Biblio

This work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads; (2) build features from write patterns and key parameters of the system architecture and configurations; (3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10 x on some samples for both of the target systems.

The number of devices in the Internet of Things (IoT) immensely grew in recent years. A frequent challenge in the assurance of the dependability of IoT systems is that components of the system appear as a black box. This paper presents a semi-automatic testing methodology for black-box systems that combines automata learning and fuzz testing. Our testing technique uses stateful fuzzing based on a model that is automatically inferred by automata learning. Applying this technique, we can simultaneously test multiple implementations for unexpected behavior and possible security vulnerabilities.We show the effectiveness of our learning-based fuzzing technique in a case study on the MQTT protocol. MQTT is a widely used publish/subscribe protocol in the IoT. Our case study reveals several inconsistencies between five different MQTT brokers. The found inconsistencies expose possible security vulnerabilities and violations of the MQTT specification.

Phishing URLs mainly target individuals and/or organizations through social engineering attacks by exploiting the humans' weaknesses in information security awareness. These URLs lure online users to access fake websites, and harvest their confidential information, such as debit/credit card numbers and other sensitive information. In this work, we introduce a phishing detection technique based on URL lexical analysis and machine learning classifiers. The experiments were carried out on a dataset that originally contained 1056937 labeled URLs (phishing and legitimate). This dataset was processed to generate 22 different features that were reduced further to a smaller set using different features reduction techniques. Random Forest, Gradient Boosting, Neural Network and Support Vector Machine (SVM) classifiers were all evaluated, and results show the superiority of SVMs, which achieved the highest accuracy in detecting the analyzed URLs with a rate of 99.89%. Our approach can be incorporated within add-on/middleware features in Internet browsers for alerting online users whenever they try to access a phishing website using only its URL.

With the development of communication technology, the disadvantages of traditional notification methods such as low efficiency gradually appear. With the introduction of WAP with WTLS security and its development and maintenance, more and more notification systems are using this technology. Through the analysis, design and implementation of notification system for large user groups, this paper studies how to collect and notify data without affecting the business system, and proposes a scheme of real-time data acquisition and filtering based on trigger. The middleware and application server implementation transaction management and database operation to separate CICS middleware technology based on research using UNIXC, Socket programming, SQL statements, SYBASE database technology, from the system requirements, business process, function structure, database and data structure, the input and output of the system, system testing the aspects such as design of practical significance to intelligent notification system for large user groups. Finally, the paper describes the test effect of the system in detail. 10 users send 1, 5, 10 and 20 strokes at the same time, and the completion time is 0.28, 1.09, 1.58 and 2.20 seconds, which proves that the system has practical significance.

Web Scraping is the technique of extracting desired data in an automated way by scanning the internal links and content of a website, this activity usually performed by systematically programmed bots. This paper explains our proposed solution to protect the blog content from theft and from being copied to other destinations by mitigating the scraping bots. To achieve our purpose we applied two steps in two levels, the first one, on the main blog page level, mitigated the work of crawler bots by adding extra empty articles anchors among real articles, and the next step, on the article page level, we add a random number of empty and hidden spans with randomly generated text among the article's body. To assess this solution we apply it to a local project developed using PHP language in Laravel framework, and put four criteria that measure the effectiveness. The results show that the changes in the file size before and after the application do not affect it, also, the processing time increased by few milliseconds which still in the acceptable range. And by using the HTML-similarity tool we get very good results that show the symmetric over style, with a few bit changes over the structure. Finally, to assess the effects on the bots, scraper bot reused and get the expected results from the programmed middleware. These results show that the solution is feasible to be adopted and use to protect blogs content.

Middleware for IoT (Internet of Things) helps application developers face challenges, such as device heterogeneity, service interoperability, security and scalability. While extensively adopted nowadays, IoT middleware systems are static because, after deployment, updates are only possible by stopping the thing. Therefore, adaptive capabilities can improve existing solutions by allowing their dynamic adaptation to changes in the environmental conditions, evolve provided functionalities, or fix bugs. This paper presents AMoT, an adaptive publish/subscribe middleware for IoT whose design and implementation adopt software architecture principles and evolutive adaptation mechanisms. The experimental evaluation of AMoT helps to measure the impact of the proposed adaptation mechanisms while also comparing the performance of AMoT with a widely adopted MQTT (Message Queuing Telemetry Transport) based middleware. In the end, adaptation has an acceptable performance cost and the advantage of tunning the middleware functionality at runtime.

This paper describes the architecture and the fundamental methodology of an anomaly detector, which by continuously monitoring Simple Network Management Protocol data and by processing it as complex-events, is able to timely recognize patterns of faults and relevant cyber-attacks. This solution has been applied in the context of smart grids, and in particular as part of a security and resilience component of the Information and Communication Technologies (ICT) Gateway, a middleware-based architecture that correlates and fuses measurement data from different sources (e.g., Inverters, Smart Meters) to provide control coordination and to enable grid observability applications. The detector has been evaluated through experiments, where we selected some representative anomalies that can occur on the ICT side of the energy distribution infrastructure: non-malicious faults (indicated by patterns in the system resources usage), as well as effects of typical cyber-attacks directed to the smart grid infrastructure. The results show that the detection is promisingly fast and efficient.

Modern Intelligent Transport Systems (ITSs) are comprehensive applications that have to cope with a multitude of challenges while meeting strict service and security standards. A novel data-centric middleware that provides the foundation of such systems is presented in this paper. This middleware is designed for high scalability, fast data processing and multimodality. To achieve these goals, an innovative spatial annotation (SpatiaIJSON) is utilised. SpatialJSON allows the representation of geometry, topology and traffic information in one dataset. Data processing is designed in such a manner that any schema or ontology can be used to express information. Further, common concerns of ITSs are addressed, such as authenticity of messages. The core task, however, is to ensure a quick exchange of evaluated information between the individual traffic participants.

In mobile edge computing, edge devices collect data, and an edge server performs computational or data processing tasks that need real-time processing. Depending upon the requested task's complexity, an edge server executes it locally or remotely in the cloud. When an edge server needs to offload its computational tasks, there could be a sudden failure in the cloud or network. In this scenario, we need to provide a flexible execution model to edge devices and servers for the continuous execution of the task. To that end, in this paper, we induced a middleware system that allows an edge server to execute a task on the edge devices instead of offloading it to a cloud server. Edge devices not only send data to an edge server for further processing but also execute edge services by utilizing nearby edge devices' computing resources. We extend the concept of service-oriented architecture and integrate a decentralized peer-to-peer network architecture to achieve reusability, location-specific security, and reliability. By following our methodology, software developers can enhance their application in a collaborative environment without worrying about low-level implementation.

This paper surveys a review of Internet of Things (IoT) protocols, Service oriented Middleware in IoT. The modern development of IoT, expected to create many divorce application in health care without human intervention. Various protocols are involved in the applications development. Researchers are doing research for desirable protocol with all functionalities. Middleware for an IoT provides interoperability between the devices or applications. The engineering of an IoT dependent on Service Oriented Architecture (SOA), it operates as middleware. We survey the existing SOA based IoT middleware and its functionalities.

System penetration testing is an important measure of discovering information system security issues. This paper summarizes and analyzes the high-risk problems found in the penetration testing of the artificial storm prediction system for power grid storm disasters from four aspects: application security, middleware security, host security and network security. In particular, in order to overcome the blindness of PGRDAIPS current SQL injection penetration test, this paper proposes a SQL blind bug based on improved second-order fragmentation reorganization. By modeling the SQL injection attack behavior and comparing the SQL injection vulnerability test in PGRDAIPS, this method can effectively reduce the blindness of SQL injection penetration test and improve its accuracy. With the prevalence of ubiquitous power internet of things, the electric power information system security defense work has to be taken seriously. This paper can not only guide the design, development and maintenance of disaster prediction information systems, but also provide security for the Energy Internet disaster safety and power meteorological service technology support.

Antifragile systems enhance their capabilities and become stronger when exposed to adverse conditions, stresses or attacks, making antifragility a desirable property for cyber defence systems that operate in contested military environments. Self-improvement in autonomic systems refers to the improvement of their self-* capabilities, so that they are able to (a) better handle previously known (anticipated) situations, and (b) deal with previously unknown (unanticipated) situations. In this position paper, we present a vision of using self-improvement through learning to achieve antifragility in autonomic cyber defence systems. We first enumerate some of the major challenges associated with realizing distributed self-improvement. We then propose a reference model for middleware frameworks for self-improving autonomic systems and a set of desirable features of such frameworks.

The interest over building security-based solutions to reduce the vulnerability exploits and mitigate the risks associated with smart homes in IoT is growing. However, our investigation identified to architect and implement distributed security mechanisms is still a challenge because is necessary to handle security and privacy in IoT middleware with a strong focus. Our investigation, it was identified the significant proportion of the systems that did not address security and did not describe the security approach in any meaningful detail. The idea proposed in this work is to provide middleware aim to implement security mechanisms in smart home and contribute as how guide to beginner developers' IoT middleware. The advantages of using MidSecThings are to avoid leakage data, unavailable service, unidentification action and not authorized access over IoT devices in smart home.

With the increasing interest in studying Automated Driving System (ADS)-equipped vehicles through simulation, there is a growing need for comprehensive and agile middleware to provide novel Virtual Analysis (VA) functions of ADS-equipped vehicles towards enabling a reliable representation for pre-deployment test. The National Institute of Standards and Technology (NIST) Universal Cyber-physical systems Environment for Federation (UCEF) is such a VA environment. It provides Application Programming Interfaces (APIs) capable of ensuring synchronized interactions across multiple simulation platforms such as LabVIEW, OMNeT++, Ricardo IGNITE, and Internet of Things (IoT) platforms. UCEF can aid engineers and researchers in understanding the impact of different constraints associated with complex cyber-physical systems (CPS). In this work UCEF is used to produce a simulated Operational Domain Design (ODD) for ADS-equipped vehicles where control (drive cycle/speed pattern), sensing (obstacle detection, traffic signs and lights), and threats (unusual signals, hacked sources) are represented as UCEF federates to simulate a drive cycle and to feed it to vehicle dynamics simulators (e.g. OpenModelica or Ricardo IGNITE) through the Functional Mock-up Interface (FMI). In this way we can subject the vehicle to a wide range of scenarios, collect data on the resulting interactions, and analyze those interactions using metrics to understand trustworthiness impact. Trustworthiness is defined here as in the NIST Framework for Cyber-Physical Systems, and is comprised of system reliability, resiliency, safety, security, and privacy. The goal of this work is to provide an example of an experimental design strategy using Fractional Factorial Design for statistically assessing the most important safety metrics in ADS-equipped vehicles.

This paper proposes an intelligent functional architecture for an advanced launch site system that is composed of five parts: the intelligent technical area, the intelligent launching region, the intelligent flight and landing area, the intelligent command and control system, and the intelligent analysis assessment system. The five parts consist of the infrastructure, facilities, equipment, hardware and software and thus include the whole mission processes of ground and launch systems from flight articles' entry to launch. The architectural framework is designed for the intelligent elements of the parts. The framework is also defined as the interrelationship and the interface of the elements, including the launch vehicle and flight payloads. Based on the Internet of Things (IoT), the framework is integrated on four levels: the physical layer, the perception layer, the network layer, and the application layer. The physical layer includes the physical objects and actuators of the launch site. The perception layer consists of the sensors and data processing system. The network layer supplies the access gateways and backbone network. The application layer serves application systems through the middleware platform. The core of the intelligent system is the controller of the automatic control system crossing the four layers. This study builds the models of the IoT, cloud platform, middleware, integrated access gateway, and automatic control system for actual ground and launch systems. A formal approach describes and defines the architecture, models and autonomous control flows in the paper. The defined models describe the physical objects, intelligent elements, interface relations, status transformation functions, etc. The test operation and launch processes are connected with the intelligent system model. This study has been applied to an individual mission project and achieved good results. The architecture and the models of this study regulate the relationship between the elements of the intelligent system. The study lays a foundation for the architectural construction, the simulation and the verification of the intelligent systems at the launch site.

Cyber-physical systems are characterized by strong interactions between their physical and computation parts. The increasing complexity of such systems, now used in numerous application domains (e.g., aeronautics, healthcare), in conjunction with hard to predict surrounding environments or the use of non-traditional middleware and with the presence of non-deterministic or non-explainable software outputs, tend to make traditional Verification and Validation (V&V) techniques ineffective. This paper presents the H2020 ADVANCE project, which aims precisely at addressing the Verification and Validation challenges that the next-generation of cyber-physical systems bring, by exploring techniques, methods and tools for achieving the technical objective of improving the overall efficiency and effectiveness of the V&V process. From a strategic perspective, the goal of the project is to create an international network of expertise on the topic of V&V of cyber-physical systems.

In this paper, an intruder detection system has been built with an implementation of convolutional neural network (CNN) using raspberry pi, Microsoft's Azure and Twilio cloud systems. The CNN algorithm which is stored in the cloud is implemented to basically classify input data as either intruder or user. By using the raspberry pi as the middleware and raspberry pi camera for image acquisition, efficient execution of the learning and classification operations are performed using higher resources that cloud computing offers. The cloud system is also programmed to alert designated users via multimedia messaging services (MMS) when intruders or users are detected. Furthermore, our work has demonstrated that, though convolutional neural network could impose high computing demands on a processor, the input data could be obtained with low-cost modules and middleware which are of low processing power while subjecting the actual learning algorithm execution to the cloud system.

Edge and Fog computing paradigms are used to process big data generated by the increasing number of IoT devices. These paradigms have enabled cities to become smarter in various aspects via real-time data-driven applications. While these have addressed some flaws of cloud computing some challenges remain particularly in terms of privacy and security. We create a testbed based on a distributed processing platform called the Information flow of Things (IFoT) middleware. We briefly describe a decentralized traffic speed query and routing service implemented on this framework testbed. We configure the testbed to test countermeasure systems that aim to address the security challenges faced by prior paradigms. Using this testbed, we investigate a novel decentralized anomaly detection approach for time-sensitive distributed smart transportation systems.

The benefits of data distribution to multiple storage platforms with different characteristics have been widely acknowledged. Such systems are more tolerant to outages and bottlenecks and allow for more flexible policies regarding cost reduction, security and workload diversity. To leverage platforms simultaneously additional orchestration steps are needed. Existing approaches either implement such steps in the application's source code, resulting to minimum reusability across applications, or handle them at the infrastructure level. The latter usually involves over-engineering to handle different application behaviors and binds the system to a specific infrastructure. In this paper we present a middle-ware that decouples the I/O path from the application's source code and performs in-transit processing before data lands on the storage platforms. Abstracting the I/O process as a graph of reusable components allows the developers to easily implement complex storage solutions without the burden of writing custom code. Similarly, the administrators can create their own graph that reflects the infrastructure setup and append it to the preceding graph, so that various policies and infrastructure-related changes can be performed transparently to the application. Users can also extend the graph chain to enhance the application's functionality by using plug-ins. Our approach eliminates the need for custom I/O management code and allows for the applications to evolve independently of the storage back-end. To evaluate our system we employed a secure web service scenario that was seamlessly adapted to the changes in its storage back-end.

The EU SoftFIRE project has built an experimentation platform for NFV and SDN experiments, tailored for testing and evaluating 5G network applications and solutions. The platform is a fully orchestrated virtualisation testbed consisting of multiple component testbeds across Europe. Users of the platform can deploy their virtualisation experiments via the platform's Middleware. This paper introduces the SoftFIRE testbed and its Middleware, and presents a set of KPI results for evaluation of experiment deployment performance.

This work presents ChainFS, a middleware system that secures cloud storage services using a minimally trusted Blockchain. ChainFS hardens the cloud-storage security against forking attacks. The ChainFS middleware exposes a file-system interface to end users. Internally, ChainFS stores data files in the cloud and exports minimal and necessary functionalities to the Blockchain for key distribution and file operation logging. We implement the ChainFS system on Ethereum and S3FS and closely integrate it with FUSE clients and Amazon S3 cloud storage. We measure the system performance and demonstrate low overhead.

In current, RFID (Radio Frequency Identification) Middleware is widely used in nearly all RFID applications, and provides service for raw data capturing, security data reading/writing as well as sensors controlling. However, as the existing Middlewares were organized with rigorous data comparison and data encryption, it is seriously affecting the usefulness and execution efficiency. Thus, in order to improve the utilization rate of effective data, increase the reading/writing speed as well as preserving the security of RFID, this paper proposed a PSO (Particle swarm optimization) clustering scheme to accelerate the procedure of data operation. Then with the help of PSO cluster, the RFID Middleware can provide better service for both data security and data availability. At last, a comparative experiment is proposed, which is used to further verify the advantage of our proposed scheme.

Security and protection are among the most squeezing worries that have developed with the Internet. As systems extended and turned out to be more open, security hones moved to guarantee insurance of the consistently developing Internet, its clients, and information. Today, the Internet of Things (IoT) is rising as another sort of system that associates everything to everybody, all over. Subsequently, the edge of resistance for security and protection moves toward becoming smaller on the grounds that a break may prompt vast scale irreversible harm. One element that eases the security concerns is validation. While diverse confirmation plans are utilized as a part of vertical system storehouses, a typical personality and validation plot is expected to address the heterogeneity in IoT and to coordinate the distinctive conventions exhibit in IoT. In this paper, a light weight secure framework is proposed. The proposed framework is analyzed for performance with security mechanism and found to be better over critical parameters.

In the present generation internet plays a major role. The data being sent by the user is created by the things like pc, mobiles, sensors etc. and these data are sent to the cloud system. When a data from the IOT devices are sent to the cloud, there is a question of privacy and security. To provide security for the data well-known security algorithms are used in fog layer and are successful in transferring the data without any damage. Here different techniques used for providing security for IOT data are discussed.

With the prevalence of Internet of Things (IoT) devices and systems, touching almost every single aspect of our modern life, one core factor that will determine whether this technology will succeed, and gain people trust, or fail is security. This technology aimed to facilitate and improve the quality of our life; however, it is hysterical and fast growth makes it an attractive and prime target for a whole variety of hackers posing a significant risk to our technology and IT infrastructures at both enterprise and individual levels. This paper discusses and identifies some critical aspects from software security perspective that need to be addressed and considered when designing IoT applications. This paper mainly concerned with potential security issues of the applications running on IoT devices including insecure interfaces, insecure software, constrained application protocol and middleware security. This effort is part of a funded research project that investigates internet of things (IoT) security and privacy issues related to architecture, connectivity and data collection.