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

As social engineering attacks have become prevalent, people are increasingly convinced to give their important personal or financial information to attackers. Telephone scams are common and less well-studied than phishing emails. We have found that social engineering attacks can be characterized by a set of speech acts which are performed as part of the scam. A speech act is statements or utterances expressed by an individual that not only conveys information but also performs an action. Although attackers adjust their delivery and wording on the phone to match the victim, scams can be grouped into classes that all share common speech acts. Each scam type is identified by a set of speech acts that are collectively referred to as a scam signature. We present a social engineering detection approach called the Anti-Social Engineering Tool ASsET, which detects attacks based on the semantic content of the conversation. Our approach uses word embedding techniques from natural language processing to determine if the meaning of a scam signature is contained in a conversation. In order to evaluate our approach, a dataset of telephone scams has been gathered which are written by volunteers based on examples of real scams from official websites. This dataset is the first telephone-based scam dataset, to the best of our knowledge. Our detection method was able to distinguish scam and non-scam calls with high accuracy.

Detecting Telephone-based Social Engineering Attacks using Scam Signatures