English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Traditional botnet attacks leverage large and distributed numbers ofcompromised internet-connected devices to target and overwhelm other deviceswith internet packets. With increasing consumer adoption of high-wattageinternet-facing "smart devices", a new "power botnet" attack emerges, wheresuch devices are used to target and overwhelm power grid devices with unusualload demand. We introduce a variant of this attack, the power-botnetweardown-attack, which does not intend to cause blackouts or short-term acuteinstability, but instead forces expensive mechanical components to activatemore frequently, necessitating costly replacements / repairs. Specifically, wetarget the on-load tap-changer (OLTC) transformer, which uses a mechanicalswitch that responds to change in load demand. In our analysis and simulations,these attacks can halve the lifespan of an OLTC, or in the most extreme cases,reduce it to $2.5\%$ of its original lifespan. Notably, these power botnets arecomposed of devices not connected to the internal SCADA systems used to controlpower grids. This represents a new internet-based cyberattack that targets thepower grid from the outside. To help the power system to mitigate these typesof botnet attacks, we develop attack-localization strategies. We formulate theproblem as a supervised machine learning task to locate the source of powerbotnet attacks. Within a simulated environment, we generate the training andtesting dataset to evaluate several machine learning algorithm basedlocalization methods, including SVM, neural network and decision tree. We showthat decision-tree based classification successfully identifies power botnetattacks and locates compromised devices with at least $94\%$ improvement ofaccuracy over a baseline "most-frequent" classifier.

Botnets Breaking Transformers: Localization of Power Botnet Attacks Against the Distribution Grid