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Handout for CYDEF2019@Tokyo, 2019/10/09

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  1. 1. AI & Cyber Defense Session Agenda : 1. Brief overview of the fields 2. Applications in AI & Cyber Defense 3. Issues on AI application 4. Open Mic (C) Takamichi Saito @CYDEF2019 1 A questionnaire & Saitoʼs handout
  2. 2. AI & Cyber Defense Prof. Takamichi Saito, Dept of Computer Science, Meiji University
  3. 3. AI Investment is Growing Fast Globally, the Hague Centre for Strategic Studies(HCSS) [1] estimates tech giants, VC and PE financing, and so on, spent $26 billion to $39 billion on AI in 2016. Some studies estimate this will grow to as high as $126 billion by 2025. [1] Artificial Intelligence, The Next Digital Frontier? (2017, McKinsey Global Institute) AI investment in the U.S. DoD The U.S. Dept. of Defense spent into three segments[2]: learning and intelligence, advanced computing, and AI systems, totaling approximately $1.76B from FY2013 to FY2017. [2] ARTIFICIAL INTELLIGENCE AND NATIONAL SECURITY (2018, CSIS) [3] FY2020 $927M [3] : JAIC+ Project Maven (C) Takamichi Saito @CYDEF2019 3
  4. 4. Definition of AI/ML/DL -beyond rules-base- AI is “the simulation of human intelligence processes by machines.” Machine Learning(ML) is an AI discipline that gives computers the ability to learn without being explicitly programmed. Rule-base It needs to comprehend the problems by a human. ML/DL (a.k.a. Narrow AI) They can find solutions from Big Data without human help. (C) Takamichi Saito @CYDEF2019 4
  5. 5. An Example of ML: Support Vector Machine Support-vector machines (SVM) is supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. (Wiki) Y=aX+b ↑SVM tries to find this. support vector (C) Takamichi Saito @CYDEF2019 5 ML itself is not new, but Big Data and GPU have make AI a big progress.
  6. 6. Deep Neural Network, a.k.a. Deep Learning Deep Learning is a specific class of ML which use multi-layered neural networks. Training: Input `Big dataʼ as for training, with repeated feedbacks Inference: the trained model can judge a new data. Cat × Dog ✔ Monkey × Training Dog ✔ Inference Within training, we will find the adaptive parameters as hidden relationships. Feedback repeatedly (C) Takamichi Saito @CYDEF2019 6
  7. 7. Applications of ML/DL in General Purpose Although ML/DL remain highly problem-specific and context-dependent, they expand our capabilities. ML/DL are good at these solvings [1]: • Anomaly detection : distinguishing normal and abnormal • Classification: identifying or linking an entity to a known entity • Clustering: classifying objects with some groups • Continuous estimation: predicting or forecasting something in the target field • Data generation: generating data, for example, making a Fake picture or movie • Optimization: finding or adapting to fit something to target somethings • Recommendation: suggesting items with finding a personʼs taste NOTICE • No yet! Self-maintenance • No yet! Self-organization(expanding capability) • No yet! Self-consciousness (C) Takamichi Saito @CYDEF2019 7[1] Artificial Intelligence, The Next Digital Frontier? (2017, McKinsey Global Institute) AI singularity ??
  8. 8. Applications of ML/DL in (U.S.) National Security • Intelligence, Surveillance, and Reconnaissance (ISR) • Project Maven: automating intelligence processing in support of the counter-ISIL campaign [4] • OSI(Open Source Indicators): Indo-Pacific Command is using AI to give commanders a better sense of whatʼs happening across the theatre based on all the publicly available information that can be gleaned, structured, and analyzed.[5] • Logistics & Predictive maintenance • ALIS: the Autonomic Logistics Information System for the F-35 • A predictive maintenance system can predict when an aircraft will break: C-5, C-130J , (F-35) • Command and Control • MDC (multidomain command and control): it aims to centralize planning and execution of air, space, cyberspace, sea, and land-based effects. It makes AI tries to speed decision-making and efficiency for battles. • Autonomous Vehicles • Loyal Wingman: it accomplishes tasks for its manned flight lead, such as reacting to electronic threats with jamming or carrying extra weapons. • Lethal Autonomous Weapon Systems (LAWS) • LAWS: it independently identifies, engage and destroy a target with no human interaction. [1] ARTIFICIAL INTELLIGENCE AND NATIONAL SECURITY (2018, CSIS) [2] [3] Artificial Intelligence and National Security (2018, CRS) [4] [5] [1][2][3] (C) Takamichi Saito @CYDEF2019 8
  9. 9. Applications of ML/DL in Cyberspace `An automated response is critical in order to minimize the impact, conduct forensics and to defend effectivelyʼ[1]. ML/DL can provide the followings: Processing Big Data: the security system is fed months of activity logs to identify anomalies and threats. Handling complexity in fine granularity: a substantial reduction in false positive and false negative rates would positively impact cybersecurity operations. Responding quickly: a hacker can infiltrate a system and either steal critical data in less than hours. NOTICE! According to CrowdStrike[2], overall average breakout time: 4 hrs. and 37 min.: • Russia: About 19 minutes • North Korea: About 2.3 hours • China: About four hours • Iran: About five hours • Independent cyber criminals: About 9.5 hours [1] [2] Takamichi Saito @CYDEF2019 9
  10. 10. Applications in AI & Cyber Defense presented by experts
  11. 11. (C) Takamichi Saito @CYDEF2019 11
  12. 12. Issues on AI & Cyber Defense
  13. 13. Issue 1: Adoption still Remains Low [1] (C) Takamichi Saito @CYDEF2019 13 `AI ecosystemʼ, a capable workforce, foundational data practices and structures, computing infrastructures, and a deployment strategy that articulates the applicability of AI to problem sets, is expected. [1] Artificial Intelligence, The Next Digital Frontier? (2017, McKinsey Global Institute)
  14. 14. Two-years `AI journeyʼ in Our Laboratory DL for our researches: browser fingerprinting / finding a vulnerability Early stage: - getting computing resources: GPU - catching up the AI methodologies - prototyping with small group - trial and error Middle stage: - making data ecosystem in the field - developing workforces Last stage: - new challenges with some projects - publishing our results at conference - collaborating with companies (C) Takamichi Saito @CYDEF2019 14
  15. 15. Issue 2: Compliance and Ethics (1) Amazonʼs system taught itself that male candidates were preferable. `Garbage in, garbage outʼ Ethics Transparency Compliance Accountability Bias & Discrimination It pointed out that AI would make some issues in its usage. OCTOBER 10, 2018 (C) Takamichi Saito 15
  16. 16. Issue 2: Compliance and Ethics (2) Compliance and Ethics will make impact against AI developments. There are principles or regulations in AI domain: • Asilomar AI Principles (2017, the Future of Life Institute) • IEEE Ethically Aligned Design ver. 2 (EADv2) • DoD Directive 3000.09., which is related to LAWS. • General Data Protection Regulation (GDPR), which regulates use of private information. (C) Takamichi Saito @CYDEF2019 16
  17. 17. Issue 3: Trust & Transparency Trust & Transparency are need in use of AI • `A spam filter that utilizes machine learning requires less transparency than applications that may result in a risk to lifeʼ [1]. • AI requires transparency or explainability. The U.S. DoD started the research of Explainable AI(XAI). Fig.: Adversary can cause the system to identify a stop sign as a speed limit sign. [2] [1] ARTIFICIAL INTELLIGENCE AND NATIONAL SECURITY (2018, CSIS) [2] Evan Ackerman, “Slight Street Sign Modifications Can Completely Fool Machine Learning Algorithms,” IEEE Spectrum, August 4, 2017 (C) Takamichi Saito @CYDEF2019 17
  18. 18. AI Issues in National Security Workforce: We have to attract and retain highly-skilled/highly-educated people within military organization, although they are scarcity and high value in the market [1]. Data: The combat environments are often less structured and unpredictable. Adversaries thwart/deceive the AI system by manipulating information [1]. We probably need a specifically-tailored version of the technology in military use. Compliance & Ethics: While we have to be limited by legal & ethical implications, the others can develop or use `non-ethicalʼ technologies, such as drone attack. Trust: In the early stages, the performance could be degraded by the introduction of AI. AI capabilities require iterative training. Trust will come only from repeated use in practice [1]. (C) Takamichi Saito @CYDEF2019 18 [1] Artificial Intelligence and National Security (2018, CRS)
  19. 19. Question AI technology can expand our capabilities in many contexts. However, there are some issues in use of AI technology. Which one do you think is needed for AI technology? 1. Workforce a skilled and educated workforce with domain expertise 2. Data ecosystem huge number of training data 3. Compliance & Ethics a lack of clear ethical guidelines results in criticism and confusions 4. Trust & Transparency transparency/accountability, reliable sets of training data, algorithm 5. Other (C) Takamichi Saito @CYDEF2019 19
  20. 20. Answers by experts & floor
  21. 21. (C) Takamichi Saito @CYDEF2019 21
  22. 22. Summary AI will change our society, even in national security. People have prepared AI technologies in some contexts. Now, Ready for the AI era. If we do not ready for the AI era., we would `drop-outʼ from the future AI world. Then, young people will have to battle a competitor by using old technologies. (C) Takamichi Saito @CYDEF2019 22
  23. 23. Disclaimer Any views or opinions represented in this slide are personal and belong solely to the slide owner and do not represent those of people, institutions or organizations that the owner may or may not be associated with in professional or personal capacity, unless explicitly stated. (C) Takamichi Saito @CYDEF2019 23
  24. 24. (C) Takamichi Saito @CYDEF2019 24