InForSec\u7f51\u5b89\u56fd\u9645\u5b66\u672f\u8bba\u575b<\/p>\n
\u65f6\u95f4\uff1a<\/strong>2017\u5e747\u67087\u65e5\uff08\u5468\u4e94\uff09\u4e0a\u53489:00-12:00<\/p>\n \u5730\u70b9\uff1a<\/strong>\u4e2d\u56fd\u79d1\u5b66\u9662\u8f6f\u4ef6\u7814\u7a76\u62405\u53f7\u697c334\u4f1a\u8bae\u5ba4<\/p>\n \u4f1a\u8bae\u65e5\u7a0b<\/strong><\/p>\n \uff08\u8bba\u575b\u53c2\u4e0e\u56de\u6267\u201c\u59d3\u540d+\u5355\u4f4d+\u624b\u673a+\u90ae\u7bb1\u201d\u8bf7\u53d1\u9001\u5230\uff1ayanjia@iscas.ac.cn\uff09<\/p>\n \u00a0\u00a0 \u5c39\u6052 \u52a0\u5dde\u5927\u5b66\u6cb3\u6ee8\u5206\u6821\u526f\u6559\u6388<\/strong><\/p>\n \u6f14\u8bb2\u4e3b\u9898\uff1aTowards Scalable and Accurate Cross-Architecture Binary Code Search<\/strong><\/p>\n \u5185\u5bb9\u6458\u8981<\/strong>\uff1aGiven a binary code function, we would like to quickly find a set of binary code functions that are semantically equivalent or similar to it. These functions may come from different architectures and platforms. This problem is known as cross-architecture binary code search, and has many security applications, such as plagiarism detection, malware detection, vulnerability search. Recent studies have demonstrated that control-flow graph (CFG) based binary code search techniques can be effective and accurate across different architectures. However, these CFG-based binary code search approaches are far from being scalable, due to their expensive graph matching overhead. Inspired by rich experience in image and video search, we propose a codebook based search scheme which addresses the scalability challenge and further improves search accuracy. Unlike existing techniques that directly conduct searches based upon raw features (CFGs) from the binary code, we convert the CFGs into high-level numeric feature vectors. Compared with the CFG feature, high-level numeric feature vectors are more robust to code variation across different architectures, and can easily achieve realtime search by using state-of-the-art hashing techniques. \u00a0To further improve the search accuracy and encoding efficiency, we propose a deep neural network based approach, which outperforms the state-of-the-art approaches by large margins with respect to accuracy. Further, this deep learning based approach can speed up prior art\u2019s encoding time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours.<\/p>\n \u5609\u5bbe\u7b80\u4ecb\uff1aDr. Heng Yin is an associate professor in the department of Computer Science and Engineering at University of California, Riverside. He obtained his PhD degree from College of William and Mary in 2009, and MS and BS from Huazhong University of Science and Technology in 2002 and 1999. His research interests lie in computer security, with emphasis on binary code analysis. His publications appear in top-notch technical conferences and journals, such as ACM CCS, USENIX Security, NDSS, TSE,TDSC, etc. His research is sponsored by National Science Foundation(NSF), Defense Advanced Research Projects Agency (DARPA), Air Force Office of Scientific Research (AFOSR), and Office of Navy Research (ONR). In 2011, he received prestigious NSF Career award. He was the technical co-lead of CodeJitsu, one of the seven finalists in DARPA Cyber Grand Challenge.<\/p>\n \u8d3e\u76f8\u5803 \u4e2d\u79d1\u9662\u8f6f\u4ef6\u6240\u8f6f\u4ef6\u667a\u80fd\u5206\u6790\u534f\u540c\u521b\u65b0\u56e2\u961f\u535a\u58eb\u751f<\/strong><\/p>\n \u6f14\u8bb2\u4e3b\u9898\uff1aTowards Efficient Heap Overflow Discovery<\/strong><\/p>\n \u5185\u5bb9\u6458\u8981<\/strong>\uff1aHeap overflow is a prevalent memory corruption vulnerability, playing an important role in recent attacks. Finding such vulnerabilities in applications is thus critical for security. Many state-of-art solutions focus on runtime detection, requiring abundant inputs to explore program paths in order to reach a high code coverage and luckily trigger security violations. It is likely that the inputs being tested could exercise vulnerable program paths, but fail to trigger (and thus miss) vulnerabilities in these paths. Moreover, these solutions may also miss heap vulnerabilities due to incomplete vulnerability models. We propose a new solution HOTracer to discover potential heap vulnerabilities. We model heap overflows as spatial inconsistencies between heap allocation and heap access operations, and perform an in-depth offline analysis on representative program execution traces to identify heap overflows. Combining with several optimizations, it could efficiently find heap overflows that are hard to trigger in binary programs.<\/p>\n \u6f14\u8bb2\u8005\u7b80\u4ecb<\/strong>\uff1a\u8d3e\u76f8\u5803\uff0c\u4e2d\u79d1\u9662\u8f6f\u4ef6\u6240\u8f6f\u4ef6\u667a\u80fd\u5206\u6790\u534f\u540c\u521b\u65b0\u56e2\u961f\u535a\u58eb\u751f\uff0c\u7814\u7a76\u65b9\u5411\u7cfb\u7edf\u548c\u8f6f\u4ef6\u5b89\u5168\u3001\u4e8c\u8fdb\u5236\u9006\u5411\u5206\u6790\u3001\u6f0f\u6d1e\u6316\u6398\u548c\u5206\u6790\u7b49\u3002\u66fe\u5bf9\u591a\u6b3e\u5e38\u7528\u8f6f\u4ef6\u8fdb\u884c\u5206\u6790\uff0c\u6316\u6398\u51fa\u672a\u77e5\u6f0f\u6d1e47\u4e2a\uff0c\u83b7\u5f972\u4e2aCVE\u6f0f\u6d1e\u7f16\u53f7\uff0c\u5e76\u83b7\u5f97\u5382\u5546\u7684\u5956\u52b1\u548c\u611f\u8c22\u3002\u8bba\u6587\u300aTowards Efficient Heap Overflow Discovery\u300b\u88abUSENIX Security 2017\u5f55\u7528\u3002<\/p>\n \u674e\u5eb7 \u7f8e\u56fd\u4e54\u6cbb\u4e9a\u5927\u5b66\u6559\u6388<\/strong><\/p>\n \u6f14\u8bb2\u4e3b\u9898\uff1aThoughts about Evaluating Autonomous Hacking<\/strong><\/p>\n \u5185\u5bb9\u6458\u8981<\/strong>\uff1aThe recent CTF competitions between human and machines have brought a hype of combining artificial intelligence with cyber security tasks. \u00a0Autonomous hacking is a noticeable one of such AI and security fusion. \u00a0Although autonomous hacking has been considered as a research goal by DARPA and other research agencies, \u00a0it is still unclear how to systematically evaluate autonomous hacking capabilities, and thus it is hard to define the success and progress of autonomous hacking.<\/p>\n In order to consider systematic methods to evaluate autonomous hacking, the speaker first attempts to define hacking as an unique programming activity \u2014 coding on unintended computing artifacts. \u00a0Consequently, fully autonomous hacking becomes computing artifact discovery along with autonomous programming over these artifacts. \u00a0The breaking down of tasks allow us to consider realistic evaluation metrics for individual hacking tasks, such as measuring vulnerability discovery efficiency and exploitation generation capability. \u00a0Whereas, wholistic evaluation of a complete autonomous systems is considered more challenging and difficulty to design. \u00a0Based on a few initial effort of developing and testing autonomous hacking platforms, the speaker summarizes the current evaluation practice and limitations, and the speaker advocates for a deeper discussion about how to setup platforms to evaluate autonomous hacking capabilities.<\/p>\n \u5609\u5bbe\u7b80\u4ecb<\/strong>\uff1aKang Li is a Professor of Computer Science at the University of Georgia and the Director of Georgia Institute for Cyber Security and Privacy. Dr. Li received a B.S. degree in computer science from Tsinghua University, a Master of Legal Study from Yale Law School, and a Ph.D in computer science and engineering from Oregon Graduate Institute at Portland, Oregon. \u00a0Professor Kang Li’s research interests are in the areas of Computer Systems, Networking, and Cyber Security. His research results have been published at academic venues, such as IEEE S&P, ACM CCS and NDSS, as well as industrial meetings, such as BlackHat, SyScan, and ShmooCon. \u00a0Dr. Kang Li is the founder and mentor of multiple CTF security teams, including SecDawg and Blue-Lotus. \u00a0He was the leader and player of Team Disekt, one of the finalist teams in DARPA Cyber Grand Challenge.<\/p>\n","protected":false},"excerpt":{"rendered":" \u201c\u8f6f\u4ef6\u667a\u80fd\u5206\u6790\u201d\u5b66\u672f\u6c99\u9f99\u7b2c\u4e8c\u6b21\u6d3b\u52a8\u5c06\u4e8e2017\u5e747\u67087\u65e5\uff08\u661f\u671f\u4e94\u4e0a\u5348\uff099\u70b9\u5728\u4e2d\u56fd\u79d1\u5b66\u9662\u8f6f\u4ef6\u7814\u7a76\u62405\u53f7… Continue reading \n\n
\n \u65f6\u95f4<\/strong><\/td>\n \u4e3b\u8bb2\u5609\u5bbe<\/strong><\/td>\n \u4e3b\u9898<\/strong><\/td>\n<\/tr>\n \n 9:00\u00a0\u2013 12:00<\/td>\n \u4e3b\u6301\u4eba\uff1a\u82cf\u749e\u777f \u4e2d\u79d1\u9662\u8f6f\u4ef6\u6240\u7814\u7a76\u5458<\/td>\n<\/tr>\n \n 9:00\u00a0\u2013\u00a010:00<\/td>\n \u4e13\u5bb6\u62a5\u544a<\/td>\n \u5c39\u6052<\/td>\n Towards Scalable and Accurate Cross-Architecture Binary Code Search<\/td>\n<\/tr>\n \n 10:00\u00a0\u2013\u00a011:00<\/td>\n \u4e13\u5bb6\u62a5\u544a<\/td>\n \u8d3e\u76f8\u5803<\/td>\n Towards Efficient Heap Overflow Discovery<\/td>\n<\/tr>\n \n 11:00\u00a0\u2013\u00a012:00<\/td>\n \u4e13\u5bb6\u62a5\u544a<\/td>\n \u674e\u5eb7<\/td>\n Thoughts about Evaluating Autonomous Hacking<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n <\/p>\n
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