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

We present a novel tool BertRLFuzzer, a BERT and Reinforcement Learning (RL)based fuzzer aimed at finding security vulnerabilities for Web applications.BertRLFuzzer works as follows: given a set of seed inputs, the fuzzer performsgrammar-adhering and attack-provoking mutation operations on them to generatecandidate attack vectors. The key insight of BertRLFuzzer is the use of RL witha BERT model as an agent to guide the fuzzer to efficiently learngrammar-adhering and attack-provoking mutation operators. In order to establishthe efficacy of BertRLFuzzer we compare it against a total of 13 black box andwhite box fuzzers over a benchmark of 9 victim websites with over 16K LOC. Weobserved a significant improvement relative to the nearest competing tool interms of time to first attack (54% less), new vulnerabilities found (17 newvulnerabilities), and attack rate (4.4% more attack vectors generated).

BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer