D
Dawn Song
Researcher at University of California, Berkeley
Publications - 504
Citations - 75245
Dawn Song is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 117, co-authored 460 publications receiving 61572 citations. Previous affiliations of Dawn Song include FireEye, Inc. & University of California.
Papers
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Proceedings ArticleDOI
A System for Efficiently Hunting for Cyber Threats in Computer Systems Using Threat Intelligence
Peng Gao,Fei Shao,Xiaoyuan Liu,Xusheng Xiao,Haoyuan Liu,Zheng Qin,Fengyuan Xu,Prateek Mittal,Sanjeev R. Kulkarni,Dawn Song +9 more
TL;DR: In this article, a system that facilitates cyber threat hunting in computer systems using open-source Cyber Threat Intelligence (OSCTI) is presented, called ThreatRaptor, which provides an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text, a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities, and an efficient query execution engine to search the big system audit logging data.
Book ChapterDOI
Code-pointer integrity: Code-Reuse Attacks and Defenses
TL;DR: Carlini et al. as discussed by the authors proposed code-pointer integrity (CPI), a new design point that guarantees the integrity of all code pointers in a program and thereby prevents all control-flow hijack attacks that exploit memory corruption errors.
Patent
Framework for efficient security coverage of mobile software applications that is usable to harden in the field code
TL;DR: In this paper, the authors describe a method that includes receiving an application and creating a representation of the application that describes states and state transitions, and then using the description and the representation to determine actions to be added to the application and locations within the application where the actions are to be performed.
Black Box Anomaly Detection: Is It Utopian?.
TL;DR: A framework for anomaly detection that allows the construction of a black box anomaly detector that can be used for automatically finding anomalies with minimal human intervention is introduced.
Proceedings ArticleDOI
Sanctorum: A lightweight security monitor for secure enclaves
Ilia Lebedev,Kyle Hogan,Jules Drean,David Kohlbrenner,Dayeol Lee,Krste Asanovic,Dawn Song,Srinivas Devadas +7 more
TL;DR: Sanctorum as mentioned in this paper is a small trusted code base (TCB) consisting of a generic enclave-capable system, which is sufficient to implement secure enclaves akin to the primitive offered by Intel's SGX.