The Experts below are selected from a list of 342 Experts worldwide ranked by ideXlab platform

Michael D Coovert - One of the best experts on this subject based on the ideXlab platform.

Michael Bossetta - One of the best experts on this subject based on the ideXlab platform.

  • a simulated cyberattack on twitter assessing partisan vulnerability to Spear Phishing and disinformation ahead of the 2018 u s midterm elections
    First Monday, 2018
    Co-Authors: Michael Bossetta
    Abstract:

    State-sponsored “bad actors” increasingly weaponize social media platforms to launch cyberattacks and disinformation campaigns during elections. Social media companies, due to their rapid growth and scale, struggle to prevent the weaponization of their platforms. This study conducts an automated Spear Phishing and disinformation campaign on Twitter ahead of the 2018 United States midterm elections. A fake news bot account — the @DCNewsReport — was created and programmed to automatically send customized tweets with a “breaking news” link to 138 Twitter users, before being restricted by Twitter. Overall, one in five users clicked the link, which could have potentially led to the downloading of ransomware or the theft of private information. However, the link in this experiment was non-malicious and redirected users to a Google Forms survey. In predicting users’ likelihood to click the link on Twitter, no statistically significant differences were observed between right-wing and left-wing partisans, or between Web users and mobile users. The findings signal that politically expressive Americans on Twitter, regardless of their party preferences or the devices they use to access the platform, are at risk of being Spear phished on social media.

  • a simulated cyberattack on twitter assessing partisan vulnerability to Spear Phishing and disinformation ahead of the 2018 u s midterm elections
    arXiv: Social and Information Networks, 2018
    Co-Authors: Michael Bossetta
    Abstract:

    State-sponsored "bad actors" increasingly weaponize social media platforms to launch cyberattacks and disinformation campaigns during elections. Social media companies, due to their rapid growth and scale, struggle to prevent the weaponization of their platforms. This study conducts an automated Spear Phishing and disinformation campaign on Twitter ahead of the 2018 United States Midterm Elections. A fake news bot account - the @DCNewsReport - was created and programmed to automatically send customized tweets with a "breaking news" link to 138 Twitter users, before being restricted by Twitter. Overall, one in five users clicked the link, which could have potentially led to the downloading of ransomware or the theft of private information. However, the link in this experiment was non-malicious and redirected users to a Google Forms survey. In predicting users' likelihood to click the link on Twitter, no statistically significant differences were observed between right-wing and left-wing partisans, or between Web users and mobile users. The findings signal that politically expressive Americans on Twitter, regardless of their party preferences or the devices they use to access the platform, are at risk of being Spear Phishing on social media.

Jaclyn Martin - One of the best experts on this subject based on the ideXlab platform.

Liling Xin - One of the best experts on this subject based on the ideXlab platform.

  • Spear Phishing emails detection based on machine learning
    Computer Supported Cooperative Work in Design, 2021
    Co-Authors: Xiong Ding, Baoxu Liu, Zhengwei Jiang, Qiuyun Wang, Liling Xin
    Abstract:

    Spear Phishing emails target to specific individual or organization, they are more elaborated, targeted, and harmful than Phishing emails. The attackers usually harvest information about the recipient in any available ways, then create a carefully camouflaged email and lure the recipient to perform dangerous actions. In this paper we present a new effective approach to detect Spear Phishing emails based on machine learning. Firstly we extracted 21 Stylometric features from email, 3 forwarding features from Email Forwarding Relationship Graph Database(EFRGD), and 3 reputation features from two third-party threat intelligence platforms, Virus Total(VT) and Phish Tank(PT). Then we made an improvement on Synthetic Minority Oversampling Technique(SMOTE) algorithm named KM-SMOTE to reduce the impact of unbalanced data. Finally we applied 4 machine learning algorithms to distinguish Spear Phishing emails from non-Spear Phishing emails. Our dataset consists of 417 Spear Phishing emails and 13916 non-Spear Phishing emails. We were able to achieve a maximum recall of 95.56%, precision of 98.85% and 97.16% of F1-score with the help of forwarding features, reputation features and KM-SMOTE algorithm.

Chad Dube - One of the best experts on this subject based on the ideXlab platform.