These are the sources and citations used to research AI.CS.NIU.2019.June. This bibliography was generated on Cite This For Me on

  • Website

    Anon

    2019

    Your Bibliography: N.p., 2019. Web. 25 Nov. 2019.

  • Govt. publication

    2018 Public-Private Analytic Exchange Program, The Department of Homeland Security, The Office of the Director of National Intelligence

    AI: Using Standards to Mitigate Risks

    2018 - The Department of Homeland Security and The Office of the Director National Intelligence - Washington DC

    In-text: (2018 Public-Private Analytic Exchange Program, The Department of Homeland Security, The Office of the Director of National Intelligence 3-20)

    Your Bibliography: 2018 Public-Private Analytic Exchange Program, The Department of Homeland Security, The Office of the Director of National Intelligence. AI: Using Standards To Mitigate Risks. Washington DC: The Department of Homeland Security and The Office of the Director National Intelligence, 2018. Print.

  • Website

    A Next Generation Artificial Intelligence Development Plan

    2019

    In-text: ("A Next Generation Artificial Intelligence Development Plan")

    Your Bibliography: "A Next Generation Artificial Intelligence Development Plan." China Copyright and Media. N.p., 2019. Web. 28 Feb. 2019.

  • Website

    About Gartner

    2019 - Gartner, Inc.

    Founded in 1979, we are the leading research and advisory company. We’ve expanded well beyond our flagship technology research to provide senior leaders across the enterprise with the indispensable business insights, advice and tools they need to achieve their mission-critical priorities and build the organizations of tomorrow. Together with our clients, we fuel the future of business so that a more successful world takes shape.

    In-text: ("About Gartner")

    Your Bibliography: "About Gartner." Gartner. N.p., 2019. Web. 26 June 2019.

  • Website

    America's Edge

    2009 - Foreign Affairs

    Slaughter, A. (2018, November 13). America's Edge. Retrieved November 13, 2018, from https://www.foreignaffairs.com/articles/united-states/2009-01-01/americas-edge

    In-text: ("America's Edge")

    Your Bibliography: "America's Edge." Foreign Affairs. N.p., 2009. Web. 13 Nov. 2018.

  • Website

    Baram, G.

    The Theft and Reuse of Advanced Offensive Cyber Weapons Pose A Growing Threat

    2018 - ForeignAffairs.com

    For example, in February 2018, security researchers at Symantec reported that an Iran-based hacking group had used EternalBlue as part of its operations. This situation whereby technologically-advanced countries are investing efforts in developing offensive cyber capabilities only to have these very tools stolen and reused raises three critical questions of urgent policy relevance. First, are states going to start reusing each other’s leaked cyber tools as a matter of course? The ability to reuse stolen cyber tools may signal the beginning of a shift in the distribution of international cyber power, as weaker actors (including non-state actors) become increasingly able to use sophisticated malware to cause global damage and possibly target the cyber weapons’ original designers. Countries that are less technologically advanced and less vulnerable to cyberattacks might find the reuse of stolen vulnerabilities appealing for their own offensive activity. Second, is it possible to prevent the leaking of cyber tools from occurring in the first place? There aren't many reasons to be optimistic.

    In-text: (Baram)

    Your Bibliography: Baram, Gil. "The Theft And Reuse Of Advanced Offensive Cyber Weapons Pose A Growing Threat." Council on Foreign Relations. N.p., 2018. Web. 9 Sept. 2018.

  • Book

    Bostrom, N.

    Superintelligence

    2017 - Oxford University Press - Oxford

    In-text: (Bostrom 26)

    Your Bibliography: Bostrom, Nick. Superintelligence. 2nd ed. Oxford: Oxford University Press, 2017. Print.

  • Website

    Boyle, A.

    Microsoft’s chatbot gone bad, Tay, makes MIT’s annual list of biggest technology fails

    2016 - GeekWire

    Tay had its day back in March, when it was touted as a millennial-minded AI agent that could learn more about the world through its conversations with users. It learned about human nature all too well: Mischief-makers fed its artificial mind with cuss words, racism, Nazi sentiments and conspiracy theories. Within 24 hours, Microsoft had to pull Tay offline.

    In-text: (Boyle)

    Your Bibliography: Boyle, Alan. "Microsoft’S Chatbot Gone Bad, Tay, Makes MIT’S Annual List Of Biggest Technology Fails." GeekWire. N.p., 2016. Web. 6 Oct. 2018.

  • Website

    Boyle, A.

    Microsoft’s chatbot gone bad, Tay, makes MIT’s annual list of biggest technology fails

    2016 - GeekWire

    Tay had its day back in March, when it was touted as a millennial-minded AI agent that could learn more about the world through its conversations with users. It learned about human nature all too well: Mischief-makers fed its artificial mind with cuss words, racism, Nazi sentiments and conspiracy theories. Within 24 hours, Microsoft had to pull Tay offline.

    In-text: (Boyle)

    Your Bibliography: Boyle, Alan. "Microsoft’S Chatbot Gone Bad, Tay, Makes MIT’S Annual List Of Biggest Technology Fails." GeekWire. N.p., 2016. Web. 6 Oct. 2018.

  • Book

    Brockman, J. and Anderson, C.

    Possible minds

    2019 - Penguin Press - New York

    In-text: (Brockman and Anderson 148-150)

    Your Bibliography: Brockman, John, and Chris Anderson. Possible Minds. 1st ed. New York: Penguin Press, 2019. Print.

  • Journal

    Buczak, A. and Guven, E.

    A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrustion Detection

    2016 - IEEE Communications Surveys & Tutorials

    In-text: (Buczak and Guven 1153-1158)

    Your Bibliography: Buczak, Anna, and Erhan Guven. "A Survey Of Data Mining And Machine Learning Methods For Cyber Security Intrustion Detection." IEEE Communications Surveys & Tutorials 18.2 (2016): 1153-1158. Web. 7 Oct. 2018.

  • Journal

    CARR, M.

    Public-private partnerships in national cyber-security strategies

    2016 - International Affairs

    In-text: (CARR 43-62)

    Your Bibliography: CARR, MADELINE. "Public-Private Partnerships In National Cyber-Security Strategies." International Affairs 92.1 (2016): 43-62. Web.

  • Book

    Chapple, M. and Seidl, D.

    Cyberwarfare

    2015 - Jones & Bartlett Learning - Burlington, MA

    In-text: (Chapple and Seidl 112-120)

    Your Bibliography: Chapple, Mike, and David Seidl. Cyberwarfare. 1st ed. Burlington, MA: Jones & Bartlett Learning, 2015. Print.

  • Website

    Chinese battery expert is charged with stealing trade secrets from US employer

    2018 - South China Morning Post website

    Assistant US Attorney General for National Security John Demers said: “The theft of intellectual property harms American companies and American workers. As our recent cases show, all too often these thefts involve the Chinese government or Chinese companies. The theft of intellectual property harms American companies and American workers. As our recent cases show, all too often these thefts involve the Chinese government or Chinese companies. Tan, an engineer with a physics degree from Nanjing University and a doctorate from the California Institute of Technology, has lived in the United States as a legal resident for the past 12 years, according to the Department of Justice.

    In-text: ("Chinese Battery Expert Is Charged With Stealing Trade Secrets From US Employer")

    Your Bibliography: "Chinese Battery Expert Is Charged With Stealing Trade Secrets From US Employer." South China Morning Post. N.p., 2018. Web. 2 Feb. 2019.

  • Website

    Cisco 2018 Annual Cybersecurity Report: The Evolution of Malware

    2018 - Cisco Corporation

    In-text: ("Cisco 2018 Annual Cybersecurity Report: The Evolution Of Malware")

    Your Bibliography: "Cisco 2018 Annual Cybersecurity Report: The Evolution Of Malware." Cisco.com. N.p., 2018. Web. 9 June 2019.

  • Online image or video

    CNBC

    Cyber Espionage: The Chinese Threat

    2012

    In-text: (CNBC)

    Your Bibliography: CNBC. Cyber Espionage: The Chinese Threat. 2012. Web. 4 Mar. 2019.

  • Website

    Committee on National Security Systems (CNSS) Glossary

    2019 - Committee on National Security Systems

    computer network exploitation (CNE): Enabling operations and intelligence collection capabilities conducted through the use of computer networks to gather data from target or adversary information systems or networks. Note: Within the Department of Defense (DoD), term was approved for deletion from JP 1-02 (DoD Dictionary). Original source of term was JP 1-02 (DoD Dictionary). The military no longer uses this term to describe these operations, but it is still used outside of military operations.

    In-text: ("Committee On National Security Systems (CNSS) Glossary")

    Your Bibliography: "Committee On National Security Systems (CNSS) Glossary." https://www.cnss.gov/. N.p., 2019. Web. 12 Aug. 2019.

  • Website

    computer network exploitation (CNE) - Glossary | Computer Security Resource Center

    2019 - National Institute of Standards and Testing

    Enabling operations and intelligence collection capabilities conducted through the use of computer networks to gather data from target or adversary information systems or networks. Note: Within the Department of Defense (DoD), term was approved for deletion from JP 1-02 (DoD Dictionary). Original source of term was JP 1-02 (DoD Dictionary). The military no longer uses this term to describe these operations, but it is still used outside of military operations.

    In-text: ("Computer Network Exploitation (CNE) - Glossary | Computer Security Resource Center")

    Your Bibliography: "Computer Network Exploitation (CNE) - Glossary | Computer Security Resource Center." Csrc.nist.gov. N.p., 2019. Web. 19 Sept. 2019.

  • Chapter of an ed. book

    Cooper, J. R.

    A New Framework for Cyber Deterrence

    2012 - Georgetown University Press - Washington DC

    In-text: (Cooper 105-118)

    Your Bibliography: Cooper, Jeffery R. "A New Framework For Cyber Deterrence." Cyberspace And National Security. Derek S. Reveron. 1st ed. Washington DC: Georgetown University Press, 2012. 105-118. Print.

  • Blog

    Costa, D.

    CERT Definition of 'Insider Threat' - Updated

    2017 - Carnegie Mellow University Software Engineering Institute Blogs

    Many definitions of insider threat exist, but we could not find one among them that met the above criteria, so we decided to build our own definition. We started with our definition of insider threat from the CERT Guide to Insider Threats: A malicious insider threat is a current or former employee, contractor, or business partner who has or had authorized access to an organization's network, system, or data and intentionally exceeded or misused that access in a manner that negatively affected the confidentiality, integrity, or availability of the organization's information or information systems.

    In-text: (Costa)

    Your Bibliography: Costa, Daniel. "CERT Definition Of 'Insider Threat' - Updated." Carnegie Mellow University Software Engineering Institute Blogs. N.p., 2017. Web. 7 Aug. 2019.

  • Book

    Creswell, J. W.

    Research design

    2014 - Sage - Los Angeles

    In-text: (Creswell 3-22)

    Your Bibliography: Creswell, John W. Research Design. 4th ed. Los Angeles: Sage, 2014. Print.

  • Website

    Crowe, J.

    GE Engineer Steals Trade Secrets | National Review

    2018 - Founder William F. Buckley's National Review

    A Chinese engineer at General Electric (GE) was arrested this week for attempting to steal files detailing proprietary wind-turbine technology from his employer. Xiaoqing Zheng, a U.S. citizen who has worked in GE’s Schenectady, N.Y. power division since 2008, allegedly used his position at the company to steal trade secrets, according to an affidavit filed by the FBI Wednesday and obtained by the Wall Street Journal. Zheng, who also owns an energy technology company in China and holds senior positions at a number of other Chinese firms, encrypted the proprietary files within a photo of a sunset, which he sent to his personal email address, according to the affidavit. A search of Zheng’s house yielded a handbook that details “the type of resources the government of China will give to individuals or entities who can provide certain technologies,” according to the affidavit.

    In-text: (Crowe)

    Your Bibliography: Crowe, Jack. "GE Engineer Steals Trade Secrets | National Review." Nationalreview.com. N.p., 2018. Web. 2 Feb. 2019.

  • Website

    Cyber Attack - Glossary | Computer Security Resource Center

    2019 - National Institute of Standards and Technology

     An attack, via cyberspace, targeting an enterprise’s use of cyberspace for the purpose of disrupting, disabling, destroying, or maliciously controlling a computing environment/infrastructure; or destroying the integrity of the data or stealing controlled information.

    In-text: ("Cyber Attack - Glossary | Computer Security Resource Center")

    Your Bibliography: "Cyber Attack - Glossary | Computer Security Resource Center." Csrc.nist.gov. N.p., 2019. Web. 19 Sept. 2019.

  • Website

    Cyber Attack - Glossary | CSRC

    2019 - National Institute of Standards and Technology

    In-text: ("Cyber Attack - Glossary | CSRC")

    Your Bibliography: "Cyber Attack - Glossary | CSRC." Csrc.nist.gov. N.p., 2019. Web. 19 Sept. 2019.

  • Magazine

    Deception technologies, AI and Robo Hunters to displace legacy cybersecurity solutions

    2018 - CommsMEA, Infotrac Newsstand

    Deception technologies create thousands of fake, user credential in conjunction with real user-identities. Once a threat actor is inside an organisations' network, they are unable to distinguish between real and fake user identity credentials. Since there are many more fake user identity credentials distributed, the probability of engaging with a fake user identity credential and triggering an intrusion alert is much higher. Afterwards an incident response alert and action are then initiated. The large number of fake credentials generated through deception technologies also facilitate pattern tracking. This allows internal teams to recreate the pattern of attack and point of entry.

    In-text: ("Deception Technologies, AI And Robo Hunters To Displace Legacy Cybersecurity Solutions")

    Your Bibliography: "Deception Technologies, AI And Robo Hunters To Displace Legacy Cybersecurity Solutions." CommsMEA, Infotrac Newsstand 2018. Web. 3 Oct. 2018.

  • Journal

    Duddu, V.

    A Survey of Adversarial Machine Learning in Cyber Warfare

    2018 - Defence Science Journal

    In-text: (Duddu 356-366)

    Your Bibliography: Duddu, Vasisht. "A Survey Of Adversarial Machine Learning In Cyber Warfare." Defence Science Journal 68.4 (2018): 356-366. Web.

  • Website

    Executive Order -- Improving Critical Infrastructure Cybersecurity

    2013 - The White House

    By the authority vested in me as President by the Constitution and the laws of the United States of America, it is hereby ordered as follows: Section 1. Policy. Repeated cyber intrusions into critical infrastructure demonstrate the need for improved cybersecurity. The cyber threat to critical infrastructure continues to grow and represents one of the most serious national security challenges we must confront. The national and economic security of the United States depends on the reliable functioning of the Nation's critical infrastructure in the face of such threats. It is the policy of the United States to enhance the security and resilience of the Nation's critical infrastructure and to maintain a cyber environment that encourages efficiency, innovation, and economic prosperity while promoting safety, security, business confidentiality, privacy, and civil liberties. We can achieve these goals through a partnership with the owners and operators of critical infrastructure to improve cybersecurity information sharing and collaboratively develop and implement risk-based standards.

    In-text: ("Executive Order -- Improving Critical Infrastructure Cybersecurity")

    Your Bibliography: "Executive Order -- Improving Critical Infrastructure Cybersecurity." whitehouse.gov. N.p., 2013. Web. 24 Nov. 2019.

  • Journal

    Fisher, S.

    Applying deep learning to cybersecurity: why a generic approach just isn't enough.

    2018 - Enterprise Innovation

    In-text: (Fisher)

    Your Bibliography: Fisher, Stuart. "Applying Deep Learning To Cybersecurity: Why A Generic Approach Just Isn't Enough.." Enterprise Innovation (2018): n. pag. Web. 5 Oct. 2018.

  • Website

    Framework for Improving Critical Infrastructure Cybersecurity

    2014 - National Institute of Standards and Technolog

    In-text: ("Framework For Improving Critical Infrastructure Cybersecurity")

    Your Bibliography: "Framework For Improving Critical Infrastructure Cybersecurity." Nist.gov. N.p., 2014. Web. 24 Nov. 2019.

  • Website

    Garrett, G.

    Cyberattacks Skyrocketed in 2018. Are You Ready for 2019?

    2018 - Industry Week

    During 2018, we have seen a 350% increase in ransomware attacks, a 250% increase in spoofing or business email compromise (BEC) attacks and a 70% increase in spear-phishing attacks in companies overall. Further, the average cost of a cyber-data breach has risen from $4.9 million in 2017 to $7.5 million in 2018, according to the U.S. Securities and Exchange Commission. Risks have grown significantly around cyberattacks, information breaches from third-party vendors and information theft (i.e., personal identifiable information, intellectual property and trade secrets). Gregory A. Garrett is head of U.S. and International Cybersecurity for BDO audit, tax and advisory firm.

    In-text: (Garrett)

    Your Bibliography: Garrett, Gregory. "Cyberattacks Skyrocketed In 2018. Are You Ready For 2019?." IndustryWeek. N.p., 2018. Web. 24 Nov. 2019.

  • Report

    Gartner, Inc. Licensed for Distribution

    The Road to AI - A Journey to Smarter Security and Risk Decision Making

    2018 - Gartner, Inc.

    In-text: (Gartner, Inc. Licensed for Distribution 1-17)

    Your Bibliography: Gartner, Inc. Licensed for Distribution. The Road To AI - A Journey To Smarter Security And Risk Decision Making. Gartner, Inc., 2018. Print. Market Insight.

  • Report

    Gartner

    Market Insight: The Road to AI - A Journey to Smarter Security and Risk Decision Making

    2018 - Gartner - Licensed for Distribution

    In-text: (Gartner 1-15)

    Your Bibliography: Gartner. Market Insight: The Road To AI - A Journey To Smarter Security And Risk Decision Making. Licensed for Distribution: Gartner, 2018. Web. 1 Dec. 2018. ID G00355788.

  • Report

    Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y.

    Generative Adversarial Nets

    2014 - Universite de Montreal - Montreal

    In-text: (Goodfellow et al. 1-3)

    Your Bibliography: Goodfellow, Ian J. et al. Generative Adversarial Nets. Montreal: Universite de Montreal, 2014. Web. 9 June 2019. The Paper Appears In Advances In Neural Information Processing Systems 27 Edited By Z. Ghahramani And M. Welling And C. Cortes And N.D. Lawrence And K.Q. Weinberger. They Are Proceedings From The Conference, "Neural Information Processing Systems 2014.."

  • Journal

    Grant, T. J., Aschmann, M. J., Greiman, V. and Hurley, J.

    Specifying Functional Requirements for Simulating Professional Offensive Cyber Operations

    2015 - Journal of Information Warfare

    In-text: (Grant et al. 43-56)

    Your Bibliography: Grant, Tim J. et al. "Specifying Functional Requirements For Simulating Professional Offensive Cyber Operations." Journal of Information Warfare 14.3 (2015): 43-56. Print.

  • Journal

    Greengard, S.

    Cybersecurity Gets Smart

    2016 - Communications of the ACM

    In-text: (Greengard 29-31)

    Your Bibliography: Greengard, Samuel. "Cybersecurity Gets Smart." Communications of the ACM 59.5 (2016): 29-31. Print.

  • Journal

    Hamilton, S. P. and Kreuzer, M. P.

    The Big Data Imperative: Air Force Intelligence for the Information Age

    2018 - Air & Space Power Journal

    The explosion of GEOINT sensors and collection capabilities introduces another significant challenge to effective analytics without the aid of big data solutions. The variety of data information collected in various graphics formats is “undiscoverable” to analysts, or what is sometimes characterized as dark data. Exploited GEOINT generally has textual summaries that can be searched, through queries similar to a Google image search, but absent text to cue the analyst, the relevant imagery may remain buried and undiscoverable in data archives. Big-data algorithms and automated 10 | Air & Space Power Journal Hamilton & Kreuzer exploitation templates can allow all images, in NRT, to be tied to geographic coordinates, aligned to known locations, and automatically archived in searchable layered databases with related images over time.

    In-text: (Hamilton and Kreuzer 9, 4-20)

    Your Bibliography: Hamilton, Shane P., and Michael P. Kreuzer. "The Big Data Imperative: Air Force Intelligence For The Information Age." Air & Space Power Journal Spring (2018): 9, 4-20. Print.

  • Website

    Janssen, D. and Janssen, C.

    What is a Multilayer Perceptron (MLP)? - Definition from Techopedia

    2019 - Techopedia is part of the Janalta Interactive network of sites, which focuses on creating highly insightful and actionable content within niche industry verticals.

    In-text: (Janssen and Janssen)

    Your Bibliography: Janssen, Dale, and Cory Janssen. "What Is A Multilayer Perceptron (MLP)? - Definition From Techopedia." Techopedia.com. N.p., 2019. Web. 23 June 2019.

  • Website

    Johnson, D. B.

    DHS awards contract for AI-enabled CDM dashboard -- GCN

    2019 - Public Sector 360 (formerly known as the 1105 Public Sector Media Group)

    In-text: (Johnson)

    Your Bibliography: Johnson, Derek B. "DHS Awards Contract For AI-Enabled CDM Dashboard -- GCN." GCN. N.p., 2019. Web. 5 Aug. 2019.

  • Website

    Keitz, A.

    GE Engineer With Ties to China Accused of Stealing Power Plant Technology

    2019 - The Street website

    Zheng, who is a U.S. citizen, was hired by GE in 2008 to work as a principal engineer for the company's power division, according to an affidavit by an FBI agent filed in federal court in Albany. Zheng is "suspected of taking/stealing, on multiple occasions via sophisticated means, data files from GE's laboratories that contain GE's trade secret information involving turbine technology," the FBI said in its affidavit. He also took "elaborate means" to conceal the removal of GE data files. "The primary focus of this affidavit is Zheng's action in 2018 in which he encrypted GE data files containing trade secret information, and thereafter sent the trade secret information from his GE work computer to Zheng's personal e-mail address hidden in the binary code of a digital photograph via a process known as steganography," the FBI said. "Additionally, the secondary focus of this affidavit is Zheng's actions in 2014 in which he downloaded more than 19,000 files from GE's computer network onto an external storage device, believed by GE investigators to have been a personal thumb drive."

    In-text: (Keitz)

    Your Bibliography: Keitz, Anders. "GE Engineer With Ties To China Accused Of Stealing Power Plant Technology." TheStreet. N.p., 2019. Web. 2 Feb. 2019.

  • Website

    Krizhevsky, A.

    Learning Multiple Layers of Featurs from Tiny Images

    2009 - Alex Krizhevsky, At Google in Mountain View, California.

    Description taken from website: https://www.cs.toronto.edu/~kriz/cifar.html The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

    In-text: (Krizhevsky)

    Your Bibliography: Krizhevsky, Alex. "Learning Multiple Layers Of Featurs From Tiny Images." https;//www.cs.toronto.edu. N.p., 2009. Web. 21 Oct. 2019.

  • Report

    Krizhevsky, A.

    Learning Multiple Layers of Features from Tiny Images

    2009

    In-text: (Krizhevsky)

    Your Bibliography: Krizhevsky, Alex. Learning Multiple Layers Of Features From Tiny Images. 2009. Web. 25 Nov. 2019.

  • Journal

    Krombholz, K., Hobel, H., Huber, M. and Weippl, E.

    Advanced socail engineering attacks

    2014 - Journal of Information Security and Applications

    Social engineering has emerged as a serious threat in virtual communities and is an effective means to attack information systems. The services used by today's knowledge workers prepare the ground for sophisticated social engineering attacks. The growing trend towards BYOD (bring your own device) policies and the use of online communication and collaboration tools in private and business environments aggravate the problem. In globally acting companies, teams are no longer geographically co-located, but staffed just-in-time. The decrease in personal interaction combined with a plethora of tools used for communication (e-mail, IM, Skype, Dropbox, LinkedIn, Lync, etc.) create new attack vectors for social engineering attacks. Recent attacks on companies such as the New York Times and RSA have shown that targeted spear-phishing attacks are an effective, evolutionary step of social engineering attacks. Combined with zero-day-exploits, they become a dangerous weapon that is often used by advanced persistent threats. This paper provides a taxonomy of well-known social engineering attacks as well as a comprehensive overview of advanced social engineering attacks on the knowledge worker.

    In-text: (Krombholz et al. 114-121)

    Your Bibliography: Krombholz, Katharina et al. "Advanced Socail Engineering Attacks." Journal of Information Security and Applications 22.June 2015 (2014): 114-121. Print.

  • Journal

    Kwon, H., Kim, Y., Yoon, H. and Choi, D.

    Random Untargeted Adversarial Example on Deep Neural Network

    2018 - Symmetry

    In-text: (Kwon et al. 738)

    Your Bibliography: Kwon, Hyun et al. "Random Untargeted Adversarial Example On Deep Neural Network." Symmetry 10.12 (2018): 738. Web. 10 Oct. 2019.

  • Journal

    Kwon, H., Kim, Y., Yoon, H. and Choi, D.

    Random Untargeted Adversarial Example on Deep Neural Network

    2018 - Symmetry

    In-text: (Kwon et al. 738)

    Your Bibliography: Kwon, Hyun et al. "Random Untargeted Adversarial Example On Deep Neural Network." Symmetry 10.12 (2018): 738. Web. 13 Oct. 2019.

  • Website

    Lang, T.

    An Overview of Four Futures Methodologies

    2000

    In-text: (Lang)

    Your Bibliography: Lang, Trudi. "An Overview Of Four Futures Methodologies." Semanticscholar.org. N.p., 2000. Web. 11 Nov. 2018.

  • Website

    Lee, B. and Falcone, R.

    OilRig Targets Technology Service Provider and Government Agency with QUADAGENT - Palo Alto Networks Blog

    2018 - Palo Alto Networks

    13,703 (4) The OilRig group continues to adapt their tactics and bolster their toolset with newly developed tools. The OilRig group (AKA APT34, Helix Kitten) is an adversary motivated by espionage primarily operating in the Middle East region. We first discovered this group in mid-2016, although it is possible their operations extends earlier than that time frame. They have shown themselves to be an extremely persistent adversary that shows no signs of slowing down. Examining their past behaviors with current events only seems to indicate that the OilRig group’s operations are likely to accelerate even further in the near future. Between May and June 2018, Unit 42 observed multiple attacks by the OilRig group appearing to originate from a government agency in the Middle East. Based on previously observed tactics, it is highly likely the OilRig group leveraged credential harvesting and compromised accounts to use the government agency as a launching platform for their true attacks.

    In-text: (Lee and Falcone)

    Your Bibliography: Lee, Bryan, and Robert Falcone. "Oilrig Targets Technology Service Provider And Government Agency With QUADAGENT - Palo Alto Networks Blog." Palo Alto Networks Blog. N.p., 2018. Web. 15 Sept. 2018.

  • Book

    Lee, K.

    AI Superpowers: China, Silicon Valley, and the New World Order

    2018 - Houghton Mifflin Harcourt Publishing Company - New York, NY

    In-text: (Lee)

    Your Bibliography: Lee, Kai-Fu. AI Superpowers: China, Silicon Valley, And The New World Order. New York, NY: Houghton Mifflin Harcourt Publishing Company, 2018. Print.

  • Book

    Lowther, A.

    Deterrence

    2012 - Palgrave Macmillan - New York, NY

    In-text: (Lowther 39-43)

    Your Bibliography: Lowther, Adam. Deterrence. 1st ed. New York, NY: Palgrave Macmillan, 2012. Print.

  • Journal

    Manheim, K. and Kaplan, L.

    Artificial Intelligence: Risks to Privacy and Democracy

    2019 - Yale Journal of Law & Technology

    A particularly effective instance of fake news is called "deepfakes," which is audio or video that has been fabricated or altered to deceive our senses. (208) While "Photoshop" has long been a verb as well as a graphics program, AI takes the deception to a whole new level. Consider the program FakeApp, which allows users to alter faces into videos. (209) It is popularly used for celebrity faceswapping pornography and having politicians appear to say humorous or outrageous things. (210) Generative adversarial networks (GANs) take this one step further, by playing one network against another in generating or spotting fake images. In such cases, "[t]he AI trying to detect fakery always loses." (211) Problems of fake news will get much worse as these tools become commonplace. Large-scale unsupervised algorithms can now produce synthetic text of unprecedented quality, (212) which have the potential to further blur the line between reality and fakery. With that in mind, the developer of one such product has declined to publically release the code "[d]ue to our concerns about malicious applications of the technology." (213) The risks are not overstated. As one article warned, "imagine a future where ... a fake video of a president incites a riot or fells the market." (214) Or as The Atlantic's Franklin Foer puts it, "We'll shortly live in a world where our eyes routinely deceive us. Put differently, we're not so far from the collapse of reality." (215) Brian Resnick of Vox is even more pessimistic. "[I]t's not just our present and future reality that could collapse; it's also our past. Fake media could manipulate what we remember, effectively altering the past by seeding the population with false memories." (216) Humans are susceptible to such distortions of reality. (217) An old Russian proverb may soon come true: "the most difficult thing to predict is not the future, but the past." (218) "The collapse of reality isn't an unintended consequence of artificial intelligence. It's long been an objective - or at least a dalliance -- of some of technology's most storied architects" argues Franklin Foer. (219) Unplugging reality is also the domain of Virtual Reality (VR) and Augmented Reality (AR) technologies. We've come to appreciate these as enhancing gaming experiences and entertainment. Will we also appreciate them as they distort democracy and individual rights? However, AI could also help provide potential solutions to the challenge of fake news. Fact checking organizations such as Politifact go after the most potent falsehoods, but so much fake news abounds that fact checking has become its own industry with its own set of standards and principles.

    In-text: (Manheim and Kaplan 10)

    Your Bibliography: Manheim, Karl, and Lyric Kaplan. "Artificial Intelligence: Risks To Privacy And Democracy." Yale Journal of Law & Technology 21.1 (2019): 10. Print.

  • Journal

    Marshall, J. B. and Saulawa, M. A.

    Cyber-Attacks: The Legal Response

    2019 - International Journal of International Law

    In-text: (Marshall and Saulawa 4-8)

    Your Bibliography: Marshall, Junaidu Bello, and Mua'zu Abdullahi Saulawa. "Cyber-Attacks: The Legal Response." International Journal of International Law 1.2 (2019): 4-8. Print.

  • Website

    MLPerf

    2019

    MLPerf began in February 2018 with a series of meetings between engineers and researchers from Baidu, Google, Harvard University, Stanford University, and the University of California Berkeley. MLPerf launched the Training benchmark suite on May 2nd, 2018 and published the first Training results, including results from Google, Intel, and NVIDIA, on December 12, 2018. MLPerf launched the Inference benchmark suite on June 24th, 2019.

    In-text: ("Mlperf")

    Your Bibliography: "Mlperf." MLPerf. N.p., 2019. Web. 7 July 2019.

  • Website

    MNIST

    2019 - DeepAI

    The MNIST database, an extension of the NIST database, is a low-complexity data collection of handwritten digits used to train and test various supervised machine learning algorithms. The database contains 70,000 28x28 black and white images representing the digits zero through nine. The data is split into two subsets, with 60,000 images belonging to the training set and 10,000 images belonging to the testing set. The separation of images ensures that given what an adequately trained model has learned previously, it can accurately classify relevant images not previously examined.

    In-text: ("MNIST")

    Your Bibliography: "MNIST." DeepAI. N.p., 2019. Web. 25 Nov. 2019.

  • Website

    MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges

    2019

    The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The digit images in the MNIST set were originally selected and experimented with by Chris Burges and Corinna Cortes using bounding-box normalization and centering. Yann LeCun's version which is provided on this page uses centering by center of mass within in a larger window. Yann LeCun, Professor The Courant Institute of Mathematical Sciences New York University Corinna Cortes, Research Scientist Google Labs, New York corinna at google dot com

    In-text: ("MNIST Handwritten Digit Database, Yann Lecun, Corinna Cortes And Chris Burges")

    Your Bibliography: "MNIST Handwritten Digit Database, Yann Lecun, Corinna Cortes And Chris Burges." Yann.lecun.com. N.p., 2019. Web. 21 Oct. 2019.

  • Chapter of an ed. book

    Muller, V. C. and Bostrom, N.

    Future Progress in Artificial Intelligence: A Survey of Expert Opinion

    2016 - Springer - Berlin

    In-text: (Muller and Bostrom 553-571)

    Your Bibliography: Muller, Vincent C., and Nick Bostrom. "Future Progress In Artificial Intelligence: A Survey Of Expert Opinion." Fundamental Issues Of Artificial Intelligence. Vincent C. Muller. 1st ed. Berlin: Springer, 2016. 553-571. Print.

  • Website

    Nicholas, J. M. and Steyn, H.

    Functional Requirement - an overview | ScienceDirect Topics

    2012

    Project Management for Engineering, Business, and Technology Book • 4th Edition • 2012

    In-text: (Nicholas and Steyn)

    Your Bibliography: Nicholas, John M., and Herman Steyn. "Functional Requirement - An Overview | Sciencedirect Topics." Sciencedirect.com. N.p., 2012. Web. 20 Nov. 2019.

  • Website

    Niskayuna Man Charged With Theft of Trade Secrets

    2018 - The Department of Justice, U.S. Attorney’s Office, Northern District of New York

    The criminal complaint alleges that on or about July 5, Zheng, an engineer employed by General Electric, used an elaborate and sophisticated means to remove electronic files containing GE’s trade secrets involving its turbine technologies. Specifically, Zheng is alleged to have used steganography to hide data files belonging to GE into an innocuous looking digital picture of a sunset, and then to have e-mailed the digital picture, which contained the stolen GE data files, to Zheng’s e-mail account.

    In-text: ("Niskayuna Man Charged With Theft Of Trade Secrets")

    Your Bibliography: "Niskayuna Man Charged With Theft Of Trade Secrets." Justice.gov. N.p., 2018. Web. 4 Mar. 2019.

  • Website

    NIST Releases SHA-3 Cryptographic Hash Standard

    2015 - National Institute of Standards and Technology (NIST)

    In-text: ("NIST Releases SHA-3 Cryptographic Hash Standard")

    Your Bibliography: "NIST Releases SHA-3 Cryptographic Hash Standard." National Institute of Standards and Technology (NIST). N.p., 2015. Web. 9 Mar. 2019.

  • Website

    Novet, J.

    One of Google's top A.I. people has joined Apple

    2019 - CNBC

    In-text: (Novet)

    Your Bibliography: Novet, Jordan. "One Of Google's Top A.I. People Has Joined Apple." CNBC. N.p., 2019. Web. 25 Nov. 2019.

  • Magazine

    Noyes, K.

    AI + humans = kick-ass cybersecurity

    2016 - CIO

    In-text: (Noyes 1)

    Your Bibliography: Noyes, Katherine. "AI + Humans = Kick-Ass Cybersecurity." CIO 2016: 1. Print.

  • Website

    Nyotron Discovers Next-Generation OilRig Attacks | Nyotron

    2018 - Nyotron, headquartered in Santa Clara, CA with an R&D office in Israel.

    Since 2015, the notorious Iran-linked APT group that launched OilRig has compromised critical infrastructure, banks, airlines, and government entities in countries such as Saudi Arabia, Qatar, United Arab Emirates, Turkey, Kuwait, Israel, Lebanon and the United States. In November 2017, Nyotron discovered new active OilRig attacks on a number of organizations across the Middle East. The OilRig group has significantly evolved its tactics, techniques and procedures, introduced next-generation malware tools and new data exfiltration methods. In total, the attackers used about 20 different tools – some were off-the-shelf, dual-purpose utilities, while others were previously unseen malware using Google Drive and SmartFile as well as the ISAPI filter for compromising IIS servers. Among key advancements, the new variant of OilRig introduces a variety of new command and control (C&C) and data exfiltration capabilities: …….

    In-text: ("Nyotron Discovers Next-Generation Oilrig Attacks | Nyotron")

    Your Bibliography: "Nyotron Discovers Next-Generation Oilrig Attacks | Nyotron." Nyotron. N.p., 2018. Web. 15 Sept. 2018.

  • Govt. publication

    Office of Director of National Intelligence

    National Intelligence Strategy of the United States of America

    2019 - Office of Director of National Intelligence - Washington DC

    This National Intelligence Strategy (NIS) provides the Intelligence Community (IC) with strategic direction from the Director of National Intelligence (DNI) for the next four years. It supports the national security priorities outlined in the National Security Strategy as well as other national strategies. In executing the NIS, all IC activities must be responsive to national security priorities and must comply with the Constitution, applicable laws and statutes, and Congressional oversight requirements.

    In-text: (Office of Director of National Intelligence 3-5)

    Your Bibliography: Office of Director of National Intelligence. National Intelligence Strategy Of The United States Of America. Washington DC: Office of Director of National Intelligence, 2019. Print.

  • Govt. publication

    Office of the Director of National Intelligence

    The AIM Initiative: A Strategy for Augmenting Intelligence Using Machines

    2019 - Office of the Director of National Intelligence - Washington DC

    In-text: (Office of the Director of National Intelligence)

    Your Bibliography: Office of the Director of National Intelligence. The AIM Initiative: A Strategy For Augmenting Intelligence Using Machines. Washington DC: Office of the Director of National Intelligence, 2019. Print.

  • Website

    ORDER OF MAGNITUDE | definition in the Cambridge English Dictionary

    2019 - Cambridge University Press

    In-text: ("ORDER OF MAGNITUDE | Definition In The Cambridge English Dictionary")

    Your Bibliography: "ORDER OF MAGNITUDE | Definition In The Cambridge English Dictionary." Dictionary.cambridge.org. N.p., 2019. Web. 6 Aug. 2019.

  • Book

    Paar, C. and Pelzl, J.

    Understanding cryptography

    2011 - Springer - Berlin

    In-text: (Paar and Pelzl 174-179)

    Your Bibliography: Paar, Christof, and Jan Pelzl. Understanding Cryptography. 2nd ed. Berlin: Springer, 2011. Print.

  • Journal

    Papernot, N., McDaniel, P., Wu, X., Jha, S. and Swami, A.

    Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

    2016 - 2016 IEEE Symposium on Security and Privacy (SP)

    In-text: (Papernot et al. 582-597)

    Your Bibliography: Papernot, Nicolas et al. "Distillation As A Defense To Adversarial Perturbations Against Deep Neural Networks." 2016 IEEE Symposium on Security and Privacy (SP) (2016): 582-597. Web. 14 Oct. 2019.

  • Website

    Puget, J.

    What Is Machine Learning? (IT Best Kept Secret Is Optimization)

    2016 - IBM Community Blog

    The first category of answer to the question is what IBM calls cognitive computing. It is about building machines (computers, software, robots, web sites, mobile apps, devices, etc) that do not need to be programmed explicitly. This view of machine learning can be traced back to Arthur Samuel's definition from 1959: Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.

    In-text: (Puget)

    Your Bibliography: Puget, Jean. "What Is Machine Learning? (IT Best Kept Secret Is Optimization)." Ibm.com. N.p., 2016. Web. 14 Oct. 2018.

  • Journal

    Ravishankar, M., Rao, D. V. and Kumar, C. R. S.

    A Game Theoretic Software Test-bed for Cyber Security Analysis of Critical Infrastructure

    2017 - Defence Science Journal

    In-text: (Ravishankar, Rao and Kumar 54)

    Your Bibliography: Ravishankar, Monica, D. Vijay Rao, and C. R. S. Kumar. "A Game Theoretic Software Test-Bed For Cyber Security Analysis Of Critical Infrastructure." Defence Science Journal 68.1 (2017): 54. Web. 17 Oct. 2019.

  • Journal

    Reith, M.

    Brandishing Our Air, Space, and Cyber Swords, Recommendations for Deterrence and Beyond

    2017 - Air & Space Power Journal

    In-text: (Reith 103-112)

    Your Bibliography: Reith, Mark. "Brandishing Our Air, Space, And Cyber Swords, Recommendations For Deterrence And Beyond." Air & Space Power Journal Winter 2017 (2017): 103-112. Print.

  • Book

    Reveron, D. S.

    Cyberspace and national security

    2012 - Georgetown University Press - Washington, DC

    In-text: (Reveron 38, 106-109)

    Your Bibliography: Reveron, Derek S. Cyberspace And National Security. 1st ed. Washington, DC: Georgetown University Press, 2012. Print.

  • Website

    Risley, J.

    Microsoft’s millennial chatbot Tay.ai pulled offline after Internet teaches her racism

    2016 - GeekWire Online Technology News Site

    “The AI chatbot Tay is a machine learning project, designed for human engagement,” a Microsoft spokesperson said in a statement. “It is as much a social and cultural experiment, as it is technical. Unfortunately, within the first 24 hours of coming online, we became aware of a coordinated effort by some users to abuse Tay’s commenting skills to have Tay respond in inappropriate ways. As a result, we have taken Tay offline and are making adjustments.”

    In-text: (Risley)

    Your Bibliography: Risley, James. "Microsoft’S Millennial Chatbot Tay.Ai Pulled Offline After Internet Teaches Her Racism." GeekWire. N.p., 2016. Web. 2 Feb. 2019.

  • E-book or PDF

    Sammut, C. and Web, G. I.

    Encyclopedia of Machine Learning and Data Mining

    2017 - Springer Science+Business Media New York - Boston

    Supervised learning refers to any machine learning process that learns a function from an input type to an output type using data comprising examples that have both input and output values. Two typical examples of supervised learning are classification learning and regression. In these cases, the output types are respectively categorical (the classes) and numeric. Supervised learning stands in contrast to unsupervised learning, which seeks to learn structure in data, and to reinforcement learning in which sequential decision-making policies are learned from reward with no examples of “correct” behavior. Unsupervised learning refers to any machine learning process that seeks to learn structure in the absence of either an identified output (cf. supervised learning) or feedback (cf. reinforcement learning). Three typical examples of unsupervised learning are clustering, association rules, and self-organizing maps.

    In-text: (Sammut and Web)

    Your Bibliography: Sammut, Claude, and Geoffrey I. Web. Encyclopedia Of Machine Learning And Data Mining. 1st ed. Boston: Springer Science+Business Media New York, 2017. Web. 2 June 2019.

  • Book

    Scharre, P.

    Army of none

    2018 - W. W. Norton & Company - New York

    Artificial neural networks don't directly mimic biology, but are inspired by it. ...

    In-text: (Scharre 86-87)

    Your Bibliography: Scharre, Paul. Army Of None. 1st ed. New York: W. W. Norton & Company, 2018. Print.

  • Book

    Scharre, P.

    Army of None: Autonomous weapons and the future of War

    2018 - W.W Norton & Company, Inc. - New York, NY

    In-text: (Scharre 205-209)

    Your Bibliography: Scharre, Paul. Army Of None: Autonomous Weapons And The Future Of War. 1st ed. New York, NY: W.W Norton & Company, Inc., 2018. Print.

  • Website

    Seals, T.

    OilRig APT Significantly Evolves in Latest Critical Infrastructure Attacks

    2018 - Infosecurity Magazine

    According to fresh analysis by Nyotron, the latest spate of attacks has been focused on a number of organizations across the Middle East and shows that the OilRig group has significantly evolved its tactics, techniques and procedures to include next-generation malware tools and new data exfiltration methods. Some of the new tools are off-the-shelf, dual-purpose utilities, but others are previously unseen malware using Google Drive and SmartFile, as well as internet server API (ISAPI) filters for compromising Microsoft Internet Information Services (IIS) servers.

    In-text: (Seals)

    Your Bibliography: Seals, Tara. "Oilrig APT Significantly Evolves In Latest Critical Infrastructure Attacks." Infosecurity Magazine. N.p., 2018. Web. 16 Sept. 2018.

  • Journal

    Shackelford, S. J. and Bohm, Z.

    Securing North American Critical Infrastructure: A Comparative Case Study in Cybersecurity Regulation

    2016 - Canada-United States Law Journal

    In-text: (Shackelford and Bohm 61-70)

    Your Bibliography: Shackelford, Scott J., and Zachery Bohm. "Securing North American Critical Infrastructure: A Comparative Case Study In Cybersecurity Regulation." Canada-United States Law Journal 40 (2016): 61-70. Print.

  • Book

    Singer, P. W. and Friedman, A.

    Cybersecurity and cyberwar

    2014 - University Press - Oxford

    In-text: (Singer and Friedman 106, 136-156)

    Your Bibliography: Singer, P. W, and Allan Friedman. Cybersecurity And Cyberwar. 1st ed. Oxford: University Press, 2014. Print.

  • Book

    Stewart, J. M., Chapple, M. and Gibson, D.

    CISSP Certified Information Systems Security Professional Study Guide, 7th

    2015 - John Wiley & Sons - Indianapolis, Indiana

    In-text: (Stewart, Chapple and Gibson 872)

    Your Bibliography: Stewart, James M, Mike Chapple, and Darril Gibson. CISSP Certified Information Systems Security Professional Study Guide, 7Th. 7th ed. Indianapolis, Indiana: John Wiley & Sons, 2015. Print.

  • Website

    The National Artificial Intelligence Research and Development Strategic Plan: 2019 Update

    2019 - A Report by the Select Committee on Artifical Intelligence of the National Science & Technology Council

    This 2019 update builds upon the first National AI R&D Strategic Plan released in 2016, accounting for new research, technical innovations, and other considerations that have emerged over the past three years. This update has been developed by leading AI researchers and research administrators from across the Federal Government, with input from the broader civil society, including from many of America’s leading academic research institutions, nonprofit organizations, and private sector technology companies.

    In-text: ("The National Artificial Intelligence Research And Development Strategic Plan: 2019 Update")

    Your Bibliography: "The National Artificial Intelligence Research And Development Strategic Plan: 2019 Update." Nitrd.gov. N.p., 2019. Web. 25 June 2019.

  • Govt. publication

    The White House

    National Cyber Strategy of the United States of America

    2018 - The U.S. Government - Washington DC

    In-text: (The White House I-VI, 1-11, 15-21)

    Your Bibliography: The White House. National Cyber Strategy Of The United States Of America. Washington DC: The U.S. Government, 2018. Print.

  • Website

    Trump, D.

    Executive Order 13859 of February 11, 2019, Maintaining American Leadership in Artificial Intelligence

    2019 - Federal Register: The Daily Journal of the United States Government

    Maintaining American leadership in AI requires a concerted effort to promote advancements in technology and innovation, while protecting American technology, economic and national security, civil liberties, privacy, and American values and enhancing international and industry collaboration with foreign partners and allies. It is the policy of the United States Government to sustain and enhance the scientific, technological, and economic leadership position of the United States in AI R&D and deployment through a coordinated Federal Government strategy, the American AI Initiative (Initiative), guided by five principles: (a) The United States must drive technological breakthroughs in AI across the Federal Government, industry, and academia in order to promote scientific discovery, economic competitiveness, and national security. (b) The United States must drive development of appropriate technical standards and reduce barriers to the safe testing and deployment of AI technologies in order to enable the creation of new AI-related industries and the adoption of AI by today’s industries. (c) The United States must train current and future generations of American workers with the skills to develop and apply AI technologies to prepare them for today’s economy and jobs of the future.

    In-text: (Trump)

    Your Bibliography: Trump, Donald. "Executive Order 13859 Of February 11, 2019, Maintaining American Leadership In Artificial Intelligence." Federal Register. N.p., 2019. Web. 25 June 2019.

  • Report

    United States of America General Accounting Office

    GAO-18-142SP Artifical Intelligence

    2018 - U.S. Government's General Accounting Office (GAO) - Washington, DC

    In-text: (United States of America General Accounting Office 58-64)

    Your Bibliography: United States of America General Accounting Office. GAO-18-142SP Artifical Intelligence. Washington, DC: U.S. Government's General Accounting Office (GAO), 2018. Print. Appendix IV: Profiles Of AI In Cybersecurity, Automated Vehicles, Criminal Justice, And Financial Services.

  • Report

    United States of America General Accounting Office

    GAO-18-142SP Artifical Intelligence

    2018 - U.S. Government's General Accounting Office (GAO) - Washington, DC

    In-text: (United States of America General Accounting Office 58-64)

    Your Bibliography: United States of America General Accounting Office. GAO-18-142SP Artifical Intelligence. Washington, DC: U.S. Government's General Accounting Office (GAO), 2018. Print. Appendix IV: Profiles Of AI In Cybersecurity, Automated Vehicles, Criminal Justice, And Financial Services.

  • Website

    US steps up reviews of Chinese research activity on American campuses

    2018 - South China Morning Post website

    Speaking at a Senate Judiciary Committee hearing in Washington last week, Bill Priestap, the bureau’s assistant director of counter-intelligence, called China “the most severe counter-intelligence threat facing our country today”. Two months ago, Wray said at a Senate Homeland Security Committee that “China in many ways represents the broadest, most complicated, most long-term counter-intelligence threat we face”. The US government’s concern about China was amplified this week, when the US Justice Department announced criminal indictments against two accused hackers associated with the Chinese government.

    In-text: ("US Steps Up Reviews Of Chinese Research Activity On American Campuses")

    Your Bibliography: "US Steps Up Reviews Of Chinese Research Activity On American Campuses." South China Morning Post. N.p., 2018. Web. 2 Feb. 2019.

  • Website

    US steps up reviews of Chinese research activity on American campuses

    2018 - South China Morning Post website

    Speaking at a Senate Judiciary Committee hearing in Washington last week, Bill Priestap, the bureau’s assistant director of counter-intelligence, called China “the most severe counter-intelligence threat facing our country today”. Two months ago, Wray said at a Senate Homeland Security Committee that “China in many ways represents the broadest, most complicated, most long-term counter-intelligence threat we face”. The US government’s concern about China was amplified this week, when the US Justice Department announced criminal indictments against two accused hackers associated with the Chinese government.

    In-text: ("US Steps Up Reviews Of Chinese Research Activity On American Campuses")

    Your Bibliography: "US Steps Up Reviews Of Chinese Research Activity On American Campuses." South China Morning Post. N.p., 2018. Web. 2 Feb. 2019.

  • Website

    Vector Institute for Artificial Intelligence |

    2019 - Vector Institute for Artificial Intelligence

    Vector is a leader in the transformative field of artificial intelligence, excelling in machine and deep learning — an area of scientific, academic, and commercial endeavour that will shape our world over the next generation. We are building on a well-established and respected foundation of globally recognized talent and learning that exists today in Toronto, Ontario and Canada more broadly. Our researchers are at the leading edge of deep learning and machine learning in a diverse set of areas, including neural networks, probabilistic models, statistical theory, computational biology, computer vision and natural language processing.

    In-text: ("Vector Institute For Artificial Intelligence |")

    Your Bibliography: "Vector Institute For Artificial Intelligence |." Vectorinstitute.ai. N.p., 2019. Web. 18 Feb. 2019.

  • Newspaper

    Viswanatha, A. and McMillan, R.

    Chinese National Charged With Providing Hackers With Malware Linked to OPM Breach; Yu Pingan is accused of conspiring with others to hack into four U.S. companies using Sakula and other malicious software tools

    2017 - Wall Street Journal (Online)

    In-text: (Viswanatha and McMillan)

    Your Bibliography: Viswanatha, Aruna, and Robert McMillan. "Chinese National Charged With Providing Hackers With Malware Linked To OPM Breach; Yu Pingan Is Accused Of Conspiring With Others To Hack Into Four U.S. Companies Using Sakula And Other Malicious Software Tools." Wall Street Journal (Online) 2017: n. pag. Print.

  • Journal

    Wang, S. and Qiao, Z.

    Robust Pervasive Detection for Adversarial Samples of Artificial Intelligence in IoT Environments

    2019 - IEEE Access

    In-text: (Wang and Qiao 88693-88704)

    Your Bibliography: Wang, Shen, and Zhuobiao Qiao. "Robust Pervasive Detection For Adversarial Samples Of Artificial Intelligence In Iot Environments." IEEE Access 7 (2019): 88693-88704. Web. 20 Oct. 2019.

  • Website

    Webster, G., Kania, E., Triolo, P. and Creemers, R.

    Full Translation: China's 'New Generation Artificial Intelligence Development Plan' (2017)

    2019 - New America

    In-text: (Webster et al.)

    Your Bibliography: Webster, Graham et al. "Full Translation: China's 'New Generation Artificial Intelligence Development Plan' (2017)." New America. N.p., 2019. Web. 28 Feb. 2019.

  • Website

    What is Deepfake? - Definition from Techopedia

    2019

    In-text: ("What Is Deepfake? - Definition From Techopedia")

    Your Bibliography: "What Is Deepfake? - Definition From Techopedia." Techopedia.com. N.p., 2019. Web. 7 Aug. 2019.

  • Website

    What is generative adversarial network (GAN)? - Definition from WhatIs.com

    2019 - TechTarget

    The TechTarget network of technology-specific websites give you access to industry experts, independent content and analysis.

    In-text: ("What Is Generative Adversarial Network (GAN)? - Definition From Whatis.Com")

    Your Bibliography: "What Is Generative Adversarial Network (GAN)? - Definition From Whatis.Com." SearchEnterpriseAI. N.p., 2019. Web. 7 Aug. 2019.

  • Journal

    Wilner, A. S.

    Cybersecurity and its discontents: Artificial intelligence, the Internet of Things, and digital misinformation

    2018 - International Journal: Canada's Journal of Global Policy Analysis

    In-text: (Wilner 308-316)

    Your Bibliography: Wilner, Alex S. "Cybersecurity And Its Discontents: Artificial Intelligence, The Internet Of Things, And Digital Misinformation." International Journal: Canada's Journal of Global Policy Analysis 73.2 (2018): 308-316. Web. 2 June 2019.

  • Journal

    Yadav, D., Arora, M. K., Tiwari, K. C. and Ghosh, J. K.

    Detection and Identification of Camouflaged Targets using Hyperspectral and LiDAR data

    2018 - Defence Science Journal

    In-text: (Yadav et al. 540)

    Your Bibliography: Yadav, Deepti et al. "Detection And Identification Of Camouflaged Targets Using Hyperspectral And Lidar Data." Defence Science Journal 68.6 (2018): 540. Web. 13 Oct. 2019.

  • Journal

    Yampolskiy, R. V.

    AI Is the Future of Cybersecurity, for Better and for Worse

    2017 - Harvard Business Review

    In-text: (Yampolskiy 2-4)

    Your Bibliography: Yampolskiy, Roman V. "AI Is The Future Of Cybersecurity, For Better And For Worse." Harvard Business Review (2017): 2-4. Web. 5 Oct. 2018.

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