DOCSLIB.ORG

The Cyber Security Arms Race and How AI Is Changing the Battlefield

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Cyber Security Arms Race and How AI Is Changing the Battlefield The Cyber Security Arms Race and How AI is Changing the Battlefield Dr. Ben Apple A Brief History of AI • AI was founded as an academic discipline in 1956 • Dartmouth College, in Hanover, New Hampshire, the term "artificial intelligence" was coined. • From 1974 to 1980 is a time known as the winter of AI • Unfulfilled promises and waning government interest resulted in reduced funding • Around 1980 there was an AI boom • British government started funding AI to compete with efforts by the Japanese. • 1987 saw the second AI Winter • Stock market collapse blamed on expert system trading • From 1993 to 2011 new interest in AI • 1997, IBM's Deep Blue became the first computer to beat a chess champion • 2011 to present day – The age of Deep Learning, Big Data, and General AI • 2011, Watson won Jeopardy by beating out the reigning champions • ChatBot beats the Turing Test • AlphaGo seen by many as the new dawn of AI • 2015 AlphaGo to beats a human Go master • 2016 AlphaGo beats Lee Sedol one of the top 5 Go masters in the world • 2017 AlphaGo beats Ke Jie the worlds number 1 Go master and is awarded the level of 9-Dan AI Under Attack • AI is not a Panacea • AI Cannot be eft to its own devices • The use of AI in Cyber Security requires a Hybrid Approach • AI will free our high value assets from the mundane • AI provides the predictive insight needed to head-off attacks before they occur The Emergence of AI in Cyber SEcurity • Machine learning and artificial intelligence (AI) are being applied more broadly across industries and governments. • This means new exploits and weaknesses can quickly be identified and analyzed to help mitigate further attacks. • If trained by the best AI can become the best. The Future Face of the Cyber Security Profession • The growing capabilities of artificial intelligence are triggering a battle across the cyber security fence – and organizations must act now to be on the right side of it. • Not only must organizations invest in preventative AI, but the government, at all levels, must continue to back the development of the next generation of technology professionals. • If we are going to be able to protect the crown jewels, we must invest in the cadre of future Cyber Security professionals, the adversaries are certainly investing in their cadre. • The need for a cyber security overhaul is necessary as the traditional practcises are no longer effective in identifying and preventing attacks. The Attackers Will Up Their AI Game • Software and Systems ae Complex, it is very near impossible to identify all vulnerabilities prior to release, creating a target rich environment for AI powered attackers. • The attackers are using AI to scan the Internet looking for possible targets • Then there is the weakest link in the chain, the human. • AI is being used to emulate human behaviors in phishing attacks. Insider Threat • AI is being effectively used to detect anomalous user behavior. • AI is well suited for tasks such as Sentiment Analyses, to identify possible disgruntled employees. We still need a human to make the actual judgement call. The Skills Gap • Centre for Cyber Safety and Education revealing that the world will face a shortfall of 1.8 million cyber security professionals by 2022. • AI can release these scarce resources from the mundane. • Preventive and Predictive AI will enhance the delivered value of the Cyber Seurity Professional. The Future • Recognize what machines do best and what people do best. • Use AI to do the repetitive, such as first pass log review. • Cyber Security Professionals use their minds to develop creative solutions to the hard problems. • AI acts as the inference engine that feeds the Cyber Security Decision makers. • AI will keep us the defenders ahead of the adversary, the attackers.
Load more

Recommended publications
  • The Discovery of Chinese Logic Modern Chinese Philosophy
    The Discovery of Chinese Logic Modern Chinese Philosophy Edited by John Makeham, Australian National University VOLUME 1 The titles published in this series are listed at brill.nl/mcp. The Discovery of Chinese Logic By Joachim Kurtz LEIDEN • BOSTON 2011 This book is printed on acid-free paper. Library of Congress Cataloging-in-Publication Data Kurtz, Joachim. The discovery of Chinese logic / by Joachim Kurtz. p. cm. — (Modern Chinese philosophy, ISSN 1875-9386 ; v. 1) Includes bibliographical references and index. ISBN 978-90-04-17338-5 (hardback : alk. paper) 1. Logic—China—History. I. Title. II. Series. BC39.5.C47K87 2011 160.951—dc23 2011018902 ISSN 1875-9386 ISBN 978 90 04 17338 5 Copyright 2011 by Koninklijke Brill NV, Leiden, The Netherlands. Koninklijke Brill NV incorporates the imprints Brill, Global Oriental, Hotei Publishing, IDC Publishers, Martinus Nijhoff Publishers and VSP. All rights reserved. No part of this publication may be reproduced, translated, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior written permission from the publisher. Authorization to photocopy items for internal or personal use is granted by Koninklijke Brill NV provided that the appropriate fees are paid directly to The Copyright Clearance Center, 222 Rosewood Drive, Suite 910, Danvers, MA 01923, USA. Fees are subject to change. CONTENTS List of Illustrations ...................................................................... vii List of Tables .............................................................................
    [Show full text]
  • (CMPUT) 455 Search, Knowledge, and Simulations
    Computing Science (CMPUT) 455 Search, Knowledge, and Simulations James Wright Department of Computing Science University of Alberta [email protected] Winter 2021 1 455 Today - Lecture 22 • AlphaGo - overview and early versions • Coursework • Work on Assignment 4 • Reading: AlphaGo Zero paper 2 AlphaGo Introduction • High-level overview • History of DeepMind and AlphaGo • AlphaGo components and versions • Performance measurements • Games against humans • Impact, limitations, other applications, future 3 About DeepMind • Founded 2010 as a startup company • Bought by Google in 2014 • Based in London, UK, Edmonton (from 2017), Montreal, Paris • Expertise in Reinforcement Learning, deep learning and search 4 DeepMind and AlphaGo • A DeepMind team developed AlphaGo 2014-17 • Result: Massive advance in playing strength of Go programs • Before AlphaGo: programs about 3 levels below best humans • AlphaGo/Alpha Zero: far surpassed human skill in Go • Now: AlphaGo is retired • Now: Many other super-strong programs, including open source Image source: • https://www.nature.com All are based on AlphaGo, Alpha Zero ideas 5 DeepMind and UAlberta • UAlberta has deep connections • Faculty who work part-time or on leave at DeepMind • Rich Sutton, Michael Bowling, Patrick Pilarski, Csaba Szepesvari (all part time) • Many of our former students and postdocs work at DeepMind • David Silver - UofA PhD, designer of AlphaGo, lead of the DeepMind RL and AlphaGo teams • Aja Huang - UofA postdoc, main AlphaGo programmer • Many from the computer Poker group
    [Show full text]
  • Computer Wins Second GAME Against Chinese GO Champion
    TECHNOLOGY SATURDAY, MAY 27, 2017 Samsung investigating Galaxy S8 ‘iris hack’ SEOUL: Samsung Electronics is investigating claims by a German hacking group that it fooled the iris recognition system of the new flagship Galaxy S8 device, the firm said yesterday. The launch of the Galaxy S8 was a key step for the world’s largest smartphone maker as it sought to move on from last year’s humiliating withdrawal of the fire-prone Galaxy Note 7s, which hammered the firm’s once-stellar reputation. But a video posted by the Chaos Computer Club (CCC), a German hacking group founded in 1981, shows the Galaxy S8 being unlocked using a printed photo of the owner’s eye covered with a contact lens to replicate the curvature of a real eyeball. “A high- resolution picture from the internet is sufficient to cap- ture an iris,” CCC spokesman Dirk Engling said, adding: “Ironically, we got the best results with laser printers made by Samsung.” A Samsung spokeswoman said it was aware of the report and was investigating. The iris scanning technolo- gy was “developed through rigorous testing”, the firm said in a statement as it sought to reassure customers. “If there is a potential vulnerability or the advent of a new method that challenges our efforts to ensure security at WUZHEN: Chinese Go player Ke Jie, left, looks at the board as a person makes a move on behalf of Google’s artificial any time, we will respond as quickly as possible to resolve intelligence program, AlphaGo, during a game of Go at the Future of Go Summit in Wuzhen in eastern China’s Zhejiang the issue.” Samsung’s hopes of competing against archri- Province.
    [Show full text]
  • Reinforcement Learning and Video Games Arxiv:1909.04751V1 [Cs.LG]
    University of Sheffield Reinforcement Learning and Video Games Yue Zheng Supervisor: Prof. Eleni Vasilaki A report submitted in partial fulfilment of the requirements for the degree of MSc Data Analytics in Computer Science in the arXiv:1909.04751v1 [cs.LG] 10 Sep 2019 Department of Computer Science September 12, 2019 i Declaration All sentences or passages quoted in this document from other people's work have been specif- ically acknowledged by clear cross-referencing to author, work and page(s). Any illustrations that are not the work of the author of this report have been used with the explicit permis- sion of the originator and are specifically acknowledged. I understand that failure to do this amounts to plagiarism and will be considered grounds for failure. Name: Yue Zheng Abstract Reinforcement learning has exceeded human-level performance in game playing AI with deep learning methods according to the experiments from DeepMind on Go and Atari games. Deep learning solves high dimension input problems which stop the development of reinforcement for many years. This study uses both two techniques to create several agents with different algorithms that successfully learn to play T-rex Runner. Deep Q network algorithm and three types of improvements are implemented to train the agent. The results from some of them are far from satisfactory but others are better than human experts. Batch normalization is a method to solve internal covariate shift problems in deep neural network. The positive influence of this on reinforcement learning has also been proved in this study. ii Acknowledgement I would like to express many thanks to my supervisor, Prof.
    [Show full text]
  • AI Beats Go Master, but Google Still Can't Come To
    AI beats Go master, but Google still can’t Come to China 24 May 2017 at 18:56 Demis Hassabis, co-founder of Google's artificial intelligence (AI) startup DeepMind speaks during the AI forum of the Future of Go summit at Wuzhen internet international conference and exhibition centre in Wuzhen, China's Zhejiang province, 24 May 2017. (EPA photo) BEIJING -- A Google artificial intelligence programme defeated a Chinese grand master at the ancient board game Go on Tuesday, a major feather in the cap for the firm's AI ambitions as it looks to woo Beijing to gain re-entry into the country. In the first of three planned games in the eastern water town of Wuzhen, the AlphaGo program held off China's world number one Ke Jie in front of Chinese officials and Google parent Alphabet's chief executive Eric Schmidt. The victory over the world's top player - which many thought would take decades to achieve - underlines the potential of artificial intelligence to take on humans at complex tasks. Wooing Beijing may be less simple. The game streamed live on Google-owned YouTube, while executives from the DeepMind unit that developed the programme sent out updates live on Twitter. Both are blocked by China, as is Google search. Google pulled its search engine from China seven years ago after it refused to self-censor internet searches, a requirement of Beijing. Since then it has been inaccessible behind the country's nationwide firewall. The ceremonial game - the second time AlphaGo has gone head-to-head with a master Go player in a public showdown - represents a major bridge-building exercise for Google in China, following a charm offensive in recent years.
    [Show full text]
  • Google's Alphago Retires on Top After Humbling World No. 1 27 May 2017
    Google's AlphaGo retires on top after humbling world No. 1 27 May 2017 Technologies, the London-based AI company that developed AlphaGo and which Google acquired in 2014. In a blog post, DeepMind founder Demis Hassabis said AlphaGo's developers will turn to "the next set of grand challenges, developing advanced general algorithms that could one day help scientists as they tackle some of our most complex problems". These could include "finding new cures for diseases, dramatically reducing energy consumption, or inventing revolutionary new materials," he said. "If AI systems prove they are able to unearth AlphaGo defeated brash 19-year-old world number one Ke Jie of China on Saturday to sweep a three-game significant new knowledge and strategies in these series that was closely watched as a measure of how far domains too, the breakthroughs could be truly artificial intelligence (AI) has come remarkable. We can't wait to see what comes next." Go involves two players laying black and white stones on a grid, trying to capture the most territory. The Google-owned computer algorithm AlphaGo is Invented nearly 3,000 years ago in China, it is retiring from playing humans in the ancient played worldwide but most popular in East Asia. Chinese game of Go after roundly defeating the world's top player this week, its developer said Top players have been mesmerised by AlphaGo, Saturday. describing its play as groundbreaking and opening up new ways of approaching the game. AlphaGo defeated brash 19-year-old world number one Ke Jie of China on Saturday to sweep a three- Likewise, AlphaGo's feats have fuelled visions of AI game series that was closely watched as a that can not only perform pre-programmed tasks, measure of how far artificial intelligence (AI) has but help humanity look at complex scientific, come.
    [Show full text]
  • February 2021
    European Go Journal February 2021 Professional Games Collection Compiled by Artem Kachanovskyi Event 25th LG Cup, final 1 Date 2021-02-01 Black Shin Minjun (9p) White Ke Jie (9p) Komi 6.5 Result White wins by resignation Diagram 1 (1-50) Diagram 2 (51-100) 28 26 22 14 12 10 54 20 16 6 8 84 18 2 24 82 96 92 78 98 94 80 90 62 76 40 72 44 38 46 74 64 70 58 88 50 30 42 34 68 4 32 36 52 66 56 48 60 100 86 Diagram 3 (101-150) Diagram 4 (151-184) 84 82 76 60 32 72 74 80 54 68 28 66 52 58 50 64 38 34 6 14 56 70 36 30 12 8 10 46 20 44 40 22 42 24 26 4 48 78 2 62 18 16 Event 25th LG Cup, final 2 Date 2021-02-03 Black Ke Jie (9p) White Shin Minjun (9p) Komi 6.5 Result White wins by resignation Diagram 1 (1-50) Diagram 2 (51-100) 50 88 6 46 76 A 32 48 90 34 82 30 40 84 36 44 20 38 86 26 42 94 22 24 92 18 28 16 98 96 12 62 14 56 100 4 8 2 68 10 58 78 80 66 64 60 72 52 70 54 at A at 74 at A Diagram 3 (101-150) Diagram 4 (151-198) 70 80 B 82 30 72 68 66 28 92 78 94 84 96 60 76 6 62 64 58 56 54 52 74 8 10 4 12 14 24 16 20 44 42 32 98 2 46 18 36 86 88 A 40 50 38 48 26 90 22 at A 34 at B Event Year 2020 Korean League, round 11 Date 2021-02-04 Black Cho Hanseung (9p) White Park Junghwan (9p) Komi 6.5 Result White wins by 0.5 Diagram 1 (1-50) Diagram 2 (51-100) 16 6 94 74 78 18 24 22 68 98 100 76 14 80 38 82 84 88 92 70 90 86 96 12 66 20 10 44 46 8 2 36 50 4 62 64 58 60 54 52 30 28 34 26 42 A 56 32 72 at 40 at 34 at 48 at 34 at A Diagram 3 (101-150) Diagram 4 (151-277) 98 100 104 102 38 14 90 110 94 2 52 28 116 150 4 20 140 174 164 108 16 8 170 176 152 162
    [Show full text]
  • (Link to ​Korean Version​) Thirdparty Comments
    English version Korean version (link to Korean version) ​ ​ ​ Thirdparty comments ­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­ FINAL VERSION(PLEASE LET EMILY OR LOIS KNOW FOR FURTHER CHANGE) Release: 9am GMT Monday 22nd February English version Details announced for Google DeepMind Challenge Match Landmark five­match tournament between the Grand Master­defeating Go computer program, AlphaGo, and best human player of the last decade, Lee Sedol, will be held from March 9 to March 15 at the Four Seasons Hotel Seoul. February 22, 2016 (Seoul, South Korea) — Following breakthrough research on AlphaGo, the first ​ ​ ​ computer program to beat a professional human Go player, Google DeepMind has announced details of the ultimate challenge to play the legendary Lee Sedol — the top Go player in the world over the past ​ ​ decade. AlphaGo will play Lee Sedol in a five­game challenge match to be held from Wednesday, March 9 to Tuesday, March 15 in Seoul, South Korea. The games will be even (no handicap), with $1 million USD in prize money for the winner. If AlphaGo wins, the prize money will be donated to UNICEF, STEM and Go charities. Regarded as the outstanding grand challenge for artificial intelligence, Go has been viewed as one of ​ ​ the hardest games for computers to master due to its sheer complexity which makes brute­force exhaustive search intractable (for example there are more possible board configurations than the number of atoms in the universe). DeepMind released details of the breakthrough in a paper published in the scientific journal Nature last month. ​ ​ The matches will be held at the Four Seasons Hotel Seoul, starting at 1pm local time (4am GMT; day ​ ​ before 11pm ET, 8pm PT) on the following days: 1.
    [Show full text]
  • A Machine Learning Playing GO
    A Machine Learning playing GO TUM Data Innovation Lab Sponsored by: Lehrstuhl für Geometrie und Visualisierung Mentor: M.Sc. Bernhard Werner TUM-DI-LAB Coordinator & Project Manager: Dr. Ricardo Acevedo Cabra Head of TUM-DI-LAB: Prof. Dr. Massimo Fornasier Nathanael Bosch, Benjamin Degenhart, Karthikeya Kaushik, Yushan Liu, Benedikt Mairhörmann, Stefan Peidli, Faruk Toy, Patrick Wilson 2016: AlphaGo vs. Lee Sedol www.newscientist.com/article/2079871-im-in-shock-how-an-ai-beat-the-worlds-best-human-at-go/ 2014: “May take another ten years or so” www.wired.com/2014/05/the-world-of-computer-go/ Complexity of Go compared to Chess 10100 = 10,000,000,000,000,000,000,000,000,000,000,000,000,000,00 0,000,000,000,000,000,000,000,000,000,000,000,000,000,000, 000,000,000,000,000. https://research.googleblog.com/2016/01/alphago-mastering-ancient-game-of-go.htm http://scienceblogs.com/startswithabang/2011/11/14/found-the-first-atoms-in-the-u Outline ● Go & Computer Go ● Our Project ● Neural Networks ● Different Approaches & Results ● Learning from AI Go & Computer Go What is Go ● Go is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent. ● Origin: China ~4000 years ago ● Among the most played games in the world ● Top-level international players can become millionaires Images from en.wikipedia.org/wiki/Go_(game) Quotes “The rules of go are so elegant, organic, and rigorously logical that if intelligent life forms exist elsewhere in the universe, they almost certainly play go”.
    [Show full text]
  • SKU Title Subtitle Author Publisher Available Price Sg0001 Ss Zhou Understanding Pro Games Yuan Zhou Slate & Shell 4/11/2011
    SKU Title Subtitle Author Publisher Available Price sg0001_ss_zhou Understanding Pro Games Yuan Zhou Slate & Shell 4/11/2011 $4.99 sg0002_ss_fair Go Seigen's Ten-Game Matches John Fairbairn Slate & Shell 4/11/2011 $4.99 sg0003_nm_ddk Double Digit Kyu Games Neil Moffatt Learn Go Press 4/11/2011 $4.99 sg0004_hi_hane Creating a Thick and Strong Game Naoki Hane Hinoki Press 4/11/2011 $8.99 sg0005_hi_riss Catching Scent of Victory O Rissei Hinoki Press 4/11/2011 $9.99 sg0006_ki_invin Invincible The Games of Shusaku John Power Kiseido 4/27/2012 $19.99 sg0007_ki_intro Go: A Complete Introduction Cho Chikun Kiseido 4/11/2011 $4.99 sg0008_ki_basic Basic Techniques of Go Haruyama & Nagahara Kiseido 4/11/2011 $8.99 sg0009_hi_yama Breakthrough Attacking Power Keigo Yamashita Hinoki Press 4/11/2011 $8.99 sg0010_ki_shuko The Games of Fujisawa Shuko John Power Kiseido 12/22/2011 $14.99 sg0011_mr_san Patterns of the Sanrensei Michael Redmond Nine Dan Press 5/31/2011 $12.99 sg0012_ss_not How Not to Play Go Yuan Zhou Slate & Shell 5/31/2011 $4.99 sg0013_hi_yoda Vital Points and Skillful Finesse for Sabaki Yoda Norimoto Hinoki Press 7/30/2011 $7.99 sg0014_hi_tom1 Shuko - The Only Move Volume 1: Joseki / Fuseki Collection Fujisawa Shuko Hinoki Press 7/30/2011 $9.99 sg0015_hi_tom2 Shuko - The Only Move Volume 2: Middlegame Fighting CollectionFujisawa Shuko Hinoki Press 7/30/2011 $9.99 sg0016_ki_mod1 Modern Master Games, Vol. 1 The Dawn of Tournament Go van Zeijst / Bozulich Kiseido 9/7/2011 $11.99 sg0017_jk_lpg1 Learn to Play Go A Step-By-Step Guide to the
    [Show full text]
  • Learning to Play
    Learning to Play Reinforcement Learning and Games Aske Plaat April 25, 2020 To my students This is a preprint. All your suggestions for improvement to [email protected] are greatly appreciated! © text Aske Plaat, 2020 Illustrations have been attributed to their authors as much as possible This edition: 2020 v3 Contents 1 Introduction 13 1.1 Tuesday March 15, 2016 ....................... 13 1.2 Intended Audience .......................... 15 1.3 Outline ................................ 17 2 Intelligence and Games 21 2.1 Intelligence .............................. 22 2.2 Games of Strategy ........................... 31 2.3 Game Playing Programs ....................... 42 2.4 Summary ............................... 52 3 Reinforcement Learning 55 3.1 Markov Decision Processes ..................... 56 3.2 Policy Function, State-Value Function, Action-Value Function ... 59 3.3 Solution Methods ........................... 61 3.4 Reinforcement Learning and Supervised Learning ......... 71 3.5 Practice ................................ 73 3.6 Summary ............................... 79 4 Heuristic Planning 81 4.1 The Search-Eval Architecture ..................... 82 4.2 State Space Complexity ........................ 91 4.3 Search Enhancements ......................... 95 4.4 Evaluation Enhancements ....................... 111 4.5 Chess Strength, System 1 and System 2 ............... 116 4.6 Practice ................................ 118 4.7 Summary ............................... 121 5 Adaptive Sampling 125 5.1 Monte Carlo Evaluation .......................
    [Show full text]
  • Standortverzeichnis Der Titel
    Sammlung Boltz Geschenk von William G. Boltz Aus dem Nachlass von Judith Magee Boltz (1947-2013) Systematik und Standortverzeichnis der Titel Inhaltsverzeichnis B - Zeitschriften .......................................................................................................................4 A - Indices, Konkordanzen und Bibliografien ......................................................................... 10 A I b - Indices und Konkordanzen ...................................................................................... 10 A II - Bibliografien ............................................................................................................. 12 C - Festschriften, Jahrbücher, Sammelwerke .......................................................................... 16 D - Nachschlagewerke, Karten, Atlanten ................................................................................ 19 E - Wörterbücher ................................................................................................................... 22 E I - Chinesisch/Chinesisch; Japanisch/Japanisch; Chinesisch/Japanisch -> andere Sprachen ........................................................................................................................................ 22 E III - Fachwörterbücher ..................................................................................................... 23 F - Ts’ung-shu / Cong shu ..................................................................................................... 26 G - Texte ..............................................................................................................................
    [Show full text]