CAAD VILLAGE - GeekPwn - The Uprising Geekpwn AI/Robotics Cybersecurity Contest U.S. 2018 - Weapons for Dog Fight:Adapting Malware to Anti-Detection based on GAN

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Video in TIB AV-Portal: CAAD VILLAGE - GeekPwn - The Uprising Geekpwn AI/Robotics Cybersecurity Contest U.S. 2018 - Weapons for Dog Fight:Adapting Malware to Anti-Detection based on GAN

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CAAD VILLAGE - GeekPwn - The Uprising Geekpwn AI/Robotics Cybersecurity Contest U.S. 2018 - Weapons for Dog Fight:Adapting Malware to Anti-Detection based on GAN
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2018
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English

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Zhuang Zhang, Bo Shi, Hangfeng Dong, from Tencent Yunding Lab(Tweet@YDLab9) Since the malware come out, there is a fight between malware and AV. So more and more methods based on machine learning apply to detect malware. We will share how to detect polymorphic malware based on CNN,then we will introduce a method use generative adversarial network to generate adversarial malware examples to bypass machine learning based detection models. Zhuang Zhang is the senior researcher at Tencent Yunding Laboratory. Bo Shi is the Ecosystem Director of Tencent Yunding Laboratory. Hangfeng Dong is the researcher of Tencent Yunding Laboratory.
Optical disc drive Representation (politics) Figurate number
Rule of inference Focus (optics) Service (economics) Constructor (object-oriented programming) Product (business) Fraction (mathematics) Machine learning Malware Software Integrated development environment Semiconductor memory Operator (mathematics) Point cloud Information security
Dynamical system Parity (mathematics) Weight Weight Mathematical analysis Sound effect Set (mathematics) Mereology Graph coloring Rule of inference Electronic signature Fluid statics Malware Annihilator (ring theory)
Vapor barrier Software Semiconductor memory Phase transition Pentagram Multiplication sign Mathematical analysis Virtual machine Generic programming Neuroinformatik Product (business) Power (physics)
Mathematics Sign (mathematics) Mobile app Electronic signature Product (business)
Malware Functional (mathematics) Service (economics) Inheritance (object-oriented programming) Observational study Code Different (Kate Ryan album) Similarity (geometry) Electronic signature Rhombus
Physical law Product (business)
Medical imaging Machine learning Spherical cap Inheritance (object-oriented programming) Different (Kate Ryan album) Term (mathematics) Factory (trading post) Energy level Boundary value problem Rule of inference Family Product (business)
Machine learning Computer file Real number Virtual machine Line (geometry) Electronic signature Numeral (linguistics) Machine learning Different (Kate Ryan album) Entropie <Informationstheorie> Heuristic Data structure Family Data structure
Noise (electronics) Latent heat Programming paradigm Software Blind spot (vehicle) Virtual machine Energy level Endliche Modelltheorie
Noise (electronics) Medical imaging Mathematics Computer file Profil (magazine) Computer-generated imagery Computer file Data structure Pixel
Algorithm Artificial neural network Linear regression Weight Multiplication sign Virtual machine Black box 2 (number) Product (business) Software Speech synthesis Endliche Modelltheorie Data structure Imaginary number Computer architecture
Algorithm Randomization Malware Standard deviation Machine learning Process (computing) Multiplication sign Mathematical analysis Virtual machine Black box
Computer file Data structure Field (computer science)
Architecture Email Malware Computer file Different (Kate Ryan album) Network topology Sheaf (mathematics) Data structure Table (information)
Medical imaging Mathematics Demon Computer file Horizon Moving average Codierung <Programmierung> Annihilator (ring theory) Neuroinformatik
Process (computing) Phase transition Software testing Wave packet Spacetime Wave packet
Architecture Randomization Email Message passing Malware Computer file Mereology Wave packet
Natural number Blind spot (vehicle) Website Right angle Software testing Maxima and minima Cycle (graph theory) Algebra Product (business) Electronic signature
Distribution (mathematics) Game controller Functional (mathematics) Randomization Inheritance (object-oriented programming) Image resolution Interface (computing) Binary code Fitness function 1 (number) Combinational logic Binary file Dressing (medical) Medical imaging Mathematics Personal digital assistant output Right angle Software testing Codierung <Programmierung> Endliche Modelltheorie God
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you know well I think I have a question Oh Oh God he said you've trained the model with behind bars tonight issues like random Claussen to use like Microsoft librarians things like that secret I'm random famine the revealed that she confronted Emily with the master fell it'll come parents that means that make a self made us all feel it's a still not abnormal fail yeah all the test cases and but you change more fries and how can't be detected but does it do the same things now he does doing the exact same things or have something has something changed I think that because for example right achieves the wrong fit in there and it hits a incorrect weather dress then the father it's gonna be useless now because now where it's not be able to hit that's command control interfaces can't do anything in combination by hand yeah yeah we by human to check the semantics so you're writing a sandbox to check the malicious okay and adding to that so we also did some similar thing for the bin - so you input to the discriminator so it looks like the benign those bananas I think can be random right and maybe not house and not necess to be because it's approximates a distribution so it's banana and the for the malicious one actually we do something like oh if you are generating on the binary code and you keep the ones and only changes they're lost so that it's kind of like you only add function but you don't delete things so that to me you will be able to preserve the malicious functions and behaviors yeah but you need to check I'm sure but trading trading hands has been difficult yeah I see newscast not autoencoders right you use cameras out in the cold all right yeah he used a generate indiscriminate instead of the auto encoder which is an encoder and decoder what is it again what kind of can you are using I think um why my understanding is for gain in image it's hard because it's very hard to generate high-resolution images but for the like the male well the binary case it's relatively easy because you don't need to care about this high resolution and you know realistic features but again it is hard but you can look at the generate Handy's from her small sander I think but yeah but I don't know what kind of care you are using but I think waterskiing is better questions so so okay let's thing with becoming
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