2019; revised April 1, 2019 and April 12, 2019; accepted April 13, 2019. Date of publication April 18, 2019; date of current version July 31, 2019. This work was supported by NPRP through the Qatar National Research Fund (a member of Qatar Foundation) under Grant NPRP 10-901-2-370. The work of M. A. Teixeira was supported in part by the São Paulo Research Foundation (FAPESP) under Grant 2017/01055-4 and in part by the Instituto Federal de Educação, Ciência e Tecnologia de São Paulo. (Corresponding author: Maede Zolanvari.)Scopu

Efficient virtual network function placement strategies for Cloud Radio Access Networks

The new generation of 5G mobile services place stringent requirements for cellular network operators in terms of latency and costs. The latest trend in radio access networks (RANs) is to pool the baseband units (BBUs) of multiple radio base stations and to install them in a centralized infrastructure, such as a cloud, for statistical multiplexing gains. The technology is known as Cloud Radio Access Network (CRAN). Since cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in the telecommunication industry, CRAN is being heralded as a candidate technology to meet the expectations of radio access networks for 5G. In CRANs, low energy base stations (BSs) are deployed over a small geographical location and are connected to a cloud via finite capacity backhaul links. Baseband processing unit (BBU) functions are implemented on the virtual machines (VMs) in the cloud over commodity hardware. Such functions, built in software, are termed as virtual functions (VFs). The optimized placement of VFs is necessary to reduce the total delays and minimize the overall costs to operate CRANs. Our study considers the problem of optimal VF placement over distributed virtual resources spread across multiple clouds, creating a centralized BBU cloud. We propose a combinatorial optimization model and the use of two heuristic approaches, which are, branch-and-bound (BnB) and simulated annealing (SA) for the proposed optimal placement. In addition, we propose enhancements to the standard BnB heuristic and compare the results with standard BnB and SA approaches. The proposed enhancements improve the quality of the solution in terms of latency and cost as well as reduce the execution complexity significantly. We also determine the optimal number of clouds, which need to be deployed so that the total links delays, as well as the service migration delays, are minimized, while the total cloud deployment cost is within the acceptable limits.This publication was made possible by the NPRP award [ NPRP 8-634-1-131 ] from the Qatar National Research Fund (a member of The Qatar Foundation) and NSF Grant CNS-1718929 . The statements made herein are solely the responsibility of the author[ s ]

TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security

Despite AI’s significant growth, its “black box" nature creates challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in IoT high-risk applications, such as critical industrial infrastructures, medical systems, and financial applications, etc. Explainable AI (XAI) has emerged to help with this problem. However, designing appropriately fast and accurate XAI is still challenging, especially in numerical applications. Here, we propose a universal XAI model named Transparency Relying Upon Statistical Theory (TRUST), which is model-agnostic, high-performing, and suitable for numerical applications. Simply put, TRUST XAI models the statistical behavior of the AI’s outputs in an AI-based system. Factor analysis is used to transform the input features into a new set of latent variables. We use mutual information to rank these variables and pick only the most influential ones on the AI’s outputs and call them “representatives” of the classes. Then we use multi-modal Gaussian distributions to determine the likelihood of any new sample belonging to each class. We demonstrate the effectiveness of TRUST in a case study on cybersecurity of the industrial Internet of things (IIoT) using three different cybersecurity datasets. As IIoT is a prominent application that deals with numerical data. The results show that TRUST XAI provides explanations for new random samples with an average success rate of 98%. Compared with LIME, a popular XAI model, TRUST is shown to be superior in the context of performance, speed, and the method of explainability. In the end, we also show how TRUST is explained to the user.Scopu

Slicing Method for curved façade and window extraction from point clouds

Laser scanning technology is a fast and reliable method to survey structures. However, the automatic conversion of such data into solid models for computation remains a major challenge, especially where non-rectilinear features are present. Since, openings and the overall dimensions of the buildings are the most critical elements in computational models for structural analysis, this article introduces the Slicing Method as a new, computationally-efficient method for extracting overall façade and window boundary points for reconstructing a façade into a geometry compatible for computational modelling. After finding a principal plane, the technique slices a façade into limited portions, with each slice representing a unique, imaginary section passing through a building. This is done along a façade’s principal axes to segregate window and door openings from structural portions of the load-bearing masonry walls. The method detects each opening area’s boundaries, as well as the overall boundary of the façade, in part, by using a one-dimensional projection to accelerate processing. Slices were optimised as 14.3 slices per vertical metre of building and 25 slices per horizontal metre of building, irrespective of building configuration or complexity. The proposed procedure was validated by its application to three highly decorative, historic brick buildings. Accuracy in excess of 93% was achieved with no manual intervention on highly complex buildings and nearly 100% on simple ones. Furthermore, computational times were less than 3 sec for data sets up to 2.6 million points, while similar existing approaches required more than 16 hr for such datasets.European Research Counci

Impact of thermal transfer on hydration heat of a Soundless Chemical Demolition Agent

This paper explores thermal transfer effects in Soundless Chemical Demolition Agents (SCDA). In a 10°C water bath, quadrupling the volume of SCDA in a pipe accelerated peak hydration onset and resulted in a 700% increase in expansive pressure and a 20% increase in volumetric expansion. An equivalent sample in a constant temperature chamber showed an almost 5°C greater hydration heat than in the water bath, which resulted in a six-fold expansive pressure difference after 4 days of testing and an order of magnitude more pressure in the first 24 h, thereby demonstrating limitations of previous SCDA experimental work and providing a temperature-based reason for discrepancies between large-scale testing and manufacturers’ predictions. Since most construction projects have scheduling requirements, understanding how to achieve sufficiently high pressures within a single work shift is important for evaluating the field viability of SCDAs on a particular project.Enterprise IrelandScience Foundation Ireland24 month embargo - A

Three-dimensional building façade segmentation and opening area detection from point clouds

Laser scanning generates a point cloud from which geometries can be extracted, but most methods struggle to do this automatically, especially for the entirety of an architecturally complex building (as opposed to that of a single façade). To address this issue, this paper introduces the Improved Slicing Method (ISM), an innovative and computationally-efficient method for three-dimensional building segmentation. The method is also able to detect opening boundaries even on roofs (e.g. chimneys), as well as a building’s overall outer boundaries using a local density analysis technique. The proposed procedure is validated by its application to two architecturally complex, historic brick buildings. Accuracies of at least 86% were achieved, with computational times as little as 0.53 s for detecting features from a data set of 5.0 million points. The accuracy more than rivalled the current state of the art, while being up to six times faster and with the further advantage of requiring no manual intervention or reliance on a priori information.European Research Counci

Flow-based intrusion detection algorithm for supervisory control and data acquisition systems: A real-time approach

Intrusion detection in supervisory control and data acquisition (SCADA) systems is integral because of the critical roles of these systems in industries. However, available approaches in the literature lack representative flow-based datasets and reliable real-time adaption and evaluation. A publicly available labelled dataset to support flow-based intrusion detection research specific to SCADA systems is presented. Cyberattacks were carried out against our SCADA system test bed to generate this flow-based dataset. Moreover, a flow-based intrusion detection system (IDS) is developed for SCADA systems using a deep learning algorithm. We used the dataset to develop this IDS model for real-time operations of SCADA systems to detect attacks momentarily after they happen. The results show empirical proof of the model’s adequacy when deployed online to detect cyberattacks in real timeNational Science Foundation; Washington University in St. Louis;?Qatar National Research Fund; Funda o de Amparo Pesquisa do Estado de So Paulo; Qatar UniversityScopu

Green biosynthesis of silver nanoparticles by Spirulina platensis

Abstract Crystallized silver nanoparticles (SNPs) have been biosynthesized by Spirulina platensis in an aqueous system. An aqueous solution of silver ions was treated with a live biomass of Spirulina platensis for the formation of SNPs. These nanoparticles showed an absorption peak at 430 nm in the UV-visible spectrum, corresponding to the plasmon resonance of SNPs. The transmission electron micrographs of nanoparticles in an aqueous solution showed the production of SNPs (average size of most particles: ∼12 nm) by Spirulina platensis. The X-Ray Diffraction (XRD) spectrum of the nanoparticles confirmed the formation of metallic silver, and the average size of the crystallite was estimated from the peak profile by the Scherrer method. The synthesized SNPs had an average size of 11.6 nm