{"id":3556,"date":"2019-09-16T16:38:14","date_gmt":"2019-09-16T07:53:14","guid":{"rendered":"https:\/\/www.inforsec.org\/wp\/?p=3556"},"modified":"2019-09-16T16:41:03","modified_gmt":"2019-09-16T07:56:03","slug":"%e3%80%90inforsecraid2019%e3%80%91%e7%bd%91%e7%bb%9c%e7%a9%ba%e9%97%b4%e5%ae%89%e5%85%a8%e5%9b%bd%e9%99%85%e5%89%8d%e6%b2%bf%e5%ad%a6%e6%9c%af%e7%a0%94%e8%ae%a8%e4%bc%9a","status":"publish","type":"post","link":"http:\/\/202.112.238.40\/wp\/?p=3556","title":{"rendered":"\u3010InForSec@RAID2019\u3011\u7f51\u7edc\u7a7a\u95f4\u5b89\u5168\u56fd\u9645\u524d\u6cbf\u5b66\u672f\u7814\u8ba8\u4f1a"},"content":{"rendered":"\n

\u65f6\u95f4\uff1a<\/strong>2019\u5e749\u670822\u65e5 14:00~18:10<\/p>\n\n\n\n

\u5730\u70b9\uff1a<\/strong>\u56fd\u5bb6\u4f1a\u8bae\u4e2d\u5fc3306A\u4f1a\u8bae\u5ba4<\/p>\n\n\n\n

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\u627f\u529e\uff1a<\/strong>\u4e2d\u56fd\u79d1\u5b66\u9662\u8f6f\u4ef6\u6240<\/strong><\/p>\n\n\n\n

\u7f51\u7edc\u5b89\u5168\u7814\u7a76\u56fd\u9645\u5b66\u672f\u8bba\u575bInForSec<\/p>\n\n\n\n

\u534f\u529e\uff1a<\/strong>\u5317\u4eac\u4fe1\u606f\u79d1\u5b66\u4e0e\u6280\u672f\u56fd\u5bb6\u7814\u7a76\u4e2d\u5fc3<\/p>\n\n\n\n

      <\/strong>\u5947\u5b89\u4fe1\u96c6\u56e2<\/p>\n\n\n\n

\u7b2c22\u5c4a\u7f51\u7edc\u653b\u9632\u56fd\u9645\u5b66\u672f\u4f1a\u8bae\uff08The 22nd International Symposium on Research in Attacks, Intrusions, and Defenses\uff0cRAID 2019\uff09\u5c06\u4e8e2019\u5e749\u670823-25\u65e5\u5728\u5317\u4eac\u53ec\u5f00\u3002\u8fd9\u662fRAID\u56fd\u9645\u4f1a\u8bae\u521b\u529e22\u5e74\u4ee5\u6765\uff0c\u7b2c\u4e00\u6b21\u8fdb\u5165\u4e2d\u56fd\u3002 <\/p>\n\n\n\n

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\"\"<\/figure>\n\n\n\n

1\u3001\u5f20\u6bb7\u4e7e   \u4fc4\u4ea5\u4fc4\u5dde\u7acb\u5927\u5b66\u6559\u6388<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898\uff1a<\/strong><\/strong><\/p>\n\n\n\n

Enhancing Intel\nSGX with Better Security and Privacy<\/p>\n\n\n\n

\u5185\u5bb9\u6458\u8981\uff1a<\/strong><\/strong><\/p>\n\n\n\n

Intel Software\nGuard Extensions (SGX) is an emerging hardware feature available in modern Intel\nprocessors, which provides software applications a Trusted Execution\nEnvironment (TEE) to protect their code and data from untrusted system\nsoftware.  Being one of the most\ngame-changing technologies, SGX has attracted considerable interests from both\nacademia and industry since its debut. However, the security promises of SGX do\nnot directly lead to strong security and privacy of software running in the\nTEEs. This talk will cover two research directions for enhancing Intel SGX with\nbetter security and privacy. First, it presents a set of compiler-assisted\ntools for protecting against side-channel attacks; second, it presents an open\nremote attestation framework for enhancing the openness, privacy, and\nperformance of Intel\u2019s current attestation model.<\/p>\n\n\n\n

\u6f14\u8bb2\u5609\u5bbe\u7b80\u4ecb\uff1a<\/strong><\/strong><\/p>\n\n\n\n

Prof. Yinqian\nZhang is an Associate Professor of the Department of Computer Science and\nEngineering at The Ohio State University. His research interests span across\nmultiple domains of computer security, including cloud security, mobile\nsecurity, IoT security, software security, trusted computing, user\nauthentication, etc. His research has been frequently published at top-tier\nsecurity venues, such as IEEE S&P, ACM CCS, USENIX Security, and NDSS.\nProf. Zhang was a recipient of the Google Ph.D. Fellowship in Security in 2013,\nCAREER Award from the National Science Foundation in 2018, Lumley Research\nAward and Outstanding Teaching Award from the Ohio State University in 2019,\nand Rising Star Award from the Association of Chinese Scholars in Computing in\n2019.<\/p>\n\n\n\n

2\u3001\u674e\u6d32 \u52a0\u5dde\u5927\u5b66\u5c14\u6e7e\u5206\u6821\uff08University of California, Irvine\uff09 \u52a9\u7406\u6559\u6388<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898\uff1a<\/strong> \u63a2\u7d22\u6076\u610f\u84dd\u7259\u5916\u90e8\u8bbe\u5907\u5bf9\u624b\u673a\u5b89\u5168\u7684\u5f71\u54cd<\/p>\n\n\n\n

\u5185\u5bb9\u6458\u8981<\/strong>:<\/strong><\/p>\n\n\n\n

 \u84dd\u7259\uff08Bluetooth\uff09\u534f\u8bae\u5df2\u88ab\u5e7f\u6cdb\u7684\u5e94\u7528\u4e8e\u5404\u79cd\u901a\u4fe1\u573a\u666f\uff0c\u4f8b\u5982\u624b\u673a\u548c\u7269\u8054\u7f51\u3002\u867d\u7136\u84dd\u7259\u534f\u8bae\u672c\u8eab\u8003\u8651\u5230\u4e86\u5b89\u5168\u95ee\u9898\u5e76\u4e14\u8bf4\u660e\u4e86\u9a8c\u8bc1\uff0c\u52a0\u5bc6\uff0c\u6388\u6743\u8be5\u5982\u4f55\u5b9e\u73b0\uff0c\u84dd\u7259\u8bbe\u5907\uff08\u624b\u673a\u548c\u5916\u8bbe\uff09\u662f\u5426\u6b63\u6267\u884c\u8fd9\u4e9b\u6807\u51c6\u5e76\u6ca1\u6709\u88ab\u7cfb\u7edf\u5730\u7814\u7a76\u8fc7\u3002\u6211\u4eec\u5728\u4eca\u5e74NDSS\u7684\u7814\u7a76\u4e2d\u53d1\u73b0\u5728\u624b\u673a\u7aef\uff08\u4e3b\u8981\u662fAndroid\uff09\uff0c\u95ee\u9898\u5b58\u5728\uff0c\u5e76\u4e14\u4e0d\u5c11\u3002\u7531\u4e8e\u5b89\u5353\u5bf9\u4e8e\u84dd\u7259\u914d\u7f6e\uff08Bluetooth Profile)\u7684\u7c97\u7c92\u5ea6\u7ba1\u7406\uff0c\u4e00\u4e2a\u6076\u610f\u7684\u5916\u8bbe\u53ef\u4ee5\u8f7b\u6613\u5730\u83b7\u5f97\u5404\u79cd\u5b89\u5353\u7cfb\u7edf\u6743\u9650\uff0c\u5e76\u4e14\u7528\u6237\u5f88\u96be\u53d1\u73b0\u3002\u57fa\u4e8e\u6b64\u6211\u4eec\u5b9e\u73b0\u4e86\u5404\u79cd\u84dd\u7259\u653b\u51fb\uff08\u547d\u540d\u4e3aBadBluetooth\uff09\u5e76\u4e14\u5728\u5b89\u5353\u4e0a\u6d4b\u8bd5\u6210\u529f\u3002\u540c\u65f6\uff0c\u6211\u4eec\u5b9e\u73b0\u4e86\u4e00\u4e2a\u7ec6\u7c92\u5ea6\u7684\u84dd\u7259\u914d\u7f6e\u7ba1\u7406\u6a21\u578b\u6765\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\u3002\u901a\u8fc7\u4e0eGoogle\u548c\u9ad8\u901a\u7684\u6c9f\u901a\uff0c\u90e8\u5206\u95ee\u9898\u5df2\u7ecf\u5f97\u5230\u4fee\u590d\u3002<\/p>\n\n\n\n

\u6f14\u8bb2\u5609\u5bbe\u7b80\u4ecb\uff1a<\/strong> <\/strong><\/p>\n\n\n\n

\u674e\u6d32\u662f\u52a0\u5dde\u5927\u5b66\u5c14\u6e7e\u5206\u6821\uff08University of California, Irvine\uff09\u7684\u52a9\u7406\u6559\u6388\u3002\u4ed6\u7684\u4e3b\u8981\u7814\u7a76\u65b9\u5411\u662f\u5927\u6570\u636e\u5b89\u5168\u5206\u6790\uff0c\u4fa7\u4fe1\u9053\uff0c\u4e92\u8054\u7f51\u6d4b\u91cf\u4ee5\u53ca\u7269\u8054\u7f51\uff08IoT\uff09\u5b89\u5168\u3002\u4ed6\u5728\u56db\u5927\u9876\u7ea7\u5b89\u5168\u4f1a\u8bae\uff08IEEE S&P\uff0cACM CCS\uff0cUsenix Security\uff0cNDSS\uff09\u90fd\u6709\u6587\u7ae0\u53d1\u8868\uff0c\u603b\u8ba117\u7bc7\u3002\u4ed6\u540c\u65f6\u5e38\u5e74\u5728\u8fd9\u4e9b\u4f1a\u8bae\u7684\u8bc4\u5ba1\u59d4\u5458\u4f1a\u4efb\u804c\u3002\u5728\u52a0\u5165\u5c14\u6e7e\u5206\u6821\u4e4b\u524d\uff0c\u4ed6\u5728\u5b89\u5168\u516c\u53f8RSA Security\u62c5\u4efb\u9996\u5e2d\u7814\u7a76\u79d1\u5b66\u5bb6\u63a5\u8fd1\u4e94\u5e74\u3002\u591a\u9879\u7814\u7a76\u6210\u679c\u5df2\u88ab\u96c6\u6210\u5165\u516c\u53f8\u4ea7\u54c1\u3002<\/p>\n\n\n\n

3\u3001\u8521\u9547\u9686 \u65b0\u52a0\u5761\u56fd\u7acb\u5927\u5b66\u52a9\u7406\u7814\u7a76\u5458<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898<\/strong>:<\/strong> Towards\na knowledge-oriented future for binary analysis<\/p>\n\n\n\n

\u5185\u5bb9\u6458\u8981<\/strong>:<\/strong><\/p>\n\n\n\n

Binary\nanalysis is a fundamental technique in software and system security. It has a\nwide range of applications, such as vulnerability discovery, attack response,\nmalware analysis, and software testing and debugging. Due to the lack of\nhigh-level semantics and complex program behaviors, it is challenging for\nbinary analysis solutions to scale up to large real-world binaries in practice.\nExisting solutions are often task-driven and bounded by a practical time limit,\nhindering a comprehensive understanding of programs and making it difficult to\nintegrate different solutions. In this talk, we discuss our solutions,\nreflections, and ongoing efforts in scaling up binary analysis in a\nknowledge-oriented manner. We believe knowledge accumulation is the key to scale\nup binary analysis, where binary analysis solutions generate understandings\nthat can be shared and reused in other solutions. Our investigation includes\ntechniques for knowledge extraction, tools for knowledge integration, and\nplatforms for knowledge accumulation and sharing. The accumulated knowledge not\nonly allows broader and deeper analysis into binaries, but it also enables\nemerging data-driven and learning techniques to be effectively adopted in\nbinary analysis tasks.<\/p>\n\n\n\n

\u6f14\u8bb2\u5609\u5bbe\u7b80\u4ecb\uff1a<\/strong><\/strong><\/p>\n\n\n\n

Dr\nCHUA Zheng Leong is currently a research fellow working at National University\nof Singapore (NUS). He co-founded NUSGreyhats, a security special interest\ngroup in NUS, participating in CTFs and vulnerability research in his free\ntime. His publications range from data-oriented attacks and side channels to\napplications of learning on binary analysis. His thesis work aims to alleviate\nthe pain of binary analysis through the introduction of an alternate,\nknowledge-oriented, paradigm for binary analysis where traditional binary analysis\ntechniques are combined with machine learning and community effort to provide\nand manage large, diverse sources of knowledge about a binary. He received his\nPh.D. degree in Computer Science from NUS in 2019 and his B.Comp in Computer\nScience from NUS in 2012.<\/p>\n\n\n\n

4\u3001\u674e\u7426  \u6e05\u534e\u5927\u5b66\u7f51\u7814\u9662\u526f\u6559\u6388<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

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\u5185\u5bb9\u6458\u8981<\/strong>:<\/strong><\/p>\n\n\n\n

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\u6f14\u8bb2\u5609\u5bbe\u7b80\u4ecb\uff1a<\/strong><\/strong><\/p>\n\n\n\n

\u674e\u7426\u76ee\u524d\u4e3a\u6e05\u534e\u5927\u5b66\u526f\u6559\u6388\uff0c\u7814\u7a76\u5174\u8da3\u5305\u62ec\u4e92\u8054\u7f51\u548c\u4e91\u5b89\u5168\u3001\u79fb\u52a8\u5b89\u5168\u4ee5\u53ca\u5927\u6570\u636e\u5b89\u5168\u3002\u7814\u7a76\u6210\u679c\u88ab\u8c37\u6b4c\u548c\u817e\u8baf\u7b49\u91c7\u7eb3\uff0c\u66fe\u83b7\u5f97\u4e862017\u5e74\u5317\u4eac\u5e02\u79d1\u5b66\u6280\u672f\u4e8c\u7b49\u5956\u7b49\u8363\u8a89\u3002\u53d1\u8868\u8bba\u6587\u5305\u62ecUSENIX Security\u3001ACM CCS\u548cIEEE\/ACM\u6c47\u520a\u7b49\u4f1a\u8bae\u548c\u671f\u520a\u8bba\u6587\u3002\u76ee\u524d\u662fIEEE TDSC\u548cACM DTRAP\u7b49\u56fd\u9645\u671f\u520a\u7684\u7f16\u59d4\uff0c\u56fd\u9645\u4f1a\u8baeRAID 2019\u4f1a\u8bae\u526f\u4e3b\u5e2d\u3001\u4ee5\u53caNDSS\u548cACSAC\u7b49\u4f1a\u8bae\u7684\u7a0b\u5e8f\u6216\u7ec4\u7ec7\u59d4\u5458\u4f1a\u59d4\u5458\u3002<\/p>\n\n\n\n

5\u3001\u5415\u52c7\u5f3a  \u6e05\u534e\u5927\u5b66\u4fe1\u606f\u56fd\u5bb6\u4e2d\u5fc3\u526f\u7814\u7a76\u5458<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898\uff1aCPU\u5b89\u5168\u533a\u4e0d\u5b89\u5168\u2014\u2014VoltJockey\uff1a\u57fa\u4e8e\u7535\u538b\u6545\u969c\u6ce8\u5165\u7684CPU\u653b\u51fb<\/p>\n\n\n\n

\u5185\u5bb9\u6458\u8981\uff1a<\/p>\n\n\n\n

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\u6f14\u8bb2\u5609\u5bbe\u7b80\u4ecb\uff1a<\/strong><\/strong><\/p>\n\n\n\n

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6\u3001\u5c39\u6052  \u52a0\u5dde\u5927\u5b66\u6cb3\u6ee8\u5206\u6821\u6559\u6388<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u6f14\u8bb2\u4e3b\u9898\uff1aFirm-AFL: High-Throughput Greybox Fuzzing of IoT\nFirmware via Augmented Process Emulation<\/p>\n\n\n\n

\u5185\u5bb9\u6458\u8981\uff1a<\/p>\n\n\n\n

Cyber attacks\nagainst IoT devices are a severe threat. These attacks exploit software\nvulnerabilities in IoT firmware. Fuzzing is an effective software testing\ntechnique for finding these vulnerabilities so they can be patched. In this\nwork, we present FIRM-AFL, the first high-throughput greybox fuzzer for IoT\nfirmware. FIRM-AFL addresses two fundamental problems in IoT fuzzing. First, it\naddresses compatibility issues by enabling fuzzing for POSIX-compatible\nfirmware that can be emulated in a system emulator. Second, it addresses the\nperformance bottleneck caused by system-mode emulation with a novel technique\ncalled augmented process emulation. By integrating system-mode emulation and\nusermode emulation, augmented process emulation provides high compatibility as\nsystem-mode emulation and high throughput as user-mode emulation. Our\nevaluation results show that (1)FIRM-AFL is fully functional and capable of\nfinding real world vulnerabilities in IoT programs and (2) the throughput of\nFIRM-AFL is on average 10 times higher than system-mode emulation based\nfuzzing; and (3) FIRM-AFL is able to find 1-day vulnerabilities much faster\nthan system-mode emulation based fuzzing, and is able to find 0-day\nvulnerabilities.<\/p>\n\n\n\n

\u6f14\u8bb2\u5609\u5bbe\u7b80\u4ecb\uff1a<\/strong><\/strong><\/p>\n\n\n\n

Dr. Heng Yin is an\nassociate professor in the department of Computer Science and Engineering at\nUniversity of California, Riverside. He is the director of CRESP (Center for\nResearch and Education in Cyber Security and Privacy) at UCR. He obtained his\nPhD degree from College of William and Mary in 2009, and MS and BS from\nHuazhong University of Science and Technology in 2002 and 1999. His research\ninterests lie in computer security, with emphasis on\nbinary code analysis. His publications appear in top-notch technical\nconferences and journals, such as ACM CCS, USENIX Security, NDSS, TSE,TDSC,\netc. His research is sponsored by National Science Foundation (NSF), Defense\nAdvanced Research Projects Agency (DARPA), Air Force Office of Scientific Research\n(AFOSR), and Office of Navy Research (ONR). In 2011, he received prestigious\nNSF Career award. He was the technical co-lead of CodeJitsu, one of the seven\nfinalists in DARPA Cyber Grand Challenge. <\/p>\n","protected":false},"excerpt":{"rendered":"

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