Biblio

With the latest development of hydroelectric power generation system, the industrial control network system of hydroelectric power generation has undergone the transformation from the dedicated network, using proprietary protocols to an increasingly open network, adopting standard protocols, and increasing integration with hydroelectric power generation system. It generally believed that with the improvement of the smart grid, the future hydroelectric power generation system will rely more on the powerful network system. The general application of standardized communication protocol and intelligent electronic equipment in industrial control network provides a technical guarantee for realizing the intellectualization of hydroelectric power generation system but also brings about the network security problems that cannot be ignored. In order to solve the vulnerability of the system, we analyze and quantitatively evaluate the industrial control network of hydropower generation as a whole, and propose a set of attack and defense strategies. The method of vulnerability assessment with high diversity score proposed by us avoids the indifference of different vulnerability score to the greatest extent. At the same time, we propose an optimal attack and defense decision algorithm, which generates the optimal attack and defense strategy. The work of this paper can distinguish the actual hazards of vulnerable points more effectively.

In this study, a systematic mapping study was conducted to systematically evaluate publications on Intrusion Detection Systems with Deep Learning. 6088 papers have been examined by using systematic mapping method to evaluate the publications related to this paper, which have been used increasingly in the Intrusion Detection Systems. The goal of our study is to determine which deep learning algorithms were used mostly in the algortihms, which criteria were taken into account for selecting the preferred deep learning algorithm, and the most searched topics of intrusion detection with deep learning algorithm model. Scientific studies published in the last 10 years have been studied in the IEEE Explorer, ACM Digital Library, Science Direct, Scopus and Wiley databases.

Mechanism to-Rube Goldberg invention accord is normal habituated to for apartment phones and Internet of Things. Agree and central knowledge are open to meet an unfailing turning between twosome gadgets. In ignoble fracas, factual methodologies many a time eon wait on a prefabricated solitarily pronunciation database and bear the ill effects of serene age rate. We verifiable GeneWave, a brusque gadget inspection and root assention convention for item cell phones. GeneWave mischievous accomplishes bidirectional ingenious inspection office on the physical reaction meantime between two gadgets. To evade the resolution of interim in compliance, we overshadow overseas time fragility on ware gadgets skim through steep flag location and excess time crossing out. At zigzag goal, we success out the elementary acoustic channel reaction for gadget verification. We combination an extraordinary coding pointing for virtual key assention while guaranteeing security. Consequently, two gadgets heart signal couple choice and safely concur on a symmetric key.

Due to its costly and time-consuming nature and a wide range of passive barrier elements and tools for their breaching, testing the delay time of passive barriers is only possible as an experimental tool to verify expert judgements of said delay times. The article focuses on the possibility of creating and utilizing a new method of acquiring values of delay time for various passive barrier elements using expert judgements which could add to the creation of charts where interactions between the used elements of mechanical barriers and the potential tools for their bypassing would be assigned a temporal value. The article consists of basic description of methods of expert judgements previously applied for making prognoses of socio-economic development and in other societal areas, which are called soft system. In terms of the problem of delay time, this method needed to be modified in such a way that the prospective output would be expressible by a specific quantitative value. To achieve this goal, each stage of the expert judgements was adjusted to the use of suitable scientific methods to select appropriate experts and then to achieve and process the expert data. High emphasis was placed on evaluation of quality and reliability of the expert judgements, which takes into account the specifics of expert selection such as their low numbers, specialization and practical experience.

This work proposes an approach to find and learn informative representations from 2 dimensional gray-level images for facial expression recognition application. The learned features are obtained from a designed convolutional neural network (CNN). The developed CNN enables us to learn features from the images in a highly efficient manner by cascading different layers together. The developed model is computationally efficient since it does not consist of a huge number of layers and at the same time it takes into consideration the overfitting problem. The outcomes from the developed CNN are compared to handcrafted features that span texture and shape features. The experiments conducted on the Bosphours database show that the developed CNN model outperforms the handcrafted features when coupled with a Support Vector Machines (SVM) classifier.

The server is an important for storing data, collected during the diagnostics of Smart Business Center (SBC) as a subsystem of Industrial Internet of Things including sensors, network equipment, components for start and storage of monitoring programs and technical diagnostics. The server is exposed most often to various kind of attacks, in particular, aimed at processor, interface system, random access memory. The goal of the paper is analyzing the methods of the SBC server protection from malicious actions, as well as the development and investigation of the Markov model of the server's functioning in the SBC network, taking into account the impact of DDoS-attacks.

Software-defined wireless sensor cognitive radio network is one of the emerging technologies which is simple, agile, and flexible. The sensor network comprises of a sink node with high processing power. The sensed data is transferred to the sink node in a hop-by-hop basis by sensor nodes. The network is programmable, automated, agile, and flexible. The sensor nodes are equipped with cognitive radios, which sense available spectrum bands and transmit sensed data on available bands, which improves spectrum utilization. Unfortunately, the Software-defined wireless sensor cognitive radio network is prone to security issues. The sinkhole attack is the most common attack which can also be used to launch other attacks. We propose and evaluate the performance of Hop Count-Based Sinkhole Attack detection Algorithm (HCOBASAA) using probability of detection, probability of false negative, and probability of false positive as the performance metrics. On average HCOBASAA managed to yield 100%, 75%, and 70% probability of detection.

Database outsourcing introduces a new paradigm, called Database as a Service (DBaaS). Database Service Providers (DSPs) have the ability to host outsourced databases and provide efficient facilities for their users. However, the data and the execution of database queries are under the control of the DSP, which is not always a trusted authority. Therefore, our problem is to ensure the outsourced database security. To address this problem, we propose a Secure scheme to provide Confidentiality and Integrity of Query results for Cloud Databases (SCIQ-CD). The performance analysis shows that our proposed scheme is secure and efficient for practical deployment.

The rapid rise of cyber-crime activities and the growing number of devices threatened by them place software security issues in the spotlight. As around 90% of all attacks exploit known types of security issues, finding vulnerable components and applying existing mitigation techniques is a viable practical approach for fighting against cyber-crime. In this paper, we investigate how the state-of-the-art machine learning techniques, including a popular deep learning algorithm, perform in predicting functions with possible security vulnerabilities in JavaScript programs. We applied 8 machine learning algorithms to build prediction models using a new dataset constructed for this research from the vulnerability information in public databases of the Node Security Project and the Snyk platform, and code fixing patches from GitHub. We used static source code metrics as predictors and an extensive grid-search algorithm to find the best performing models. We also examined the effect of various re-sampling strategies to handle the imbalanced nature of the dataset. The best performing algorithm was KNN, which created a model for the prediction of vulnerable functions with an F-measure of 0.76 (0.91 precision and 0.66 recall). Moreover, deep learning, tree and forest based classifiers, and SVM were competitive with F-measures over 0.70. Although the F-measures did not vary significantly with the re-sampling strategies, the distribution of precision and recall did change. No re-sampling seemed to produce models preferring high precision, while re-sampling strategies balanced the IR measures.

This article describes a privacy policy framework that can represent and reason about complex privacy policies. By using a Common Data Model together with a formal shareability theory, this framework enables the specification of expressive policies in a concise way without burdening the user with technical details of the underlying formalism. We also build a privacy policy decision engine that implements the framework and that has been deployed as the policy decision point in a novel enterprise privacy prototype system. Our policy decision engine supports two main uses: (1) interfacing with user interfaces for the creation, validation, and management of privacy policies; and (2) interfacing with systems that manage data requests and replies by coordinating privacy policy engine decisions and access to (encrypted) databases using various privacy enhancing technologies.

The confidentiality of data stored in embedded and handheld devices has become an urgent necessity more than ever before. Encryption of sensitive data is a well-known technique to preserve their confidentiality, however it comes with certain costs that can heavily impact the device processing resources. Utilizing multicore processors, which are equipped with current embedded devices, has brought a new era to enhance data confidentiality while maintaining suitable device performance. Encrypting the complete storage area, also known as Full Disk Encryption (FDE) can still be challenging, especially with newly emerging massive storage systems. Alternatively, since the most user sensitive data are residing inside persisting databases, it will be more efficient to focus on securing SQLite databases, through encryption, where SQLite is the most common RDBMS in handheld and embedded systems. This paper addresses the problem of ensuring data protection in embedded and mobile devices while maintaining suitable device performance by mitigating the impact of encryption. We presented here a proposed design for a parallel database encryption system, called SQLite-XTS. The proposed system encrypts data stored in databases transparently on-the-fly without the need for any user intervention. To maintain a proper device performance, the system takes advantage of the commodity multicore processors available with most embedded and mobile devices.

A significant amount of attempt has been lately committed for the progress of Database Management Systems (DBMS) that ensures high assertion and high security. Common security measures for database like access control measures, validation, encryption technologies, etc are not sufficient enough to secure the data from all the threats. By using an anomaly detection system, we are able to enhance the security feature of the Database management system. We are taking an assumption that the database access control is role based. In this paper, a mechanism is proposed for finding the anomaly in database by using machine learning technique such as classification. The importance of providing anomaly detection technique to a Role-Based Access Control database is that it will help for the protection against the insider attacks. The experimentation results shows that the system is able to detect intrusion effectively with high accuracy and high F1-score.

Feature of modern scientific information systems is their integration with computing applications, providing distributed computer simulation and intellectual processing of Big Data using high-efficiency computing. Often these software systems include legacy applications in different programming languages, with non-standardized interfaces. To solve the problem of applications integration, containerization systems are using that allow to configure environment in the shortest time to deploy software system. However, there are no such systems for computer simulation systems with large number of nodes. The article considers the actual task of combining containers into a cluster, integrating legacy applications to manage the distributed software system MD-SLAG-MELT v.14, which supports high-performance computing and visualization of the computer experiments results. Testing results of the container cluster including automatic load sharing module for MD-SLAG-MELT system v.14. are given.

The RFID based communication between objects within the framework of IoT is potentially very efficient in terms of power requirements and system complexity. The new design incorporating the emerging chipless RFID tags has the potential to make the system more efficient and simple. However, these systems are prone to privacy and security risks and these challenges associated with such systems have not been addressed appropriately in the broader IoT framework. In this context, a lightweight collision free algorithm based on n-bit pseudo random number generator, X-OR hash function, and rotations for chipless RFID system is presented. The algorithm has been implemented on an 8-bit open-loop resonator based chipless RFID tag based system and is validated using BASYS 2 FPGA board based platform. The proposed scheme has been shown to possess security against various attacks such as Denial of Service (DoS), tag/reader anonymity, and tag impersonation.

In the context of emerging applications such as IoT, an RFID framework that can dynamically incorporate, identify, and seamlessly regulate the RFID tags is considered exciting. Earlier RFID frameworks developed using the older web technologies were limited in their ability to provide complete information about the RFID tags and their respective locations. However, the new and emerging web technologies have transformed this scenario and now framework can be developed to include all the required flexibility and security for seamless applications such as monitoring of RFID tags. This paper revisits and proposes a generic scenario of an RFID framework built using latest web technology and demonstrates its ability to customize using an application for tracking of personal user objects. This has been shown that the framework based on newer web technologies can be indeed robust, uniform, unified, and integrated.

The RFID technology is now widely used and combined with everyday life. RFID Tag is a wireless device used to identify individuals and objects, in fact, it is a combination of the chip and antenna that sends the necessary information to an RFID Reader. On the other hand, an RFID Reader converts received radio waves into digital information and then provides facilities such as sending data to the computer and processing them. Radio frequency identification is a comprehensive processing technology that has led to a revolution in industry and medicine as an alternative to commercial barcodes. RFID Tag is used to tracking commodities and personal assets in the chain stores and even the human body and medical science. However, security and privacy problems have not yet been solved satisfactorily. There are many technical and economic challenges in this direction. In this paper, some of the latest technical research on privacy and security problems has been investigated in radio-frequency identification and security bit method, and it has been shown that in order to achieve this level of individual security, multiple technologies of RFID security development should combine with each other. These solutions should be cheap, efficient, reliable, flexible and long-term.

Data lineage is a fundamental type of information that describes the relationships between input and output data items in a workflow. As such, an immense amount of data-intensive applications with logic over the input-output relationships can be expressed declaratively in lineage terms. Unfortunately, many applications resort to hand-tuned implementations because either lineage systems are not fast enough to meet their requirements or due to no knowledge of the lineage capabilities. Recently, we introduced a set of implementation design principles and associated techniques to optimize lineage-enabled database engines and realized them in our prototype database engine, namely, Smoke. In this demonstration, we showcase lineage as the building block across a variety of data-intensive applications, including tooltips and details on demand; crossfilter; and data profiling. In addition, we show how Smoke outperforms alternative lineage systems to meet or improve on existing hand-tuned implementations of these applications.

False alarm and miss are two general kinds of alarm errors and they can decrease operator's trust in the alarm system. Specifically, there are two different forms of trust in such systems, represented by two kinds of responses to alarms in this research. One is compliance and the other is reliance. Besides false alarm and miss, the two responses are differentially affected by properties of the alarm system, situational factors or operator factors. However, most of the existing studies have qualitatively analyzed the relationship between a single variable and the two responses. In this research, all available experimental studies are identified through database searches using keyword "compliance and reliance" without restriction on year of publication to December 2017. Six relevant studies and fifty-two sets of key data are obtained as the data base of this research. Furthermore, neural network is adopted as a tool to establish the quantitative relationship between multiple factors and the two forms of trust, respectively. The result will be of great significance to further study the influence of human decision making on the overall fault detection rate and the false alarm rate of the human machine system.

Due to expansion of Internet and huge dataset, many organizations started to use cloud. Cloud Computing moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. Due to this cloud faces many threats. In this work, we study the problem of ensuring the integrity of data storage in Cloud Computing. To reduce the computational cost at user side during the integrity verification of their data, the notion of public verifiability has been proposed. Our approach is to create a new entity names Cloud Service Controller (CSC) which will help us to reduce the trust on the Third Party Auditor (TPA). We have strengthened the security model by using AES Encryption with SHA-S12 & tag generation. In this paper we get a brief introduction about the file upload phase, integrity of the file & Proof of Retrievability of the file.

Personal data have been compiled and harnessed by a great number of establishments to execute their legal activities. Establishments are legally bound to maintain the confidentiality and security of personal data. Hence it is a requirement to provide access logs for the personal information. Depending on the needs and capacity, personal data can be opened to the users via platforms such as file system, database and web service. Web service platform is a popular alternative since it is autonomous and can isolate the data source from the user. In this paper, the way to log personal data accessed via web service method has been discussed. As an alternative to classical method in which logs were recorded and saved by client applications, a different mechanism of forming a central audit log with API manager has been investigated. By forging a model policy to exemplify central logging method, its advantages and disadvantages have been explored. It has been concluded in the end that this model could be employed in centrally recording audit logs.

The pervasive use of databases for the storage of critical and sensitive information in many organizations has led to an increase in the rate at which databases are exploited in computer crimes. While there are several techniques and tools available for database forensic analysis, such tools usually assume an apriori database preparation, such as relying on tamper-detection software to already be in place and the use of detailed logging. Further, such tools are built-in and thus can be compromised or corrupted along with the database itself. In practice, investigators need forensic and security audit tools that work on poorlyconfigured systems and make no assumptions about the extent of damage or malicious hacking in a database.In this paper, we present our database forensics methods, which are capable of examining database content from a storage (disk or RAM) image without using any log or file system metadata. We describe how these methods can be used to detect security breaches in an untrusted environment where the security threat arose from a privileged user (or someone who has obtained such privileges). Finally, we argue that a comprehensive and independent audit framework is necessary in order to detect and counteract threats in an environment where the security breach originates from an administrator (either at database or operating system level).

Cloud Computing is an important term of modern technology. The usefulness of Cloud is increasing day by day and simultaneously more and more security problems are arising as well. Two of the major threats of Cloud are improper authentication and multi-tenancy. According to the specialists both pros and cons belong to multi-tenancy. There are security protocols available but it is difficult to claim these protocols are perfect and ensure complete protection. The purpose of this paper is to propose an integrated model to ensure better Cloud security for Authentication and multi-tenancy. Multi-tenancy means sharing of resources and virtualization among clients. Since multi-tenancy allows multiple users to access same resources simultaneously, there is high probability of accessing confidential data without proper privileges. Our model includes Kerberos authentication protocol to enhance authentication security. During our research on Kerberos we have found some flaws in terms of encryption method which have been mentioned in couple of IEEE conference papers. Pondering about this complication we have elected Elliptic Curve Cryptography. On the other hand, to attenuate arose risks due to multi-tenancy we are proposing a Resource Allocation Manager Unit, a Control Database and Resource Allocation Map. This part of the model will perpetuate resource allocation for the users.

Despite the importance of databases in virtually all data driven applications, database forensics is still not the thriving topic it ought to be. Many database management systems (DBMSs) structure the data in the form of trees, most notably B+-Trees. Since the tree structure is depending on the characteristics of the INSERT-order, it can be used in order to generate information on later manipulations, as was shown in a previously published approach. In this work we analyse this approach and investigate, whether it is possible to generalize it to detect DELETE-operations within general INSERT-only trees. We subsequently prove that almost all forms of B+-Trees can be constructed solely by using INSERT-operations, i.e. that this approach cannot be used to prove the existence of DELETE-operations in the past.

It is technically challenging to conduct a security analysis of a dynamic network, due to the lack of methods and techniques to capture different security postures as the network changes. Graphical Security Models (e.g., Attack Graph) are used to assess the security of network systems, but it typically captures a snapshot of a network state to carry out the security analysis. To address this issue, we propose a new Graphical Security Model named Time-independent Hierarchical Attack Representation Model (Ti-HARM) that captures security of multiple network states by taking into account the time duration of each network state and the visibility of network components (e.g., hosts, edges) in each state. By incorporating the changes, we can analyse the security of dynamic networks taking into account all the threats appearing in different network states. Our experimental results show that the Ti-HARM can effectively capture and assess the security of dynamic networks which were not possible using existing graphical security models.

Web application technologies are growing rapidly with continuous innovation and improvements. This paper focuses on the popular Spring Boot [1] java-based framework for building web and enterprise applications and how it provides the flexibility for service-oriented architecture (SOA). One challenge with any Spring-based applications is its level of complexity with configurations. Spring Boot makes it easy to create and deploy stand-alone, production-grade Spring applications with very little Spring configuration. Example, if we consider Spring Model-View-Controller (MVC) framework [2], we need to configure dispatcher servlet, web jars, a view resolver, and component scan among other things. To solve this, Spring Boot provides several Auto Configuration options to setup the application with any needed dependencies. Another challenge is to identify the framework dependencies and associated library versions required to develop a web application. Spring Boot offers simpler dependency management by using a comprehensive, but flexible, framework and the associated libraries in one single dependency, which provides all the Spring related technology that you need for starter projects as compared to CRUD web applications. This framework provides a range of additional features that are common across many projects such as embedded server, security, metrics, health checks, and externalized configuration. Web applications are generally packaged as war and deployed to a web server, but Spring Boot application can be packaged either as war or jar file, which allows to run the application without the need to install and/or configure on the application server. In this paper, we discuss how Atmospheric Radiation Measurement (ARM) Data Center (ADC) at Oak Ridge National Laboratory, is using Spring Boot to create a SOA based REST [4] service API, that bridges the gap between frontend user interfaces and backend database. Using this REST service API, ARM scientists are now able to submit reports via a user form or a command line interface, which captures the same data quality or other important information about ARM data.