19,565 research outputs found
Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems
The first-ever Ukraine cyberattack on power grid has proven its devastation
by hacking into their critical cyber assets. With administrative privileges
accessing substation networks/local control centers, one intelligent way of
coordinated cyberattacks is to execute a series of disruptive switching
executions on multiple substations using compromised supervisory control and
data acquisition (SCADA) systems. These actions can cause significant impacts
to an interconnected power grid. Unlike the previous power blackouts, such
high-impact initiating events can aggravate operating conditions, initiating
instability that may lead to system-wide cascading failure. A systemic
evaluation of "nightmare" scenarios is highly desirable for asset owners to
manage and prioritize the maintenance and investment in protecting their
cyberinfrastructure. This survey paper is a conceptual expansion of real-time
monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework
that emphasizes on the resulting impacts, both on steady-state and dynamic
aspects of power system stability. Hypothetically, we associate the
combinatorial analyses of steady state on substations/components outages and
dynamics of the sequential switching orders as part of the permutation. The
expanded framework includes (1) critical/noncritical combination verification,
(2) cascade confirmation, and (3) combination re-evaluation. This paper ends
with a discussion of the open issues for metrics and future design pertaining
the impact quantification of cyber-related contingencies
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System
Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity networkāthe smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
- ā¦