Biblio
With the continuously development of smart meter-reading technologies for decades, remote information collection of electricity, water, gas and heat meters have been realized. Due to the difference of electrical interfaces and communication protocols among various types of meters, communication modes of meter terminals are not so compatible, it is difficult to realize communication optimization of electricity, water, gas and heat meters information collection services. In addition, with the development of power consumption information acquisition system, the number of acquisition terminals soars greatly and the data of terminal access is highly concurrent. Therefore, the risk of security access is increasing. This paper presents a light-weighted security access scheme of power line communication based on multi-source data acquisition of electricity, water, gas and heat meters, which separates multi-source data acquisition services and achieve services security isolation and channel security isolation. The communication reliability and security of the meter-reading service of "electricity, water, gas and heat" will be improved and the integrated meter service will be realized reliably.
Industrial control systems (ICS) are systems used in critical infrastructures for supervisory control, data acquisition, and industrial automation. ICS systems have complex, component-based architectures with many different hardware, software, and human factors interacting in real time. Despite the importance of security concerns in industrial control systems, there has not been a comprehensive study that examined common security architectural weaknesses in this domain. Therefore, this paper presents the first in-depth analysis of 988 vulnerability advisory reports for Industrial Control Systems developed by 277 vendors. We performed a detailed analysis of the vulnerability reports to measure which components of ICS have been affected the most by known vulnerabilities, which security tactics were affected most often in ICS and what are the common architectural security weaknesses in these systems. Our key findings were: (1) Human-Machine Interfaces, SCADA configurations, and PLCs were the most affected components, (2) 62.86% of vulnerability disclosures in ICS had an architectural root cause, (3) the most common architectural weaknesses were “Improper Input Validation”, followed by “Im-proper Neutralization of Input During Web Page Generation” and “Improper Authentication”, and (4) most tactic-related vulnerabilities were related to the tactics “Validate Inputs”, “Authenticate Actors” and “Authorize Actors”.
The cyber threat landscape is a constantly morphing surface; the need for cyber defenders to develop and create proactive threat intelligence is on the rise, especially on critical infrastructure environments. It is commonly voiced that Supervisory Control and Data Acquisition (SCADA) systems and Industrial Control Systems (ICS) are vulnerable to the same classes of threats as other networked computer systems. However, cyber defense in operational ICS is difficult, often introducing unacceptable risks of disruption to critical physical processes. This is exacerbated by the notion that hardware used in ICS is often expensive, making full-scale mock-up systems for testing and/or cyber defense impractical. New paradigms in cyber security have focused heavily on using deception to not only protect assets, but also gather insight into adversary motives and tools. Much of the work that we see in today's literature is focused on creating deception environments for traditional IT enterprise networks; however, leveraging our prior work in the domain, we explore the opportunities, challenges and feasibility of doing deception in ICS networks.
In this work, a measurement system is developed based on acoustic resonance which can be used for classification of materials. Basically, the inspection methods based on acoustic, utilized for containers screening in the field, identification of defective pills hold high significance in the fields of health, security and protection. However, such techniques are constrained by costly instrumentation, offline analysis and complexities identified with transducer holder physical coupling. So a simple, non-destructive and amazingly cost effective technique in view of acoustic resonance has been formulated here for quick data acquisition and analysis of acoustic signature of liquids for their constituent identification and classification. In this system, there are two ceramic coated piezoelectric transducers attached at both ends of V-shaped glass, one is act as transmitter and another as receiver. The transmitter generates sound with the help of white noise generator. The pick up transducer on another end of the V-shaped glass rod detects the transmitted signal. The recording is being done with arduino interfaced to computer. The FFTs of recorded signals are being analyzed and the resulted resonant frequency observed for water, water+salt and water+sugar are 4.8 KHz, 6.8 KHz and 3.2 KHz respectively. The different resonant frequency in case different sample is being observed which shows that the developed prototype model effectively classifying the materials.
In this paper, we report our work on using machine learning techniques to predict back bending activity based on field data acquired in a local nursing home. The data are recorded by a privacy-aware compliance tracking system (PACTS). The objective of PACTS is to detect back-bending activities and issue real-time alerts to the participant when she bends her back excessively, which we hope could help the participant form good habits of using proper body mechanics when performing lifting/pulling tasks. We show that our algorithms can differentiate nursing staffs baseline and high-level bending activities by using human skeleton data without any expert rules.
Supervisory Control and Data Acquisition(SCADA) communications are often subjected to various sophisticated cyber-attacks mostly because of their static system characteristics, enabling an attacker for easier profiling of the target system(s) and thereby impacting the Critical Infrastructures(CI). In this Paper, a novel approach to mitigate such static vulnerabilities is proposed by implementing a Moving Target Defense (MTD) strategy in a power grid SCADA environment, leveraging the existing communication network with an end-to-end IP-Hopping technique among trusted peers. The main contribution involves the design and implementation of MTD Architecture on Iowa State's PowerCyber testbed for targeted cyber-attacks, without compromising the availability of a SCADA system and studying the delay and throughput characteristics for different hopping rates in a realistic environment. Finally, we study two cases and provide mitigations for potential weaknesses of the proposed mechanism. Also, we propose to incorporate port mutation to further increase attack complexity as part of future work.
Traditionally, utility crews have used faulted circuit indicators (FCIs) to locate faulted line sections. FCIs monitor current and provide a local visual indication of recent fault activity. When a fault occurs, the FCIs operate, triggering a visual indication that is either a mechanical target (flag) or LED. There are also enhanced FCIs with communications capability, providing fault status to the outage management system (OMS) or supervisory control and data acquisition (SCADA) system. Such quickly communicated information results in faster service restoration and reduced outage times. For distribution system protection, protection devices (such as recloser controls) must coordinate with downstream devices (such as fuses or other recloser controls) to clear faults. Furthermore, if there are laterals on a feeder that are protected by a recloser control, it is desirable to communicate to the recloser control which lateral had the fault in order to enhance tripping schemes. Because line sensors are typically placed along distribution feeders, they are capable of sensing fault status and characteristics closer to the fault. If such information can be communicated quickly to upstream protection devices, at protection speeds, the protection devices can use this information to securely speed up distribution protection scheme operation. With recent advances in low-power electronics, wireless communications, and small-footprint sensor transducers, wireless line sensors can now provide fault information to the protection devices with low latencies that support protection speeds. This paper describes the components of a wireless protection sensor (WPS) system, its integration with protection devices, and how the fault information can be transmitted to such devices. Additionally, this paper discusses how the protection devices use this received fault information to securely speed up the operation speed of and improve the selectivity of distribution protection schemes, in add- tion to locating faulted line sections.
In order to ensure the security of electric power supervisory control and data acquisition (SCADA) system, this paper proposes a dynamic awareness security protection model based on security policy, the design idea of which regards safety construction protection as a dynamic analysis process and the security policy should adapt to the network dynamics. According to the current situation of the power SCADA system, the related security technology and the investigation results of system security threat, the paper analyzes the security requirements and puts forward the construction ideas of security protection based on policy protection detection response (P2DR) policy model. The dynamic awareness security protection model proposed in this paper is an effective and useful tool for protecting the security of power-SCADA system.
With the popularization and development of network knowledge, network intruders are increasing, and the attack mode has been updated. Intrusion detection technology is a kind of active defense technology, which can extract the key information from the network system, and quickly judge and protect the internal or external network intrusion. Intrusion detection is a kind of active security technology, which provides real-time protection for internal attacks, external attacks and misuse, and it plays an important role in ensuring network security. However, with the diversification of intrusion technology, the traditional intrusion detection system cannot meet the requirements of the current network security. Therefore, the implementation of intrusion detection needs diversifying. In this context, we apply neural network technology to the network intrusion detection system to solve the problem. In this paper, on the basis of intrusion detection method, we analyze the development history and the present situation of intrusion detection technology, and summarize the intrusion detection system overview and architecture. The neural network intrusion detection is divided into data acquisition, data analysis, pretreatment, intrusion behavior detection and testing.
The precise measurement of temperature is very important to the security and stability of the operation for a superconducting magnet. A slight fluctuation in the operating temperature may cause a superconducting magnet unstable. This paper presents a low-temperature measurement system based on C8051 Micro Controller Unit and Platinum resistance thermometer. In the process of data acquisition, a modified weighted average algorithm is applied to the digital filter program of the micro controller unit. The noise can be effectively reduced and can measure temperature of three different location points simultaneously, and there is no the interference among the three channels. The designed system could measure the temperature from 400 K to 4.0 K with a resolution of 1 mK. This system will be applied in a conduction cooling Nb3Al superconducting magnet. In order to certify the feasibility of the system, tests are performed in a small NbTi non-insulation superconducting magnet model. The results show that the measurement system is reliable and the measured temperature is accurate.
NSTX used MDSplus extensively to record data, relay information and control data acquisition hardware. For NSTX-U the same functionality is expected as well as an expansion into the realm of securely maintaining parameters for machine protection. Specifically, we designed the Digital Coil Protection System (DCPS) to use MDSplus to manage our physical and electrical limit values and relay information about the state of our acquisition system to DCPS. Additionally, test and development systems need to use many of the same resources concurrently without causing interference with other critical systems. Further complications include providing access to critical, protected data without risking changes being made to it by unauthorized users or through unsupported or uncontrolled methods either maliciously or unintentionally. To achieve a level of confidence with an existing software system designed with minimal security controls, a number of changes to how MDSplus is used were designed and implemented. Trees would need to be verified and checked for changes before use. Concurrent creation of trees from vastly different use-cases and varying requirements would need to be supported. This paper will further discuss the impetus for developing such designs and the methods used to implement them.
This paper proposes a fast and robust procedure for sensing and reconstruction of sparse or compressible magnetic resonance images based on the compressive sampling theory. The algorithm starts with incoherent undersampling of the k-space data of the image using a random matrix. The undersampled data is sparsified using Haar transformation. The Haar transform coefficients of the k-space data are then reconstructed using the orthogonal matching Pursuit algorithm. The reconstructed coefficients are inverse transformed into k-space data and then into the image in spatial domain. Finally, a median filter is used to suppress the recovery noise artifacts. Experimental results show that the proposed procedure greatly reduces the image data acquisition time without significantly reducing the image quality. The results also show that the error in the reconstructed image is reduced by median filtering.
The concept of smart cities envisions services that provide distraction-free support for citizens. To realize this vision, the services must adapt to the citizens' situations, behaviors and intents at runtime. This requires services to gather and process the context of their users. Mobile devices provide a promising basis for determining context in an automated manner on a large scale. However, despite the wide availability of versatile programmable mobile platforms such as Android and iOS, there are only few examples of smart city applications. One reason for this is that existing software platforms primarily focus on low-level resource management which requires application developers to repeatedly tackle many challenging tasks. Examples include efficient data acquisition, secure and privacy-preserving data distribution as well as interoperable data integration. In this paper, we describe the GAMBAS middleware which tries to simplify the development of smart city applications. To do this, GAMBAS introduces a Java-based runtime system with an associated software development kit (SDK). To clarify how the runtime system and the SDK can be used for application development, we describe two simple applications that highlight different middleware functions.
The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.
This scientific paper reveals an intelligent system for data acquisition for dam monitoring and diagnose. This system is built around the RS485 communication standard and uses its own communication protocol [2]. The aim of the system is to monitor all signal levels inside the communication bus, respectively to detect the out of action data loggers. The diagnose test extracts the following functional parameters: supply voltage and the absolute value and common mode value for differential signals used in data transmission (denoted with “A” and “B”). Analyzing this acquired information, it's possible to find short-circuits or open-circuits across the communication bus. The measurement and signal processing functions, for flaws, are implemented inside the system's central processing unit. The next testing step is finding the out of action data loggers and is being made by trying to communicate with every data logger inside the network. The lack of any response from a data logger is interpreted as an error and using the code of the data logger's microcontroller, it is possible to find its exact position inside the dam infrastructure. The novelty of this procedure is the fact that it completely automates the diagnose procedure, which, until now, was made visually by checking every data logger.
With the urban traffic planning and management development, it is a highly considerable issue to analyze and estimate the original-destination data in the city. Traditional method to acquire the OD information usually uses household survey, which is inefficient and expensive. In this paper, the new methodology proposed that using mobile phone data to analyze the mechanism of trip generation, trip attraction and the OD information. The mobile phone data acquisition is introduced. A pilot study is implemented on Beijing by using the new method. And, much important traffic information can be extracted from the mobile phone data. We use the K-means clustering algorithm to divide the traffic zone. The attribution of traffic zone is identified using the mobile phone data. Then the OD distribution and the commuting travel are analyzed. At last, an experiment is done to verify availability of the mobile phone data, that analyzing the "Traffic tide phenomenon" in Beijing. The results of the experiments in this paper show a great correspondence to the actual situation. The validated results reveal the mobile phone data has tremendous potential on OD analysis.