Abstract
In this chapter, I draw on my previous work on trust and cybersecurity to offer a definition of trust and trustworthiness to understand to what extent trusting AI for cybersecurity tasks is justified and what measures can be put in place to rely on AI in cases where trust is not justified, but the use of AI is still beneficial.
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References
BehavioSec: Continuous Authentication Through Behavioral Biometrics (2019) BehavioSec 2019. https://www.behaviosec.com/
DarkLight Offers First of Its Kind Artificial Intelligence to Enhance Cybersecurity Defenses (2017) Business wire.. 26 July 2017. https://www.businesswire.com/news/home/20170726005117/en/DarkLight-Offers-Kind-Artificial-Intelligence-Enhance-Cybersecurity
DeepLocker: How AI Can Power a Stealthy New Breed of Malware (2018) Security intelligence (blog). 8 August 2018. https://securityintelligence.com/deeplocker-how-ai-can-power-a-stealthy-new-breed-of-malware/
Acalvio Autonomous Deception (2019) Acalvio 2019. https://www.acalvio.com/
Athalye A, Engstrom L, Ilyas A, Kwok K (2017) Synthesizing robust adversarial examples. ArXiv:170707397 [Cs] (July). http://arxiv.org/abs/1707.07397
Biggio B, Roli F (2018) Wild patterns: ten years after the rise of adversarial machine learning. In: Proceedings of the 2018 ACM SIGSAC conference on computer and communications security – CCS ’18. ACM Press, Toronto, pp 2154–2156. https://doi.org/10.1145/3243734.3264418
Borno R (2017) ‘The first imperative: the best digital offense starts with the best security Defense’. 2017. https://newsroom.cisco.com/feature-content?type=webcontent&articleId=1843565
Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. In: 2017 IEEE symposium on security and privacy (SP), pp 39–57. https://doi.org/10.1109/SP.2017.49
Eykholt, Kevin, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song (2018) Robust physical-world attacks on deep learning visual classification. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 1625–1634. IEEE, Salt Lake City, UT, USA. https://doi.org/10.1109/CVPR.2018.00175
Floridi L (2002) On the intrinsic value of information objects and the Infosphere. Ethics Inf Technol 4(4):287–304
Floridi L (2011) The philosophy of information. Oxford University Press, Oxford; New York
Floridi L (2014) The fourth revolution, how the Infosphere is reshaping human reality. Oxford University Press, Oxford
Floridi L (2016) Mature information societies—a matter of expectations. Philos Technol 29(1):1–4. https://doi.org/10.1007/s13347-016-0214-6
Floridi L, Taddeo M (2016) What is data ethics? Philos Trans Roy Soc A Math Phys Eng Sci 374(2083):20160360 https://doi.org/10.1098/rsta.2016.0360
Gu T, Dolan-Gavitt B, Garg S (2017) BadNets: identifying vulnerabilities in the machine learning model supply chain. ArXiv:170806733 [Cs] (August). http://arxiv.org/abs/1708.06733
High Level Expert Group on Artificial Intelligence (2019) Ethics guideline for trustworthy AI. https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
IEEE (2017) Artificial intelligence and machine learning applied to cybersecurity. https://www.ieee.org/about/industry/confluence/feedback.html
Jagielski M, Oprea A, Biggio B, Liu C, Nita-Rotaru C, Li B (2018) Manipulating machine learning: poisoning attacks and countermeasures for regression learning. ArXiv:180400308 [Cs] (April). http://arxiv.org/abs/1804.00308
Liao C, Zhong H, Squicciarini A, Zhu S, Miller D (2018) Backdoor embedding in convolutional neural network models via invisible perturbation. ArXiv:180810307 [Cs, Stat] (August). http://arxiv.org/abs/1808.10307
Luhmann N (1979) Trust and power: two works. Wiley, Chichester; New York
Mirsky, Yisroel, Tom Mahler, Ilan Shelef, and Yuval Elovici. 2019. ‘CT-GAN: malicious tampering of 3D medical imagery using deep learning’.. ResearchGate. https://www.researchgate.net/publication/330357848_CT-GAN_Malicious_Tampering_of_3D_Medical_Imagery_using_Deep_Learning/figures?lo=1
Primiero G, Taddeo M (2012) A modal type theory for formalizing trusted communications. J Appl Log 10(1):92–114. https://doi.org/10.1016/j.jal.2011.12.002
Sharif M, Bhagavatula S, Bauer L, Reiter MK (2016) Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security – CCS’16. ACM Press, Vienna, Austria, pp 1528–1540. https://doi.org/10.1145/2976749.2978392
Sinha A, Namkoong H, Duchi J (2017) Certifying some distributional robustness with principled adversarial training. ArXiv:1710.10571 [Cs, stat], October. http://arxiv.org/abs/1710.10571
Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2013) Intriguing properties of neural networks. ArXiv:13126199 [Cs] (December). http://arxiv.org/abs/1312.6199
Taddeo M (2009) Defining trust and E-trust: from old theories to new problems. Int J Technol Hum Interact. www.igi-global.com/article/defining-trust-trust/2939
Taddeo M (2010a) Modelling Trust in Artificial Agents, a first step toward the analysis of e-trust. Mind Mach 20(2):243–257. https://doi.org/10.1007/s11023-010-9201-3
Taddeo M (2010b) An information-based solution for the puzzle of testimony and trust. Soc Epistemol 24(4):285–299. https://doi.org/10.1080/02691728.2010.521863
Taddeo M (2013) Cyber security and individual rights, striking the right balance. Philos Technol 26(4):353–356. https://doi.org/10.1007/s13347-013-0140-9
Taddeo M (2014) The struggle between liberties and authorities in the information age. Sci Eng Ethics 1–14 https://doi.org/10.1007/s11948-014-9586-0
Taddeo M (2017a) The limits of deterrence theory in cyberspace. Philos Technol. https://doi.org/10.1007/s13347-017-0290-2
Taddeo M (2017b) Trusting digital technologies correctly. Minds Mach. https://doi.org/10.1007/s11023-017-9450-5
Taddeo M (2018a) How AI can be a force for good. Science 361(6404):751–752. https://doi.org/10.1126/science.aat5991
Taddeo M (2018b) The grand challenges of science robotics. Sci Robot 3(14):eaar7650. https://doi.org/10.1126/scirobotics.aar7650
Taddeo M (2019) Three ethical challenges of applications of artificial intelligence in cybersecurity. Mind Mach 29(2):187–191. https://doi.org/10.1007/s11023-019-09504-8
Taddeo M, Floridi L (2018) Regulate artificial intelligence to avert cyber arms race. Nature 556(7701):296–298. https://doi.org/10.1038/d41586-018-04602-6
Taddeo M, McCutcheon T, Floridi L (2019) Trusting artificial intelligence in cybersecurity is a double-edged sword. Nat Mach Intell 1(12):557–560. https://doi.org/10.1038/s42256-019-0109-1
The 2019 Official Annual Cybercrime Report (2019) Herjavec Group. https://www.herjavecgroup.com/the-2019-official-annual-cybercrime-report/
Uesato J, O’Donoghue B, van den Oord A, Kohli P (2018) Adversarial risk and the dangers of evaluating against weak attacks. ArXiv:180205666 [Cs, Stat] (February). http://arxiv.org/abs/1802.05666
European Union (2019) Regulation of the European Parliament and of the council on ENISA (the European Union Agency for cybersecurity) and on information and communications technology cybersecurity certification and repealing regulation (EU) no 526/2013 (cybersecurity act)
Yang G-Z, Bellingham J, Dupont PE, Fischer P, Floridi L, Full R, Jacobstein N et al (2018) The grand challenges of science robotics. Sci Robot 3(14):eaar7650. https://doi.org/10.1126/scirobotics.aar7650
Zhuge J, Holz T, Han X, Song C, Zou W (2007) Collecting autonomous spreading malware using high-interaction honeypots. In: Qing S, Imai H, Wang G (eds) Information and communications security. Lecture notes in computer science. Springer, Berlin Heidelberg, pp 438–451
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Taddeo, M. (2021). On the Risks of Trusting Artificial Intelligence: The Case of Cybersecurity. In: Cowls, J., Morley, J. (eds) The 2020 Yearbook of the Digital Ethics Lab. Digital Ethics Lab Yearbook. Springer, Cham. https://doi.org/10.1007/978-3-030-80083-3_10
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