Show filters Hide filters

Refine your search

Publication Year
Image Motifs found in the video
1-2 out of 2 results
Change view
  • Sort by:
44:42 DEF CON English 2016

Weaponizing Data Science for Social Engineering: Automated E2E Spear Phishing on Twitter

Historically, machine learning for information security has prioritized defense: think intrusion detection systems, malware classification and bonnet traffic identification. Offense can benefit from data just as well. Social networks, especially Twitter with its access to extensive personal data, bot-friendly API, colloquial syntax and prevalence of shortened links, are the perfect venues for spreading machine-generated malicious content. We present a recurrent neural network that learns to tweet phishing posts targeting specific users. The model is trained using spear phishing pen-testing data, and in order to make a click-through more likely, it is dynamically seeded with topics extracted from timeline posts of both the target and the users they retweet or follow. We augment the model with clustering to identify high value targets based on their level of social engagement such as their number of followers and retweets, and measure success using click-rates of IP-tracked links. Taken together, these techniques enable the world’s first automated end-to-end spear phishing campaign generator for Twitter. Bios: John Seymour is a Data Scientist at ZeroFOX, Inc. by day, and Ph.D. student at University of Maryland, Baltimore County by night. He researches the intersection of machine learning and InfoSec in both roles. He’s mostly interested in avoiding and helping others avoid some of the major pitfalls in machine learning, especially in dataset preparation (seriously, do people still use malware datasets from 1998?) He has spoken at both DEF CON and BSides, and aims to add BlackHat USA and SecTor to the list in the near future. Philip Tully is a Senior Data Scientist at ZeroFOX, a social media security company based in Baltimore. He employs natural language processing and computer vision techniques in order to develop predictive models for combating threats emanating from social media. His pivot into the realm of infosec is recent, but his experience in machine learning and artificial neural networks is not. Rather than learning patterns within text and image data, his previous work focused on learning patterns of spikes in large-scale recurrently connected neural circuit models. He is an all-but-defended computer science PhD student, in the final stages of completing a joint degree at the Royal Institute of Technology (KTH) and the University of Edinburgh.
  • Published: 2016
  • Publisher: DEF CON
  • Language: English
33:13 netzpolitik.org English 2016

Artificial Intelligence is Hard to See: Social & ethical impacts of AI

ARTIFICIAL INTELLIGENCE IS HARD TO SEE: ON THE SOCIAL & ETHICAL IMPACTS OF AI This will be an on-stage conversation with academic and author Kate Crawford being interviewed by artists and researcher Trevor Paglen about the recent turn to AI in our wider social systems – from healthcare to criminal justice – and what the implications might be in terms of power, ethics and accountability.
  • Published: 2016
  • Publisher: netzpolitik.org
  • Language: English
out of 1 pages
Loading...
Feedback

Timings

   55 ms - page object
   28 ms - search
    3 ms - highlighting
    2 ms - highlighting/40477
    3 ms - highlighting/36231

Version

AV-Portal 3.8.0 (dec2fe8b0ce2e718d55d6f23ab68f0b2424a1f3f)