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Emma Woollacott02 June 2020 at 13:14 UTC Updated: 02 June 2020 at 14:48 UTC

ML security evasion event is based on a similar competition held at DEF CON 27 last summer

The defensive capabilities of machine learning (ML) systems will be stretched to the limit at a Microsoft security event this summer.

Along with various industry partners, the company is sponsoring a Machine Learning Security Evasion Competition involving both ML experts and cybersecurity professionals.

The event is based on a similar competition held at AI Village at DEF CON 27 last summer, where contestants took part in a white-box attack against static malware machine learning models.

Several participants discovered approaches that completely and simultaneously bypassed three different machine learning anti-malware models.

The 2020 Machine Learning Security Evasion Competition is similarly designed to surface countermeasures to adversarial behavior and raise awareness about the variety of ways ML systems may be evaded by malware, in order to better defend against these techniques, says Hyrum Anderson, Microsofts principal architect for enterprise protection and detection.

The competition will consist of two different challenges. A Defender Challenge will run from June 15 through July 23, with the aim of identifying new defenses to counter cyber-attacks.

The winning defensive technique will need to be able to detect real-world malware with moderate false-positive rates, says the team.

Next, an Attacker Challenge running from August 6 through September 18 provides a black-box threat model.

Participants will be given API access to hosted anti-malware models, including those developed in the Defender Challenge.

RECOMMENDED DEF CON 2020: Safe Mode virtual event will be free to attend, organizers confirm

Contestants will attempt to evade defenses using hard-label query results, with samples from final submissions detonated in a sandbox to make sure theyre still functional.

The final ranking will depend on the total number of API queries required by a contestant, as well as evasion rates, says the team.

Each challenge will net the winner $2,500 in Azure credits, with the runner up getting $500 in Azure credits.

To win, researchers must publish their detection or evasion strategies. Individuals or teams can register on the MLSec website.

Companies investing heavily in machine learning are being subjected to various degrees of adversarial behavior, and most organizations are not well-positioned to adapt, says Anderson.

It is our goal that through our internal research and external partnerships and engagements including this competition well collectively begin to change that.

READ MORE Going deep: How advances in machine learning can improve DDoS attack detection

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Microsoft throws weight behind machine learning hacking competition - The Daily Swig

The Global Machine Learning Chip Market Size Is Expected To Reach $8,272 Million In 2022 From $4,495 Million In 2015, Growing At A Cagr Of 9.4% From 2016 To 2022. The Global Machine Learning Chip Market report draws precise insights by examining the latest and prospective industry trends and helping readers recognize the products and services that are boosting revenue growth and profitability. The study performs a detailed analysis of all the significant factors, including drivers, constraints, threats, challenges, prospects, and industry-specific trends, impacting the market on a global and regional scale. Additionally, the report cites worldwide market scenario along with competitive landscape of leading participants.

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Machine Learning Chip Market Is Thriving Worldwide to reach $8,272 Million by 2022 | Advanced Micro Devices, Inc., Google Inc., Graphcore, Intel...

London, June 03, 2020 (GLOBE NEWSWIRE) -- Artificial intelligence has emerged as the most potent technologies over the past few years, that is transitioning the landscape of almost all industry verticals. Although enterprise applications based on AI and machine learning (ML) are still in the nascent stages of development, they are gradually beginning to drive innovation strategies of the business.

In the supply chain and logistics industry, artificial intelligence is gaining rapid traction among industry stakeholders. Players operating in the supply chain and logistics industry are increasingly realizing the potential of AI to solve the complexities of running a global logistics network. Adoption of artificial intelligence in the supply chain is routing a new era or industrial transformation, allowing the companies to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in digital world.

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Based on application, AI in supply chain market is broadly segmented into supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics. In 2019, the supply chain planning segment commanded the largest share of the overall AI in supply chain market. The growth of this segment can be attributed to the increasing demand for enhancing factory scheduling & production planning and the evolving agility and optimization of supply chain decision-making. In addition, digitizing existing processes and workflows to reinvent the supply chain planning model is also contributing to the growth of this segment.

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Based on geography, the global artificial intelligence in supply chain market is categorized into five major geographies, namely, North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. In 2019, North America commanded for the largest share of the global artificial intelligence in supply chain market, followed by Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The large share of the North American region is attributed to the presence of developed economies focusing on enhancing the existing solutions in the supply chain space, and the existence of major players in this market along with a high willingness to adopt advanced technologies.

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Artificial Intelligence (AI) in Supply Chain Market is projected to reach $21.8 billion by 2027, Growing at a CAGR of 45.3% from 2019- Meticulous...

DeepMind has recently releasedAcme, a library with an objective to simplify the development ofreinforcement learningalgorithms and agent building blocks. This application can be run at various scales of execution and it is achieved by enabling AI-driven agents to enable simple agent implementations. Acme can be used to create agents with greater parallelization than in previous approaches as per reports. This tool can be used by researchers to reproduce published RL algorithms or rapidly prototype ideas. Acme aims to make the results of various reinforcement learning (RL) algorithms developed in academia and industrial labs easier to reproduce and extend.

Acme strives to bring simple, efficient, and readable agents, that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research. The design of Acme also attempts to provide multiple points of entry to the RL problem at differing levels of complexity.

Github: https://github.com/deepmind/acme

Paper: https://arxiv.org/pdf/2006.00979.pdf

Installation

To installacmecore:

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Asif Razzaq is an AI Tech Blogger and Digital Health Business Strategist with robust medical device and biotech industry experience and an enviable portfolio in development of Health Apps, AI, and Data Science. An astute entrepreneur, Asif has distinguished himself as a startup management professional by successfully growing startups from launch phase into profitable businesses. This has earned him awards including, the SGPGI NCBL Young Biotechnology Entrepreneurs Award.

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DeepMind releases Acme: A library of reinforcement learning components and agents - MarkTechPost