Biblio
The hybrid microgrid is attracting great attention in recent years as it combines the main advantages of the alternating current (AC) and direct current (DC) microgrids. It is one of the best candidates to support a net-zero energy community. Thus, this paper investigates and compares different hybrid AC/DC microgrid configurations that are suitable for a net-zero energy community. Four different configurations are compared with each other in terms of their impacts on the overall system reliability, expandability, load shedding requirements, power sharing issues, net-zero energy capability, number of the required interface converters, and the requirement of costly medium-voltage components. The results of the investigations indicate that the best results are achieved when each building is enabled to supply its critical loads using an independent AC microgrid that is interfaced to the DC microgrid through a dedicated interface converter.
A dynamic DNA for key-based Cryptography that encrypt and decrypt plain text characters, text file, image file and audio file using DNA sequences. Cryptography is always taken as the secure way while transforming the confidential information over the network such as LAN, Internet. But over the time, the traditional cryptographic approaches are been replaced with more effective cryptographic systems such as Quantum Cryptography, Biometric Cryptography, Geographical Cryptography and DNA Cryptography. This approach accepts the DNA sequences as the input to generate the key that going to provide two stages of data security.
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.
Distributed denial of service (DDoS) attacks is a serious cyberattack that exhausts target machine's processing capacity by sending a huge number of packets from hijacked machines. To minimize resource consumption caused by DDoS attacks, filtering attack packets at source machines is the best approach. Although many studies have explored the detection of DDoS attacks, few studies have proposed DDoS attack prevention schemes that work at source machines. We propose a reliable, lightweight, transparent, and flexible DDoS attack prevention scheme that works at source machines. In this scheme, we employ a hypervisor with a packet filtering mechanism on each managed machine to allow the administrator to easily and reliably suppress packet transmissions. To make the proposed scheme lightweight and transparent, we exploit a thin hypervisor that allows pass-through access to hardware (except for network devices) from the operating system, thereby reducing virtualization overhead and avoiding compromising user experience. To make the proposed scheme flexible, we exploit a configurable packet filtering mechanism with a guaranteed safe code execution mechanism that allows the administrator to provide a filtering policy as executable code. In this study, we implemented the proposed scheme using BitVisor and the Berkeley Packet Filter. Experimental results show that the proposed scheme can suppress arbitrary packet transmissions with negligible latency and throughput overhead compared to a bare metal system without filtering mechanisms.
In Software Defined Networking (SDN) control plane of forwarding devices is concentrated in the SDN controller, which assumes the role of a network operating system. Big share of today's commercial SDN controllers are based on OpenDaylight, an open source SDN controller platform, whose bug repository is publicly available. In this article we provide a first insight into 8k+ bugs reported in the period over five years between March 2013 and September 2018. We first present the functional components in OpenDaylight architecture, localize the most vulnerable modules and measure their contribution to the total bug content. We provide high fidelity models that can accurately reproduce the stochastic behaviour of bug manifestation and bug removal rates, and discuss how these can be used to optimize the planning of the test effort, and to improve the software release management. Finally, we study the correlation between the code internals, derived from the Git version control system, and software defect metrics, derived from Jira issue tracker. To the best of our knowledge, this is the first study to provide a comprehensive analysis of bug characteristics in a production grade SDN controller.
Bitcoin provides freshness properties by forming a blockchain where each block is associated with its timestamp and the previous block. Due to these properties, the Bitcoin protocol is being used as a decentralized, trusted, and secure timestamping service. Although Bitcoin participants which create new blocks cannot modify their order, they can manipulate timestamps almost undetected. This undermines the Bitcoin protocol as a reliable timestamping service. In particular, a newcomer that synchronizes the entire blockchain has a little guarantee about timestamps of all blocks. In this paper, we present a simple yet powerful mechanism that increases the reliability of Bitcoin timestamps. Our protocol can provide evidence that a block was created within a certain time range. The protocol is efficient, backward compatible, and surprisingly, currently deployed SSL/TLS servers can act as reference time sources. The protocol has many applications and can be used for detecting various attacks against the Bitcoin protocol.
A covert channel is a communication channel that is subjugated for illegal flow of information in a way that violates system security policies. It is a dangerous, invisible, undetectable, and developed security attack. Recently, Packet length covert channel has motivated many researchers as it is a one of the most undetectable network covert channels. Packet length covert channel generates a covert traffic that is very similar to normal terrific which complicates the detection of such type of covert channels. This motivates us to introduce a machine learning based detection scheme. Recently, a machine learning approach has proved its capability in many different fields especially in security field as it usually brings up a reliable and realistic results. Based in our developed content and frequency-based features, the developed detection scheme has been fully trained and tested. Our detection scheme has gained an excellent degree of detection accuracy which reaches 98% (zero false negative rate and 0.02 false positive rate).
Internet of Things (IoT) is experiencing exponential scalability. This scalability introduces new challenges regarding management of IoT networks. The question that emerges is how we can trust the constrained infrastructure that shortly is expected to be formed by millions of 'things.' The answer is not to trust. This research introduces Amatista, a blockchain-based middleware for management in IoT. Amatista presents a novel zero-trust hierarchical mining process that allows validating the infrastructure and transactions at different levels of trust. This research evaluates Amatista on Edison Arduino Boards.
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.
Security is one of the most important properties of electric power system (EPS). We consider the state estimation (SE) tool as a barrier to the corruption of data on current operating conditions of the EPS. An algorithm for a two-level SE on the basis of SCADA and WAMS measurements is effective in terms of detection of malicious attacks on energy system. The article suggests a methodology to identify cyberattacks on SCADA and WAMS.
The Machine Type Communication Devices (MTCDs) are usually based on Internet Protocol (IP), which can cause billions of connected objects to be part of the Internet. The enormous amount of data coming from these devices are quite heterogeneous in nature, which can lead to security issues, such as injection attacks, ballot stuffing, and bad mouthing. Consequently, this work considers machine learning trust evaluation as an effective and accurate option for solving the issues associate with security threats. In this paper, a comparative analysis is carried out with five different machine learning approaches: Naive Bayes (NB), Decision Tree (DT), Linear and Radial Support Vector Machine (SVM), KNearest Neighbor (KNN), and Random Forest (RF). As a critical element of the research, the recommendations consider different Machine-to-Machine (M2M) communication nodes with regard to their ability to identify malicious and honest information. To validate the performances of these models, two trust computation measures were used: Receiver Operating Characteristics (ROCs), Precision and Recall. The malicious data was formulated in Matlab. A scenario was created where 50% of the information were modified to be malicious. The malicious nodes were varied in the ranges of 10%, 20%, 30%, 40%, and the results were carefully analyzed.
Physical Unclonable Functions (PUFs) have been designed for many security applications such as identification, authentication of devices and key generation, especially for lightweight electronics. Traditional approaches to enhancing security, such as hash functions, may be expensive and resource dependent. However, modelling attacks using machine learning (ML) show the vulnerability of most PUFs. In this paper, a combination of a 32-bit current mirror and 16-bit arbiter PUFs in 65nm CMOS technology is proposed to improve resilience against modelling attacks. Both PUFs are vulnerable to machine learning attacks and we reduce the output prediction rate from 99.2% and 98.8% individually, to 60%.
This computer era leads human to interact with computers and networks but there is no such solution to get rid of security problems. Securities threats misleads internet, we are sometimes losing our hope and reliability with many server based access. Even though many more crypto algorithms are coming for integrity and authentic data in computer access still there is a non reliable threat penetrates inconsistent vulnerabilities in networks. These vulnerable sites are taking control over the user's computer and doing harmful actions without user's privileges. Though Firewalls and protocols may support our browsers via setting certain rules, still our system couldn't support for data reliability and confidentiality. Since these problems are based on network access, lets we consider TCP/IP parameters as a dataset for analysis. By doing preprocess of TCP/IP packets we can build sovereign model on data set and clump cluster. Further the data set gets classified into regular traffic pattern and anonymous pattern using KNN classification algorithm. Based on obtained pattern for normal and threats data sets, security devices and system will set rules and guidelines to learn by it to take needed stroke. This paper analysis the computer to learn security actions from the given data sets which already exist in the previous happens.
The safety of industrial control systems (ICS) depends not only on comprehensive solutions for protecting information, but also on the timing and closure of vulnerabilities in the software of the ICS. The investigation of security incidents in the ICS is often greatly complicated by the fact that malicious software functions only within the computer's volatile memory. Obtaining the contents of the volatile memory of an attacked computer is difficult to perform with a guaranteed reliability, since the data collection procedure must be based on a reliable code (the operating system or applications running in its environment). The paper proposes a new instrumental method for obtaining the contents of volatile memory, general rules for implementing the means of collecting information stored in memory. Unlike software methods, the proposed method has two advantages: firstly, there is no problem in terms of reading the parts of memory, blocked by the operating system, and secondly, the resulting contents are not compromised by such malicious software. The proposed method is relevant for investigating security incidents of ICS and can be used in continuous monitoring systems for the security of ICS.
In order to meet the demand of electrical energy by consumers, utilities have to maintain the security of the system. This paper presents a design of the Microgrid Central Energy Management System (MCEMS). It will plan operation of the system one-day advance. The MCEMS will adjust itself during operation if a fault occurs anywhere in the generation system. The proposed approach uses Dynamic Programming (DP) algorithm solves the Unit Commitment (UC) problem and at the same time enhances the security of power system. A case study is performed with ten subsystems. The DP is used to manage the operation of the subsystems and determines the UC on the situation demands. Faults are applied to the system and the DP corrects the UC problem with appropriate power sources to maintain reliability supply. The MATLAB software has been used to simulate the operation of the system.
Iris recognition is one of the most reliable biometrics for identification purpose in terms of reliability and accuracy. Hence, in this research the integration of cancelable biometrics features for iris recognition using encryption and decryption non-invertible transformation is proposed. Here, the biometric data is protected via the proposed cancelable biometrics method. The experimental results showed that the recognition rate achieved is 99.9% using Bath-A dataset with a maximum decision criterion of 0.97 along with acceptable processing time.
Cyber physical systems are the key innovation driver for many domains such as automotive, avionics, industrial process control, and factory automation. However, their interconnection potentially provides adversaries easy access to sensitive data, code, and configurations. If attackers gain control, material damage or even harm to people must be expected. To counteract data theft, system manipulation and cyber-attacks, security mechanisms must be embedded in the cyber physical system. Adding hardware security in the form of the standardized Trusted Platform Module (TPM) is a promising approach. At the same time, traditional dependability features such as safety, availability, and reliability have to be maintained. To determine the right balance between security and dependability it is essential to understand their interferences. This paper supports developers in identifying the implications of using TPMs on the dependability of their system.We highlight potential consequences of adding TPMs to cyber-physical systems by considering the resulting safety, reliability, and availability. Furthermore, we discuss the potential of enhancing the dependability of TPM services by applying traditional redundancy techniques.