16,885 research outputs found
Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems
Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions
Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities
Critical Infrastructures (CIs), such as smart power grids, transport systems,
and financial infrastructures, are more and more vulnerable to cyber threats,
due to the adoption of commodity computing facilities. Despite the use of
several monitoring tools, recent attacks have proven that current defensive
mechanisms for CIs are not effective enough against most advanced threats. In
this paper we explore the idea of a framework leveraging multiple data sources
to improve protection capabilities of CIs. Challenges and opportunities are
discussed along three main research directions: i) use of distinct and
heterogeneous data sources, ii) monitoring with adaptive granularity, and iii)
attack modeling and runtime combination of multiple data analysis techniques.Comment: EDCC-2014, BIG4CIP-201
Predicting Cyber Events by Leveraging Hacker Sentiment
Recent high-profile cyber attacks exemplify why organizations need better
cyber defenses. Cyber threats are hard to accurately predict because attackers
usually try to mask their traces. However, they often discuss exploits and
techniques on hacking forums. The community behavior of the hackers may provide
insights into groups' collective malicious activity. We propose a novel
approach to predict cyber events using sentiment analysis. We test our approach
using cyber attack data from 2 major business organizations. We consider 3
types of events: malicious software installation, malicious destination visits,
and malicious emails that surpassed the target organizations' defenses. We
construct predictive signals by applying sentiment analysis on hacker forum
posts to better understand hacker behavior. We analyze over 400K posts
generated between January 2016 and January 2018 on over 100 hacking forums both
on surface and Dark Web. We find that some forums have significantly more
predictive power than others. Sentiment-based models that leverage specific
forums can outperform state-of-the-art deep learning and time-series models on
forecasting cyber attacks weeks ahead of the events
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