AVATAR: A Framework to Support Dynamic Security Analysis of Embedded Systems' Firmwares

By Jonas Zaddach, Luca Bruno, Aurélien Francillon, Davide Balzarotti. Network and Distributed System Security Symposium, 2014

385 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ea6396579b7b4ce98575e0d3fc584c6b13fe7491

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

1. Thorsten Holz
2. Heng Yin
3. David Brumley
4. Robert Gawlik
5. Sheng Wen
6. Christian Rossow
7. Davide Balzarotti
8. Aurélien Francillon

In Software

1. Thorsten Holz
2. Robert Gawlik
3. Sheng Wen
4. Heng Yin
5. Rosli Bin Salleh
6. David Brumley
7. Seyit Ahmet Camtepe
8. Nor Badrul Anuar

In Signal Processing

1. David Brumley
2. Thorsten Holz
3. Heng Yin
4. Seyit Ahmet Camtepe
5. Robert Gawlik
6. Sheng Wen
7. Rosli Bin Salleh
8. Christian Rossow

Citations in the corpus

• The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “In addition to smart stimuli, modifying emu-lators for increased transparency (e.g., realistic GPS, realistic phone identifiers) or using emulators with access to real physical hardware (e.g., sensors, accelerometer) to fool VM-aware malware may prove useful and interesting [Zaddach et al. 2014].”
• IoT Security: Ongoing Challenges and Research Opportunities (2014, methodology): “Building these analysis techniques require ad-hoc development for different platforms in the diversified IOT environments [12][13].”
• A Large-Scale Analysis of the Security of Embedded Firmwares (2014, background): “[70] describe Avatar, a dynamic analysis platform for firmware security testing.”
• Scalable Graph-based Bug Search for Firmware Images (2016, background): “To address this critical problem, security researchers have been actively developing techniques to automatically analyze and detect vulnerabilities in IoT products [17,52,61].”
• Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations (2019, background): “Context-aware Permission Models [76], [120] Software Assurance [84], [85], [121]–[123]”
• Towards Automated Dynamic Analysis for Linux-based Embedded Firmware (2016, background): “Using a sample of 74 exploits on the 9,486 firmware images that our system can successfully extract, we discover that 887 firmware images spanning at least 89 distinct products are vulnerable to one or more of the sampled exploit(s).”
• Fuzzing: A Survey for Roadmap (2022, methodology): “The emulators [34, 205] can execute programs, which originally run on IoT firmware, without the corresponding hardware.”
• Cross-architecture bug search in binary executables (2015, background): “[43], this is far from easy on highly-specialized hardware such as embedded devices.”