Address Space Layout Randomization Next Generation

[EN] Systems that are built using low-power computationally-weak devices, which force developers to favor performance over security; which jointly with its high connectivity, continuous and autonomous operation makes those devices specially appealing to attackers. ASLR (Address Space Layout Randomization) is one of the most effective mitigation techniques against remote code execution attacks, but when it is implemented in a practical system its effectiveness is jeopardized by multiple constraints: the size of the virtual memory space, the potential fragmentation problems, compatibility limitations, etc. As a result, most ASLR implementations (specially in 32-bits) fail to provide the necessary protection. In this paper we propose a taxonomy of all ASLR elements, which categorizes the entropy in three dimensions: (1) how, (2) when and (3) what; and includes novel forms of entropy. Based on this taxonomy we have created, ASLRA, an advanced statistical analysis tool to assess the effectiveness of any ASLR implementation. Our analysis show that all ASLR implementations suffer from several weaknesses, 32-bit systems provide a poor ASLR, and OS X has a broken ASLR in both 32- and 64-bit systems. This is jeopardizing not only servers and end users devices as smartphones but also the whole IoT ecosystem. To overcome all these issues, we present ASLR-NG, a novel ASLR that provides the maximum possible absolute entropy and removes all correlation attacks making ASLR-NG the best solution for both 32- and 64-bit systems. We implemented ASLR-NG in the Linux kernel 4.15. The comparative evaluation shows that ASLR-NG overcomes PaX, Linux and OS X implementations, providing strong protection to prevent attackers from abusing weak ASLRs.Marco-Gisbert, H.; Ripoll-Ripoll, I. (2019). Address Space Layout Randomization Next Generation. Applied Sciences. 9(14):1-25. https://doi.org/10.3390/app9142928S125914Aga, M. T., & Austin, T. (2019). Smokestack: Thwarting DOP Attacks with Runtime Stack Layout Randomization. 2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). doi:10.1109/cgo.2019.8661202Object Size Checking to Prevent (Some) Buffer Overflows (GCC FORTIFY) http://gcc.gnu.org/ml/gcc-patches/2004-09/msg02055.htmlShahriar, H., & Zulkernine, M. (2012). Mitigating program security vulnerabilities. ACM Computing Surveys, 44(3), 1-46. doi:10.1145/2187671.2187673Carlier, M., Steenhaut, K., & Braeken, A. (2019). Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks. Computers, 8(1), 27. doi:10.3390/computers8010027Choudhary, J., Balasubramanian, P., Varghese, D., Singh, D., & Maskell, D. (2019). Generalized Majority Voter Design Method for N-Modular Redundant Systems Used in Mission- and Safety-Critical Applications. Computers, 8(1), 10. doi:10.3390/computers8010010Shacham, H., Page, M., Pfaff, B., Goh, E.-J., Modadugu, N., & Boneh, D. (2004). On the effectiveness of address-space randomization. Proceedings of the 11th ACM conference on Computer and communications security - CCS ’04. doi:10.1145/1030083.1030124Marco-Gisbert, H., & Ripoll, I. (2013). Preventing Brute Force Attacks Against Stack Canary Protection on Networking Servers. 2013 IEEE 12th International Symposium on Network Computing and Applications. doi:10.1109/nca.2013.12Friginal, J., de Andres, D., Ruiz, J.-C., & Gil, P. (2010). Attack Injection to Support the Evaluation of Ad Hoc Networks. 2010 29th IEEE Symposium on Reliable Distributed Systems. doi:10.1109/srds.2010.11Jun Xu, Kalbarczyk, Z., & Iyer, R. K. (s. f.). Transparent runtime randomization for security. 22nd International Symposium on Reliable Distributed Systems, 2003. Proceedings. doi:10.1109/reldis.2003.1238076Zhan, X., Zheng, T., & Gao, S. (2014). Defending ROP Attacks Using Basic Block Level Randomization. 2014 IEEE Eighth International Conference on Software Security and Reliability-Companion. doi:10.1109/sere-c.2014.28Iyer, V., Kanitkar, A., Dasgupta, P., & Srinivasan, R. (2010). Preventing Overflow Attacks by Memory Randomization. 2010 IEEE 21st International Symposium on Software Reliability Engineering. doi:10.1109/issre.2010.22Van der Veen, V., dutt-Sharma, N., Cavallaro, L., & Bos, H. (2012). Memory Errors: The Past, the Present, and the Future. Lecture Notes in Computer Science, 86-106. doi:10.1007/978-3-642-33338-5_5PaX Address Space Layout Randomization (ASLR) http://pax.grsecurity.net/docs/aslr.txtKernel Address Space Layout Randomization https://lwn.net/Articles/569635Rahman, M. A., & Asyhari, A. T. (2019). The Emergence of Internet of Things (IoT): Connecting Anything, Anywhere. Computers, 8(2), 40. doi:10.3390/computers8020040Bojinov, H., Boneh, D., Cannings, R., & Malchev, I. (2011). Address space randomization for mobile devices. Proceedings of the fourth ACM conference on Wireless network security - WiSec ’11. doi:10.1145/1998412.1998434Hiser, J., Nguyen-Tuong, A., Co, M., Hall, M., & Davidson, J. W. (2012). ILR: Where’d My Gadgets Go? 2012 IEEE Symposium on Security and Privacy. doi:10.1109/sp.2012.39Xu, H., & Chapin, S. J. (2009). Address-space layout randomization using code islands. Journal of Computer Security, 17(3), 331-362. doi:10.3233/jcs-2009-0322Wartell, R., Mohan, V., Hamlen, K. W., & Lin, Z. (2012). Binary stirring. Proceedings of the 2012 ACM conference on Computer and communications security - CCS ’12. doi:10.1145/2382196.2382216Growable Maps Removal https://lwn.net/Articles/294001/Silent Stack-Heap Collision under GNU/Linux https://gcc.gnu.org/ml/gcc-help/2014-07/msg00076.htmlAMD Bulldozer Linux ASLR Weakness: Reducing Entropy by 87.5% http://hmarco.org/bugs/AMD-Bulldozer-linux-ASLR-weakness-reducing-mmaped-files-by-eight.htmlCVE-2015-1593—Linux ASLR Integer Overflow: Reducing Stack Entropy by Four http://hmarco.org/bugs/linux-ASLR-integer-overflow.htmlLinux ASLR Mmap Weakness: Reducing Entropy by Half http://hmarco.org/bugs/linux-ASLR-reducing-mmap-by-half.htmlLESNE, A. (2014). Shannon entropy: a rigorous notion at the crossroads between probability, information theory, dynamical systems and statistical physics. Mathematical Structures in Computer Science, 24(3). doi:10.1017/s0960129512000783Scraps of Notes on Remote Stack Overflow Exploitation http://www.phrack.org/issues.html?issue=67&id=13#articleUchenick, G. M., & Vanfleet, W. M. (2005). Multiple independent levels of safety and security: high assurance architecture for MSLS/MLS. MILCOM 2005 - 2005 IEEE Military Communications Conference. doi:10.1109/milcom.2005.1605749Lee, B., Lu, L., Wang, T., Kim, T., & Lee, W. (2014). From Zygote to Morula: Fortifying Weakened ASLR on Android. 2014 IEEE Symposium on Security and Privacy. doi:10.1109/sp.2014.34The Heartbleed Bug http://heartbleed.co

Grid móvil para procesar imágenes médicas

El procesamiento de imágenes médicas ayuda a los profesionales de la medicina a tomar decisiones de diagnóstico y tratamiento de pacientes. Algunos de estos algoritmos requieren gran cantidad de recursos, por esto se pueden apoyar en la computación distribuida y la abundancia de dispositivos móviles ociosos. En un trabajo anterior, se seleccionó Boinc como Grid Móvil, no obstante, se requería modificar los algoritmos a ejecutar en dispositivos móviles para integrarlos a esta infraestructura. En el presente proyecto se abordó dicho problema junto con la compilación cruzada de la librería ITK para la arquitectura ARM y la división de imágenes para su procesamiento paralelo.Medical image processing helps health professionals make decisions to diagnose and treat patients. Some of these algorithms require large amounts of resources, this is why they can be supported by distributed computing and an abundant number of idle mobile devices. In a previous project, Boinc was selected as the infrastructure for the Mobile Grid, however, it was required to modify the algorithms that would be executed in the devices, in order to integrate them with the system. This project addressed this problem along with the cross compilation of ITK library for the ARM architecture and the division of images to be processed in parallel.Ingeniero (a) de SistemasPregrad

Safe and automatic live update

Tanenbaum, A.S. [Promotor

Address space randomization for mobile devices

Address Space Layout Randomization (ASLR) is a defensive technique supported by many desktop and server operating systems. While smartphone vendors wish to make it available on their platforms, there are technical challenges in implementing ASLR on these devices. Pre-linking, limited processing power and restrictive update processes make it difficult to use existing ASLR implementation strategies even on the latest generation of smartphones. In this paper we introduce retouching, a mechanism for executable ASLR that requires no kernel modifications and is suitable for mobile devices. We have implemented ASLR for the Android operating system and evaluated its effectiveness and performance. In addition, we introduce crash stack analysis, a technique that uses crash reports locally on the device, or in aggregate in the cloud to reliably detect attempts to brute-force ASLR protection. We expect that retouching and crash stack analysis will become standard techniques in mobile ASLR implementations