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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The predictability of program execution provides attackers a rich source ofknowledge who can exploit it to spy or remotely control the program. Movingtarget defense addresses this issue by constantly switching between manydiverse variants of a program, which reduces the certainty that an attacker canhave about the program execution. The effectiveness of this approach relies onthe availability of a large number of software variants that exhibit differentexecutions. However, current approaches rely on the natural diversity providedby off-the-shelf components, which is very limited. In this paper, we explorethe automatic synthesis of large sets of program variants, called sosies.Sosies provide the same expected functionality as the original program, whileexhibiting different executions. They are said to be computationally diverse.This work addresses two objectives: comparing different transformations forincreasing the likelihood of sosie synthesis (densifying the search space forsosies); demonstrating computation diversity in synthesized sosies. Wesynthesized 30184 sosies in total, for 9 large, real-world, open sourceapplications. For all these programs we identified one type of program analysisthat systematically increases the density of sosies; we measured computationdiversity for sosies of 3 programs and found diversity in method calls or datain more than 40% of sosies. This is a step towards controlled massiveunpredictability of software.

Tailored Source Code Transformations to Synthesize Computationally Diverse Program Variants