Augmented intelligence in social engineering attacks: a diffusion of innovation perspective

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DOI:

https://doi.org/10.36096/ijbes.v7i1.676

Keywords:

Augmented intelligence, artificial intelligence, information security, social network site (SNS) users

Abstract

This article explores social network site (SNS) users’ understanding of the danger the integration of human intelligence and artificial intelligence (AI), termed “augmented intelligence,” presents.  Augmented intelligence, a subsection of artificial intelligence (AI), aims to enhance human intelligence with AI and is heralded as a significant step in problem-solving. A crucial concern is the profound threat to SNS users’ information security. A quantitative approach examined SNS understanding regarding the diffusion of augmented intelligence into SNS users’ spaces. An online survey was administered to 165 SNS users residing in the Gauteng province of South Africa.  Diffusion of Innovation (DOI) theory was used as the theoretical lens. Ethical clearance was obtained, and the data collected was anonymized and kept confidential. The article provides new insights that can help SNS users understand that a new threat to their information security in the form of augmented intelligence is emerging. Findings suggest that out of the five constructs drawn from DOI that explain the diffusion of augmented intelligence into sophisticated social engineering attacks, relative advantage, compatibility, and complexity were perceived by study participants as likely predictors of augmented intelligence adoption. Users, however, differed on exactly how the augmentation process was being achieved.

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References

Agrawal, A. K., Gans, J. S., & Goldfarb, A. (2021). AI adoption and system-wide change. https://doi.org/10.3386/w28811 DOI: https://doi.org/10.3386/w28811

Algarni, A., Xu, Y., Chan, T., & Tian, Y.-C. (2013). Social engineering in social networking sites: Affect-based model. Proceedings of the 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013). https://doi.org/10.1109/ICITST.2013.6750253 DOI: https://doi.org/10.1109/ICITST.2013.6750253

Alneyadi, M. R. M. A. H., & Normalini, M. K. (2023). Factors influencing user’s intention to adopt AI-based cybersecurity systems in the UAE. Interdisciplinary Journal of Information, Knowledge, and Management, 18, 459–486. https://doi.org/10.28945/5166 DOI: https://doi.org/10.28945/5166

Alqatawna, J. F., Madain, A., Al-Zoubi, A. M., & Al-Sayyed, R. (2017). Online social networks security: Threats, attacks, and future directions. In Social Media Shaping E-Publishing and Academia (pp. 121–132). https://doi.org/10.1007/978-3-319-55354-2_10 DOI: https://doi.org/10.1007/978-3-319-55354-2_10

Angelica, A., Opris, I., Lebedev, M. A., & Boehm, F. (2021). Cognitive augmentation via a brain/cloud interface. In Modern Approaches to Augmentation of Brain Function (pp. 357–386). https://doi.org/10.1007/978-3-030-54564-2_17 DOI: https://doi.org/10.1007/978-3-030-54564-2_17

Arquilla, J., Fusco, J., Ruiz, P., & Roschelle, J. (2021). Securing seabed cybersecurity, emphasizing intelligence augmentation. Communications of the ACM, 64(7), 10–12. https://doi.org/10.1145/3464931 DOI: https://doi.org/10.1145/3464931

Bansal, G., Nushi, B., Kamar, E., Weld, D. S., Lasecki, W. S., & Horvitz, E. (2019b). Updates in human-AI teams: Understanding and addressing the performance/compatibility tradeoff. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v33i01.33012429 DOI: https://doi.org/10.1609/aaai.v33i01.33012429

Bansal, G., Nushi, B., Kamar, E., Weld, D., Lasecki, W., & Horvitz, E. (2019a). A case for backward compatibility for human-AI teams. arXiv Preprint, arXiv:1906.01148. https://doi.org/10.48550/arXiv.1906.01148

Barrat, J. (2023). Our final invention: Artificial intelligence and the end of the human era. Hachette UK.

Barukh, M. C., Zamanirad, S., Baez, M., Beheshti, A., Benatallah, B., Casati, F., … Schiliro, F. (2021). Cognitive augmentation in processes. In Next-Gen Digital Services: A Retrospective and Roadmap for Service Computing of the Future (pp. 123–137). https://doi.org/10.1007/978-3-030-73203-5_10 DOI: https://doi.org/10.1007/978-3-030-73203-5_10

Bazoukis, G., Hall, J., Loscalzo, J., Antman, E. M., Fuster, V., & Armoundas, A. A. (2022). The inclusion of augmented intelligence in medicine: A framework for successful implementation. Cell Reports Medicine, 3(1). https://doi.org/10.1016/j.xcrm.2021.100485 DOI: https://doi.org/10.1016/j.xcrm.2021.100485

Brézillon, P. (1999). Context in artificial intelligence: I. A survey of the literature. Computers and Artificial Intelligence, 18(4), 321–340. Retrieved from https://www.cai.sk/ojs/index.php/cai/article/view/589

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., … Filar, B. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv Preprint, arXiv:1802.07228. https://doi.org/10.48550/arXiv.1802.07228

Burkholder, G. J., Cox, K. A., Crawford, L. M., & Hitchcock, J. H. (2019). Research design and methods: An applied guide for the scholar-practitioner. Sage Publications.

Caliskan, A. (2023). Artificial intelligence, bias, and ethics. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2023/799 DOI: https://doi.org/10.24963/ijcai.2023/799

Cambiaso, E., & Caviglione, L. (2023). Scamming the scammers: Using ChatGPT to reply mails for wasting time and resources. arXiv Preprint, arXiv:2303.13521. https://doi.org/10.48550/arXiv.2303.13521

Cinel, C., Valeriani, D., & Poli, R. (2019). Neurotechnologies for human cognitive augmentation: Current state of the art and future prospects. Frontiers in Human Neuroscience, 13, 13. https://doi.org/10.3389/fnhum.2019.00013 DOI: https://doi.org/10.3389/fnhum.2019.00013

Clinch, S., & Davies, N. (2023). Hacking the brain: The risks and challenges of cognitive augmentation. IFIP Conference on Human-Computer Interaction. https://doi.org/10.1007/978-3-031-42293-5_18 DOI: https://doi.org/10.1007/978-3-031-42293-5_18

Cooke, P. (2023). Learning as imitation or mimesis: How ‘smart’ is machine learning for its planning controllers? European Planning Studies, 31(7), 1345–1357. https://doi.org/10.1080/09654313.2022.2124102 DOI: https://doi.org/10.1080/09654313.2022.2124102

Craighead, C. W., Ketchen, D. J., Dunn, K. S., & Hult, G. T. M. (2011). Addressing common method variance: Guidelines for survey research on information technology, operations, and supply chain management. IEEE Transactions on Engineering Management, 58(3), 578–588. https://doi.org/10.1109/TEM.2011.2136437 DOI: https://doi.org/10.1109/TEM.2011.2136437

Dalton, A., Dorr, B., Liang, L., & Hollingshead, K. (2017). Improving cyber-attack predictions through information foraging. 2017 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/BigData.2017.8258509 DOI: https://doi.org/10.1109/BigData.2017.8258509

De Felice, F., Petrillo, A., De Luca, C., & Baffo, I. (2022). Artificial intelligence or augmented intelligence? Impact on our lives, rights and ethics. Procedia Computer Science, 200, 1846–1856. https://doi.org/10.1016/j.procs.2022.01.385 DOI: https://doi.org/10.1016/j.procs.2022.01.385

Dobrkovic, A., Döppner, D. A., Iacob, M.-E., & van Hillegersberg, J. (2018). Collaborative literature search system: An intelligence amplification method for systematic literature search. 13th International Conference, DESRIST 2018, Chennai, India, June 3–6, 2018. https://doi.org/10.1007/978-3-319-91800-6_12 DOI: https://doi.org/10.1007/978-3-319-91800-6_12

Dobrkovic, A., Liu, L., Iacob, M.-E., & van Hillegersberg, J. (2016). Intelligence amplification framework for enhancing scheduling processes. 15th Ibero-American Conference on AI, San José, Costa Rica, November 23-25, 2016. https://doi.org/10.1007/978-3-319-47955-2_8 DOI: https://doi.org/10.1007/978-3-319-47955-2_8

Dong, Y., Jiang, X., Jin, Z., & Li, G. (2023). Self-collaboration code generation via ChatGPT. arXiv Preprint, arXiv:2304.07590. https://doi.org/10.48550/arXiv.2304.07590

Falade, P. V. (2023). Decoding the threat landscape: ChatGPT, FraudGPT, and WormGPT in social engineering attacks. arXiv Preprint, arXiv:2310.05595. https://doi.org/10.32628/CSEIT2390533 DOI: https://doi.org/10.32628/CSEIT2390533

Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Taylor & Francis, 25, 277–304. https://doi.org/10.1080/15228053.2023.2233814 DOI: https://doi.org/10.1080/15228053.2023.2233814

Galliers, R. D., & Land, F. F. (1987). Choosing appropriate information systems research methodologies. Communications of the ACM, 30(11), 901–902. https://doi.org/10.1145/32206.315753 DOI: https://doi.org/10.1145/32206.315753

Gehl, R. W., & Lawson, S. T. (2022). Social engineering: How crowdmasters, phreaks, hackers, and trolls created a new form of manipulative communication. MIT Press. https://doi.org/10.7551/mitpress/12984.001.0001 DOI: https://doi.org/10.7551/mitpress/12984.001.0001

Grbic, D. V., & Dujlovic, I. (2023). Social engineering with ChatGPT. 2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH). https://doi.org/10.1109/INFOTEH57020.2023.10094141 DOI: https://doi.org/10.1109/INFOTEH57020.2023.10094141

Heale, R., & Twycross, A. (2015). Validity and reliability in quantitative studies. Evidence-Based Nursing, 18(3), 66–67. https://doi.org/10.1136/eb-2015-102129 DOI: https://doi.org/10.1136/eb-2015-102129

Hernández-Orallo, J., & Vold, K. (2019). AI extenders: The ethical and societal implications of humans cognitively extended by AI. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3306618.3314238 DOI: https://doi.org/10.1145/3306618.3314238

Hoong, P. (2021). A preliminary propagation tool in social engineering attacks. UTAR. http://eprints.utar.edu.my/id/eprint/4159

Hurley, D. (2020). Brain-computer interfaces move forward at the speed of Musk. Neurology Today, 20(19), 40–42. https://doi.org/10.1097/01.NT.0000720212.33775.ac DOI: https://doi.org/10.1097/01.NT.0000720212.33775.ac

Jain, H., Padmanabhan, B., Pavlou, P. A., & Raghu, T. (2021). Editorial for the special section on humans, algorithms, and augmented intelligence: The future of work, organizations, and society. Information Systems Research, 32(3), 675–687. https://doi.org/10.1287/isre.2021.1046 DOI: https://doi.org/10.1287/isre.2021.1046

Jordan, T. (2009). Hacking and power: Social and technological determinism in the digital age. First Monday. https://doi.org/10.5210/fm.v14i7.2417 DOI: https://doi.org/10.5210/fm.v14i7.2417

Jun, Y., Craig, A., Shafik, W., & Sharif, L. (2021). Artificial intelligence application in cybersecurity and cyberdefense. Wireless Communications and Mobile Computing, 2021, 1–10. https://doi.org/10.1155/2021/3329581 DOI: https://doi.org/10.1155/2021/3329581

Kharb, L., & Chahal, D. (2023). Exploring social engineering exploitations with ChatGPT. International Research Journal of Modernization in Engineering Technology and Science, 05(08), 1843–1849. https://doi.org/10.56726/IRJMETS44199 DOI: https://doi.org/10.56726/IRJMETS44199

Krombholz, K., Hobel, H., Huber, M., & Weippl, E. (2015). Advanced social engineering attacks. Journal of Information Security and Applications, 22, 113–122. https://doi.org/10.1016/j.jisa.2014.09.005 DOI: https://doi.org/10.1016/j.jisa.2014.09.005

Mansfield-Devine, S. (2023). Weaponising ChatGPT. Network Security, 2023(4). https://doi.org/10.12968/S1353-4858(23)70017-2 DOI: https://doi.org/10.12968/S1353-4858(23)70017-2

Manyam, S. (2022). Artificial intelligence’s impact on social engineering attacks. Retrieved from https://opus.govst.edu/cgi/viewcontent.cgi?article=1521&context=capstones

Marcus, B., Weigelt, O., Hergert, J., Gurt, J., & Gelléri, P. (2017). The use of snowball sampling for multi-source organizational research: Some cause for concern. Personnel Psychology, 70(3), 635–673. https://doi.org/10.1111/peps.12169 DOI: https://doi.org/10.1111/peps.12169

Mikhalevich, I. F., & Ryjov, A. P. (2018). Augmented intelligence framework for protecting against cyberattacks. 2018 Engineering and Telecommunication (EnT-MIPT). https://doi.org/10.1109/EnT-MIPT.2018.00039 DOI: https://doi.org/10.1109/EnT-MIPT.2018.00039

Milberry, K. (2012). Hacking for social justice: The politics of prefigurative technology. In (Re) Inventing the Internet (pp. 109–130). Brill. https://doi.org/10.1007/978-94-6091-734-9_6 DOI: https://doi.org/10.1007/978-94-6091-734-9_6

Narayanan, V. K., & O’Connor, G. C. (2015). Knowledge management as intelligence amplification for breakthrough innovations. Design Thinking: New Product Development Essentials from the PDMA, 187–204. https://doi.org/10.1002/9781119154273.ch13 DOI: https://doi.org/10.1002/9781119154273.ch13

Okoli, C., & Schabram, K. (2015). A guide to conducting a systematic literature review of information systems research. https://doi.org/10.17705/1CAIS.03743 DOI: https://doi.org/10.17705/1CAIS.03743

Pankratz, M., Hallfors, D., & Cho, H. (2002). Measuring perceptions of innovation adoption: The diffusion of a federal drug prevention policy. Health Education Research, 17(3), 315–326. https://doi.org/10.1093/her/17.3.315 DOI: https://doi.org/10.1093/her/17.3.315

Parker, C., Scott, S., & Geddes, A. (2019). Snowball sampling. SAGE Research Methods Foundations.

Paul, S., Yuan, L., Jain, H. K., Robert Jr, L. P., Spohrer, J., & Lifshitz-Assaf, H. (2022). Intelligence augmentation: Human factors in AI and future of work. AIS Transactions on Human-Computer Interaction, 14(3), 426–445. https://doi.org/10.17705/1thci.00174 DOI: https://doi.org/10.17705/1thci.00174

Peltier, T. R. (2006). Social engineering: Concepts and solutions. Information Security Journal, 15(5), 13. https://doi.org/10.1201/1086.1065898X/46353.15.4.20060901/95427.3 DOI: https://doi.org/10.1201/1086.1065898X/46353.15.4.20060901/95427.3

Plsek, P. (2003). Complexity and the adoption of innovation in health care. National Institute for Healthcare Management Foundation and National Committee for Quality in Health Care. https://chess.wisc.edu/niatx/PDF/PIPublications/Plsek_2003_NIHCM.pdf

Vargo, A., Tag, B., Hutin, M., Abou-Khalil, V., Ishimaru, S., Augereau, O., & Devillers, L. (2023). Intelligence augmentation: Future directions and ethical implications in HCI. Paper presented at the IFIP Conference on Human-Computer Interaction. https://doi.org/10.1007/978-3-031-42293-5_87 DOI: https://doi.org/10.1007/978-3-031-42293-5_87

Velliangiri, S., Karthikeyan, P., Ravi, V., Almeshari, M., & Alzamil, Y. (2023). Intelligence amplification-based smart health record chain for enterprise management system. Information, 14(5), 284. https://doi.org/10.3390/info14050284 DOI: https://doi.org/10.3390/info14050284

Ventayen, R. J. M. (2023). ChatGPT by OpenAI: Students' viewpoint on cheating using artificial intelligence-based applications. Available at SSRN 4361548. https://doi.org/10.2139/ssrn.4361548 DOI: https://doi.org/10.2139/ssrn.4361548

Walters, G. (2018). Evaluating the effectiveness of personal cognitive augmentation: Utterance/intent relationships, brittleness, and personal cognitive agents. Paper presented at the Human Interface and the Management of Information. https://doi.org/10.1007/978-3-319-92046-7_46 DOI: https://doi.org/10.1007/978-3-319-92046-7_46

Wellsandt, S., Klein, K., Hribernik, K., Lewandowski, M., Bousdekis, A., Mentzas, G., & Thoben, K.-D. (2022). Hybrid-augmented intelligence in predictive maintenance with digital intelligent assistants. Annual Reviews in Control, 53, 382–390. https://doi.org/10.1016/j.arcontrol.2022.04.001 DOI: https://doi.org/10.1016/j.arcontrol.2022.04.001

Wijnhoven, F. (2022). Organizational learning for intelligence amplification adoption: Lessons from a clinical decision support system adoption project. Information Systems Frontiers, 24(3), 731–744. https://doi.org/10.1007/s10796-021-10206-9 DOI: https://doi.org/10.1007/s10796-021-10206-9

Xu, M., Niyato, D., Chen, J., Zhang, H., Kang, J., Xiong, Z., & Han, Z. (2023). Generative AI-empowered simulation for autonomous driving in vehicular mixed reality metaverses. arXiv preprint arXiv:2302.08418. https://doi.org/10.1109/JSTSP.2023.3293650 DOI: https://doi.org/10.1109/JSTSP.2023.3293650

Xue, J., Hu, B., Li, L., & Zhang, J. (2022). Human-machine augmented intelligence: Research and applications. Frontiers of Information Technology & Electronic Engineering, 23(8), 1139–1141. https://doi.org/10.1631/FITEE.2250000 DOI: https://doi.org/10.1631/FITEE.2250000

Yilmaz, R., & Yilmaz, F. G. K. (2023). Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning. Computers in Human Behavior: Artificial Humans, 1(2), 100005. https://doi.org/10.1016/j.chbah.2023.100005 DOI: https://doi.org/10.1016/j.chbah.2023.100005

Zeng, Y. (2022). AI empowers security threats and strategies for cyber attacks. Procedia Computer Science, 208, 170–175. https://doi.org/10.1016/j.procs.2022.10.025 DOI: https://doi.org/10.1016/j.procs.2022.10.025

Zheng, N.-N., Liu, Z.-Y., Ren, P.-J., Ma, Y.-Q., Chen, S.-T., Yu, S.-Y., & Wang, F.-Y. (2017). Hybrid-augmented intelligence: Collaboration and cognition. Frontiers of Information Technology & Electronic Engineering, 18(2), 153–179. https://doi.org/10.1631/FITEE.1700053 DOI: https://doi.org/10.1631/FITEE.1700053

Zhou, J., Zhang, Y., Luo, Q., Parker, A. G., & De Choudhury, M. (2023). Synthetic lies: Understanding AI-generated misinformation and evaluating algorithmic and human solutions. Paper presented at the Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581318 DOI: https://doi.org/10.1145/3544548.3581318

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Published

2025-03-07

How to Cite

Njenga, K., & Matemane, B. (2025). Augmented intelligence in social engineering attacks: a diffusion of innovation perspective. International Journal of Business Ecosystem & Strategy (2687-2293), 7(1), 106–121. https://doi.org/10.36096/ijbes.v7i1.676

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Leadership, Innovation and Technology