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- posterMay 2018
POSTER: I Can't Hear This Because I Am Human: A Novel Design of Audio CAPTCHA System
ASIACCS '18: Proceedings of the 2018 on Asia Conference on Computer and Communications SecurityMay 2018, pp 833–835https://doi.org/10.1145/3196494.3201590A CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) provides the first line of defense to protect websites against bots and automatic crawling. Recently, audio-based CAPTCHA systems are started to use for visually ...
- posterMay 2018
POSTER: DeepCRACk: Using Deep Learning to Automatically CRack Audio CAPTCHAs
ASIACCS '18: Proceedings of the 2018 on Asia Conference on Computer and Communications SecurityMay 2018, pp 797–799https://doi.org/10.1145/3196494.3201581A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a defensive mechanism designed to differentiate humans and computers to prevent unauthorized use of online services by automated attacks. They often consist of a ...
- research-articleMay 2018
Investigating Web Defacement Campaigns at Large
ASIACCS '18: Proceedings of the 2018 on Asia Conference on Computer and Communications SecurityMay 2018, pp 443–456https://doi.org/10.1145/3196494.3196542Website defacement is the practice of altering the web pages of a website after its compromise. The altered pages, calleddeface pages, can negatively affect the reputation and business of the victim site. Previous research has focused primarily on ...
- research-articleMay 2018
You Are Your Photographs: Detecting Multiple Identities of Vendors in the Darknet Marketplaces
ASIACCS '18: Proceedings of the 2018 on Asia Conference on Computer and Communications SecurityMay 2018, pp 431–442https://doi.org/10.1145/3196494.3196529Darknet markets are online services behind Tor where cybercriminals trade illegal goods and stolen datasets. In recent years, security analysts and law enforcement start to investigate the darknet markets to study the cybercriminal networks and predict ...
- research-articleMay 2018
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
- M. Sadegh Riazi,
- Christian Weinert,
- Oleksandr Tkachenko,
- Ebrahim M. Songhori,
- Thomas Schneider,
- Farinaz Koushanfar
ASIACCS '18: Proceedings of the 2018 on Asia Conference on Computer and Communications SecurityMay 2018, pp 707–721https://doi.org/10.1145/3196494.3196522We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE ...
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- research-articleMay 2018
Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning
ASIACCS '18: Proceedings of the 2018 on Asia Conference on Computer and Communications SecurityMay 2018, pp 735–747https://doi.org/10.1145/3196494.3196517Machine learning systems, though being successful in many real-world applications, are known to remain prone to errors and attacks. A major attack, called data pollution, injects maliciously crafted training data samples into the training set, causing ...
- research-articleMay 2018
Detecting Malicious PowerShell Commands using Deep Neural Networks
ASIACCS '18: Proceedings of the 2018 on Asia Conference on Computer and Communications SecurityMay 2018, pp 187–197https://doi.org/10.1145/3196494.3196511Microsoft's PowerShell is a command-line shell and scripting language that is installed by default on Windows machines. Based on Microsoft's .NET framework, it includes an interface that allows programmers to access operating system services. While ...
- short-paperMay 2018
Detection under Privileged Information
- Z. Berkay Celik,
- Patrick McDaniel,
- Rauf Izmailov,
- Nicolas Papernot,
- Ryan Sheatsley,
- Raquel Alvarez,
- Ananthram Swami
ASIACCS '18: Proceedings of the 2018 on Asia Conference on Computer and Communications SecurityMay 2018, pp 199–206https://doi.org/10.1145/3196494.3196502For well over a quarter century, detection systems have been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network event, potential malware sample, suspicious email) is deemed malicious or ...