An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

The smart grid is arguably one of the most complex cyber-physical systems (CPS). Complex security challenges have been revealed in both the physical and the cyber parts of the smart grid, and an integrative analysis on the cyber-physical (CP) security is emerging. This paper provides a comprehensive and systematic review of the critical attack threats and defence strategies in the smart grid. We start this survey with an overview of the smart grid security from the CP perspective, and then focuses on prominent CP attack schemes with significant impact on the smart grid operation and corresponding defense solutions. With an in-depth review of the attacks and defences, we then discuss the opportunities and challenges along the smart grid CP security. We hope this paper raises awareness of the CP attack threats and defence strategies in complex CPS-based infrastructures such as the smart grid and inspires research effort toward the development of secure and resilient CP infrastructures.

Cyber-physical attacks and defences in the smart grid: a survey

A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network

Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience

As the deployment of Internet of Things (IoT) is experiencing an exponential growth, it is no surprise that many recent cyber attacks are IoT-enabled : the attacker initially exploits some vulnerable IoT technology as a first step toward compromising a critical system that is connected, in some way, with the IoT. For some sectors, like industry, smart grids, transportation, and medical services, the significance of such attacks is obvious, since IoT technologies are part of critical back-end systems. However, in sectors where IoT is usually at the end-user side, like smart homes, such attacks can be underestimated, since not all possible attack paths are examined. In this paper, we survey IoT-enabled cyber attacks, found in all application domains since 2010. For each sector, we emphasize on the latest, verified IoT-enabled attacks, based on known real-world incidents and published proof-of-concept attacks. We methodologically analyze representative attacks that demonstrate direct, indirect, and subliminal attack paths against critical targets. Our goal is threefold: 1) to assess IoT-enabled cyber attacks in a risk-like approach, in order to demonstrate their current threat landscape; 2) to identify hidden and subliminal IoT-enabled attack paths against critical infrastructures and services; and 3) to examine mitigation strategies for all application domains.

A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastructures and Services

Local line directional pattern for palmprint recognition

Security issues related to power grid networks have attracted the attention of researchers in many fields. Recently, a new network model that combines complex network theories with power flow models was proposed. This model, referred to as the extended model, is suitable for investigating vulnerabilities in power grid networks. In this paper, we study cascading failures of power grids under the extended model. Particularly, we discover that attack strategies that select target nodes (TNs) based on load and degree do not yield the strongest attacks. Instead, we propose a novel metric, called the risk graph, and develop novel attack strategies that are much stronger than the load-based and degree-based attack strategies. The proposed approaches and the comparison approaches are tested on IEEE 57 and 118 bus systems and Polish transmission system. The results demonstrate that the proposed approaches can reveal the power grid vulnerability in terms of causing cascading failures more effectively than the comparison approaches.

https://www.ele.uri.edu/faculty/he/PDFfiles/TPDS_Cascadingfailure.pdf

Revealing cascading failure vulnerability in power grids using risk-graph

Direction information serves as one of the most important features for palmprint recognition In the past decade, many effective direction representation (DR)-based methods have been proposed and achieved promising recognition performance However, due to an incomplete understanding for DR, these methods only extract DR in one direction level and one scale Hence, they did not fully utilize all potentials of DR In addition, most researchers only focused on the DR extraction in spatial coding domain, and rarely considered the methods in frequency domain In this paper, we propose a general framework for DR-based method named complete DR (CDR), which reveals DR by a comprehensive and complete way Different from traditional methods, CDR emphasizes the use of direction information with strategies of multi-scale, multi-direction level, multi-region, as well as feature selection or learning This way, CDR subsumes previous methods as special cases Moreover, thanks to its new insight, CDR can guide the design of new DR-based methods toward better performance Motived this way, we propose a novel palmprint recognition algorithm in frequency domain First, we extract CDR using multi-scale modified finite radon transformation Then, an effective correlation filter, namely, band-limited phase-only correlation, is explored for pattern matching To remove feature redundancy, the sequential forward selection method is used to select a small number of CDR images Finally, the matching scores obtained from different selected features are integrated using score-level-fusion Experiments demonstrate that our method can achieve better recognition accuracy than the other state-of-the-art methods More importantly, it has fast matching speed, making it quite suitable for the large-scale identification applications

Palmprint Recognition Based on Complete Direction Representation

Gait recognition, the process of identifying an individual by his /her walking style, is a relatively new research area. It has been receiving wide attention in the computer vision community. In this paper, a comprehensive survey of video based gait recognition approaches is presented. And the research challenges and future directions of the gait recognition are also discussed.

Survey of gait recognition

The modern society increasingly relies on electrical service, which also brings risks of catastrophic consequences, e.g., large-scale blackouts. In the current literature, researchers reveal the vulnerability of power grids under the assumption that substations/transmission lines are removed or attacked synchronously. In reality, however, it is highly possible that such removals can be conducted sequentially. Motivated by this idea, we discover a new attack scenario, called the sequential attack, which assumes that substations/transmission lines can be removed sequentially, not synchronously. In particular, we find that the sequential attack can discover many combinations of substation whose failures can cause large blackout size. Previously, these combinations are ignored by the synchronous attack. In addition, we propose a new metric, called the sequential attack graph (SAG), and a practical attack strategy based on SAG. In simulations, we adopt three test benchmarks and five comparison schemes. Referring to simulation results and complexity analysis, we find that the proposed scheme has strong performance and low complexity.

Yihai Zhu

Papers

Local line directional pattern for palmprint recognition

Revealing cascading failure vulnerability in power grids using risk-graph

Palmprint Recognition Based on Complete Direction Representation

Survey of gait recognition

Resilience Analysis of Power Grids Under the Sequential Attack