PMU Placement in Electric Transmission Networks for Reliable State Estimation against False Data Injection Attacks

Currently the false data injection (FDI) attack bring direct challenges in synchronized phase measurement unit (PMU) based network state estimation in wide-area measurement system (WAMS), resulting in degraded system reliability and power supply security. This paper assesses the performance of state estimation in electric cyber-physical system (ECPS) paradigm considering the presence of FDI attacks. The adverse impact on network state estimation is evaluated through simulations for a range of FDI attack scenarios using IEEE 14-bus network model. In addition, an algorithmic solution is proposed to address the issue of additional PMU installation and placement with cyber security consideration and evaluated for a set of standard electric transmission networks (IEEE 14-bus, 30-bus and 57-bus network). The numerical result confirms that the FDI attack can significantly degrade the state estimation and the cyber security can be improved by an appropriate placement of a limited number of additional PMUs

Architecture and Experimental Validation of a Low-Latency Phasor Data Concentrator

The paper presents the design principles of a Phasor Data Concentrator (PDC) that implements both the absolute and relative time data pushing logics together with a third one that aims at minimizing the latency introduced by the PDC without increasing the data incompleteness, as suggested in the IEEE Guide C37.244-2013. The performance of the aforementioned logics are assessed and compared in terms of reliability, determinism and reduction of the overall latency in two real Phasor Measurement Unit (PMU) installations adopting different telecom infrastructures. The first one is based on optical fiber links that transmit synchrophasor data measured by 15 PMUs installed in the sub-transmission network of the city of Lausanne, Switzerland. The second one adopts a 4G LTE wireless infrastructure to support the data streaming of 10 PMUs installed in a distribution network supplying the city of Huissen, in the Netherlands. The experimental results show that the proposed logic is characterized by the lowest latency, whereas the absolute time logic better mitigates the synchrophasor data latency variations

Security risks in cyber physical systems—A systematic mapping study

The increased need for constant connectivity and complete automation of existing systems fuels the popularity of Cyber Physical Systems (CPS) worldwide. Increasingly more, these systems are subjected to cyber attacks. In recent years, many major cyber-attack incidents on CPS have been recorded and, in turn, have been raising concerns in their users' minds. Unlike in traditional IT systems, the complex architecture of CPS consisting of embedded systems integrated with the Internet of Things (IoT) requires rather extensive planning, implementation, and monitoring of security requirements. One crucial step to planning, implementing, and monitoring of these requirements in CPS is the integration of the risk management process in the CPS development life cycle. Existing studies do not clearly portray the extent of damage that the unattended security issues in CPS can cause or have caused, in the incidents recorded. An overview of the possible risk management techniques that could be integrated into the development and maintenance of CPS contributing to improving its security level in its actual environment is missing. In this paper, we are set out to highlight the security requirements and issues specific to CPS that are discussed in scientific literature and to identify the state-of-the-art risk management processes adopted to identify, monitor, and control those security issues in CPS. For that, we conducted a systematic mapping study on the data collected from 312 papers published between 2000 and 2020, focused on the security requirements, challenges, and the risk management processes of CPS. Our work aims to form an overview of the security requirements and risks in CPS today and of those published contributions that have been made until now, towards improving the reliability of CPS. The results of this mapping study reveal (i) integrity authentication and confidentiality as the most targeted security attributes in CPS, (ii) model-based techniques as the most used risk identification and assessment and management techniques in CPS, (iii) cyber-security as the most common security risk in CPS, (iv) the notion of “mitigation measures” based on the type of system and the underline internationally recognized standard being the most used risk mitigation technique in CPS, (v) smart grids being the most targeted systems by cyber-attacks and thus being the most explored domain in CPS literature, and (vi) one of the major limitations, according to the selected literature, concerns the use of the fault trees for fault representation, where there is a possibility of runtime system faults not being accounted for. Finally, the mapping study draws implications for practitioners and researchers based on the findings.</p

Design and Analysis of Security Attacks against Critical Smart Grid Infrastructures

Smart grid, the future power grid, is expected to provide better energy efficiency, more customer choices and improved reliability and security. As the smart grid is an integrated system that consists of multiple subsystems, understanding it as a whole system is required to fully understand the security risks it faces. In this paper, a sophisticated cyber-physical system (CPS) unique malware attack against the smart grid is proposed. The paper first outlines the architecture of the smart grid in general. Then we present the characteristics of recent malware attacks targeting the CPS such as Stuxnet and Shamoon. These lead to the design of our proposed attack that incorporates the key features from the smart grid architecture and the recent real attacks. One key aspect of the proposed attack is that it manipulates various physical field devices as well as cyber systems to illustrate how a blackout is possible even under the security-improved smart grid environment. Then, we explain the application of defensive techniques in the context of the suggested attack. Lastly, prototype implementation showing the effectiveness of the attack and the defensive measures is described.10 page(s

Optimum parameter machine learning classification and prediction of Internet of Things (IoT) malwares using static malware analysis techniques

Application of machine learning in the field of malware analysis is not a new concept, there have been lots of researches done on the classification of malware in android and windows environments. However, when it comes to malware analysis in the internet of things (IoT), it still requires work to be done. IoT was not designed to keeping security/privacy under consideration. Therefore, this area is full of research challenges. This study seeks to evaluate important machine learning classifiers like Support Vector Machines, Neural Network, Random Forest, Decision Trees, Naive Bayes, Bayesian Network, etc. and proposes a framework to utilize static feature extraction and selection processes highlight issues like over-fitting and generalization of classifiers to get an optimized algorithm with better performance. For background study, we used systematic literature review to find out research gaps in IoT, presented malware as a big challenge for IoT and the reasons for applying malware analysis targeting IoT devices and finally perform classification on malware dataset. The classification process used was applied on three different datasets containing file header, program header and section headers as features. Preliminary results show the accuracy of over 90% on file header, program header, and section headers. The scope of this document just discusses these results as initial results and still require some issues to be addressed which may effect on the performance measures