Biblio
The security of Energy Data collection is the basis of achieving reliability and security intelligent of smart grid. The newest security communication of Data collection is Zero Trust communication; The Strategy of Zero Trust communication is that don’t trust any device of outside or inside. Only that device authenticate is successful and software and hardware is more security, the Energy intelligent power system allow the device enroll into network system, otherwise deny these devices. When the device has been communicating with the Energy system, the Zero Trust still need to detect its security and vulnerability, if device have any security issue or vulnerability issue, the Zero Trust deny from network system, it ensures that Energy power system absolute security, which lays a foundation for the security analysis of intelligent power unit.
Modern Energy Management Systems (EMS) are becoming increasingly complex in order to address the urgent issue of global energy consumption. These systems retrieve vital information from various Internet-connected resources in a smart grid to function effectively. However, relying on such resources results in them being susceptible to cyber attacks. Malicious actors can exploit the interconnections between the resources to perform nefarious tasks such as modifying critical firmware, sending bogus sensor data, or stealing sensitive information. To address this issue, we propose a novel framework that integrates PowerWatch, a solution that detects compromised devices in the smart grid with Cyber-secure Power Router (CSPR), a smart energy management system. The goal is to ascertain whether or not such a device has operated maliciously. To achieve this, PowerWatch utilizes a machine learning model that analyzes information from system and library call lists extracted from CSPR in order to detect malicious activity in the EMS. To test the efficacy of our framework, a number of unique attack scenarios were performed on a realistic testbed that comprises functional versions of CSPR and PowerWatch to monitor the electrical environment for suspicious activity. Our performance evaluation investigates the effectiveness of this first-of-its-kind merger and provides insight into the feasibility of developing future cybersecure EMS. The results of our experimental procedures yielded 100% accuracy for each of the attack scenarios. Finally, our implementation demonstrates that the integration of PowerWatch and CSPR is effective and yields minimal overhead to the EMS.
In typical Wireless Sensor Network (WSN) applications, the sensor nodes deployed are constrained both in computational and energy resources. For this reason, simple communication protocols are usually employed along with shortrange multi-hop topologies. In this paper, we challenge this notion and propose a structure that employs more robust (and naturally more complex) forward-error correction schemes in multi-hop extended star topologies. We demonstrate using simulation and real-world data based on popular WSN platforms that this approach can actually reduce the overall energy consumption of the nodes by significant margins (from 40 to 70%) compared to traditional WSN schemes that do not support sophisticated communication mechanisms and it is feasible to implement it economically without relying on expensive hardware.
With the rapid development of bulk power grid under extra-high voltage (EHV) AC/DC hybrid power system and extensive access of distributed energy resources (DER), operation characteristics of power grid have become increasingly complicated. To cope with new severe challenges faced by safe operation of interconnected bulk power grids, an in-depth analysis of bulk power grid security defense system under the background of EHV and new energy resources was implemented from aspects of management and technology in this paper. Supported by big data and cloud computing, bulk power grid security defense system was divided into two parts: one is the prevention and control of operation risks. Power grid risks are eliminated and influence of random faults is reduced through measures such as network planning, power-cut scheme, risk pre-warning, equipment status monitoring, voltage control, frequency control and adjustment of operating mode. The other is the fault recovery control. By updating “three defense lines”, intelligent relay protection is used to deal with the challenges brought by EHV AC/DC hybrid grid and new energy resources. And then security defense system featured by passive defense is promoted to active type power grid security defense system.
Ensuring security in the military applications of IoT is a big challenge. The main reasons for this state of affairs is that the sensor nodes of the network are usually mobile, use wireless links, have a small processing power and have a little energy resources. The paper presents the solution for cryptographic protection of transmission between sensor nodes in the data link layer and for cryptographic protection of data stored in the sensor node resources. For this purpose, the Trusted Platform Module (TPM) was used. The proposed solution makes it possible to build secure and fault tolerant sensor network. The following aspects were presented in the paper: the model of such a network, applied security solutions, analysis of the security in the network and selected investigation results of such a network were presented.
This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource's behaviour under control. An intrusion detection system observes distributed energy resource's behaviour, control actions and the power system impact, and is tested together with an ongoing voltage control attack in a co-simulation set-up. The simulation results obtained with a real photovoltaic rooftop power plant data show that the contextual anomaly detection performs on average 55% better in the control detection and over 56% better in the malicious control detection over the point anomaly detection.