BitBlaze: A New Approach to Computer Security via Binary Analysis

By Dawn Song, David Brumley, Heng Yin, Juan Caballero, Ivan Jager, M. Kang, Zhenkai Liang, James Newsome, Pongsin Poosankam, Prateek Saxena. International Conferences on Information Science and System, 2008

799 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/88cb2f118ae343bd48d7b88f8a95b9178c37c812

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Software

In Signal Processing

Citations in the corpus

S2E: a platform for in-vivo multi-path analysis of software systems (2011, methodology): “BitBlaze [36] is the closest dynamic analysis framework to S(2)E.”
A Survey on Malware Detection Using Data Mining Techniques (2017, background): “Zeus has often been used to steal banking-related information by keystroke logging and form grabbing [Wikipedia 2017g].”
Automatic analysis of malware behavior using machine learning (2011, neutral): “Analysis systems such as CWSandbox [69], Anubis [6,7], BitBlaze [59], Norman 2 or ThreatExpert 3 execute malware samples within an instrumented environment and monitor their behavior for analysis and development of defense mechanisms.”
Evaluating Analysis Tools for Android Apps: Status Quo and Robustness Against Obfuscation (2016, methodology): “We did not address the injection of opaque predicates [12], which can be used to amplify the path explosion problem in multipath execution [29] and dynamic symbolic execution [46].”
Firmalice - Automatic Detection of Authentication Bypass Vulnerabilities in Binary Firmware (2015, background): “While a number of previous efforts have been focusing on analyzing binary applications on commodity software and hardware platforms, including general frameworks such as Valgrind [23], BitBlaze [28], and Pin[21], as well as symbolic execution based frameworks like AEG [29] and Mayhem [24], focusing ”
Dynamic Malware Analysis in the Modern Era—A State of the Art Survey (2019, background): “Originally a component within the BitBlaze project [103], it has since become an independent project.”
LEMNA: Explaining Deep Learning based Security Applications (2018, background): “Binary code reverse-engineering, which transfers binary code to assembly code, is a crucial step in (1) examining and detecting malware [51], (2) hardening the security of software [75], and (3) generating security patches [56].”
AVATAR: A Framework to Support Dynamic Security Analysis of Embedded Systems' Firmwares (2014, background): “We believe that Avatar represents a flexible solution to provide a symbolic analysis environment which can greatly speed-up such blackbox analysis cases, aiming at automatically reverse engineer input formats [21], [9] and detect hidden data structures [53].”