AI-Powered Cyber Threats: A Systematic Review
Main Article Content
Abstract
The joining of artificial intelligence (AI) across different areas has fundamentally improved productivity and development. Nevertheless, this progression has increased cybersecurity threats, especially those determined by AI itself. These AI-powered threats exploit the advancements intended to obtain computerized frameworks, in this manner subverting their honesty. This systematic review focuses on the intricacies of AI-driven cyber threats, which use complex AI abilities to lead to intricate and tricky cyberattacks. Our review integrates existing examinations to determine the extension, location procedures, effects, and relief systems connected with AI-initiated threats. We feature the powerful exchange between AI improvement and cybersecurity, underlining the requirement for cutting edge protective frameworks that advance pairs with increasing threats. The discoveries highlight the basic job of AI in both carrying out and countering cybersecurity measures, representing a dualistic effect that requires ceaseless development in cybersecurity techniques.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
A. Clim, “Cyber Security Beyond the Industry 4.0 Era. A Short Review on a Few Technological Promises,” Inform. Econ., vol. 23, no. 2/2019, pp. 34–44, Jun. 2019, doi: 10.12948/issn14531305/23.2.2019.04.
P. Bagó, “Cyber security and artificial intelligence,” Econ. Finance, vol. 10, no. 2, pp. 189–212, 2023, doi: 10.33908/EF.2023.2.5.
M. Mijwil, O. J. Unogwu, Y. Filali, I. Bala, and H. Al-Shahwani, “Exploring the Top Five Evolving Threats in Cybersecurity: An In-Depth Overview,” Mesopotamian J. Cyber Secur., pp. 57–63, Mar. 2023, doi: 10.58496/MJCS/2023/010.
“Cyber Threat Intelligence Market Report 2024 - Cyber Threat Intelligence Market Trends And Overview.” Accessed: Nov. 06, 2024. [Online]. Available: https://www.thebusinessresearchcompany.com/report/cyber-threat-intelligence-global-market-report
N. Kaloudi and J. Li, “The AI-Based Cyber Threat Landscape: A Survey,” ACM Comput. Surv., vol. 53, no. 1, p. 20:1-20:34, Feb. 2020, doi: 10.1145/3372823.
S. Zeadally, E. Adi, Z. Baig, and I. A. Khan, “Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity,” IEEE Access, vol. 8, pp. 23817–23837, 2020, doi: 10.1109/ACCESS.2020.2968045.
R. Sarkis-Onofre, F. Catalá-López, E. Aromataris, and C. Lockwood, “How to properly use the PRISMA Statement,” Syst. Rev., vol. 10, no. 1, pp. 117, s13643-021-01671-z, Dec. 2021, doi: 10.1186/s13643-021-01671-z.
V. Welch et al., “Extending the PRISMA statement to equity-focused systematic reviews (PRISMA-E 2012): explanation and elaboration,” J. Clin. Epidemiol., vol. 70, pp. 68–89, Feb. 2016, doi: 10.1016/j.jclinepi.2015.09.001.
R. Briner and D. Denyer, “Systematic Review and Evidence Synthesis as a Practice and Scholarship Tool,” in Handbook of evidence-based management: Companies, classrooms and research, 2012, pp. 112–129. doi: 10.1093/oxfordhb/9780199763986.013.0007.
D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement,” Int. J. Surg., vol. 8, no. 5, pp. 336–341, Jan. 2010, doi: 10.1016/j.ijsu.2010.02.007.
M. Yampolskiy, P. Horvath, X. D. Koutsoukos, Y. Xue, and J. Sztipanovits, “Taxonomy for description of cross-domain attacks on CPS,” in Proceedings of the 2nd ACM international conference on High confidence networked systems, Philadelphia Pennsylvania USA: ACM, Apr. 2013, pp. 135–142. doi: 10.1145/2461446.2461465.
Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.
J. Heaton, “Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning,” Genet. Program. Evolvable Mach., vol. 19, no. 1, pp. 305–307, Jun. 2018, doi: 10.1007/s10710-017-9314-z.
J. Seymour and P. Tully, “Weaponizing data science for social engineering: Automated E2E spear phishing on Twitter,” 2016.
Apruzzese et al., “Cyber Law and Espionage Law as Communicating Vessels,” 2018.
L. A. E. Al-saeedi et al., “Artificial Intelligence and Cybersecurity in Face Sale Contracts: Legal Issues and Frameworks,” Mesopotamian J. CyberSecurity, vol. 4, no. 2, Art. no. 2, Aug. 2024, doi: 10.58496/MJCS/2024/0012.
J. Hong, T. Kim, J. Liu, N. Park, and S.-W. Kim, “Phishing URL Detection with Lexical Features and Blacklisted Domains,” in Adaptive Autonomous Secure Cyber Systems, S. Jajodia, G. Cybenko, V. S. Subrahmanian, V. Swarup, C. Wang, and M. Wellman, Eds., Cham: Springer International Publishing, 2020, pp. 253–267. doi: 10.1007/978-3-030-33432-1_12.
J. Gao, J. Lanchantin, M. L. Soffa, and Y. Qi, “Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers,” in 2018 IEEE Security and Privacy Workshops (SPW), May 2018, pp. 50–56. doi: 10.1109/SPW.2018.00016.
G. Wangen and A. Shalaginov, “Quantitative Risk, Statistical Methods and the Four Quadrants for Information Security,” in Risks and Security of Internet and Systems, C. Lambrinoudakis and A. Gabillon, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2016, pp. 127–143. doi: 10.1007/978-3-319-31811-0_8.
C. Yin, Y. Zhu, J. Fei, and X. He, “A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks,” IEEE Access, vol. 5, pp. 21954–21961, 2017, doi: 10.1109/ACCESS.2017.2762418.
B. Dolhansky et al., “The DeepFake Detection Challenge (DFDC) Dataset,” Oct. 27, 2020, arXiv: arXiv:2006.07397. Accessed: Mar. 10, 2024. [Online]. Available: http://arxiv.org/abs/2006.07397
M. Jagielski, A. Oprea, B. Biggio, C. Liu, C. Nita-Rotaru, and B. Li, “Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning,” Sep. 28, 2021, arXiv: arXiv:1804.00308. Accessed: Mar. 10, 2024. [Online]. Available: http://arxiv.org/abs/1804.00308
B. Biggio and F. Roli, “Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning,” Pattern Recognit., vol. 84, pp. 317–331, Dec. 2018, doi: 10.1016/j.patcog.2018.07.023.
I. Goodfellow, P. McDaniel, and N. Papernot, “Making machine learning robust against adversarial inputs,” Commun. ACM, vol. 61, no. 7, pp. 56–66, Jun. 2018, doi: 10.1145/3134599.
U. Kumar, S. Navaneet, N. Kumar, and S. C. Pandey, “Isolation of DDoS Attack in IoT: A New Perspective,” Wirel. Pers. Commun., vol. 114, no. 3, pp. 2493–2510, Oct. 2020, doi: 10.1007/s11277-020-07486-w.
A. A. Cain, M. E. Edwards, and J. D. Still, “An exploratory study of cyber hygiene behaviors and knowledge,” J. Inf. Secur. Appl., vol. 42, pp. 36–45, Oct. 2018, doi: 10.1016/j.jisa.2018.08.002.
Y. Wang, M. Singgih, J. Wang, and M. Rit, “Making sense of blockchain technology: How will it transform supply chains?,” Int. J. Prod. Econ., vol. 211, pp. 221–236, May 2019, doi: 10.1016/j.ijpe.2019.02.002.
S. Mishra, “Exploring the Impact of AI-Based Cyber Security Financial Sector Management,” Appl. Sci., vol. 13, no. 10, p. 5875, May 2023, doi: 10.3390/app13105875.
S. Tweneboah-Kodua, F. Atsu, and W. Buchanan, “Impact of cyberattacks on stock performance: a comparative study,” Inf. Comput. Secur., vol. 26, no. 5, pp. 637–652, Jan. 2018, doi: 10.1108/ICS-05-2018-0060.
M. Humayun, M. Niazi, N. Jhanjhi, M. Alshayeb, and S. Mahmood, “Cyber Security Threats and Vulnerabilities: A Systematic Mapping Study,” Arab. J. Sci. Eng., vol. 45, no. 4, pp. 3171–3189, Apr. 2020, doi: 10.1007/s13369-019-04319-2.
P. Bagó, “Cyber security and artificial intelligence,” Econ. Finance, vol. 10, no. 2, pp. 189–212, 2023, doi: 10.33908/EF.2023.2.5.
S. Varga, J. Brynielsson, and U. Franke, “Cyber-threat perception and risk management in the Swedish financial sector,” Comput. Secur., vol. 105, p. 102239, Jun. 2021, doi: 10.1016/j.cose.2021.102239.
L. A. Gordon, M. P. Loeb, W. Lucyshyn, and L. Zhou, “Empirical Evidence on the Determinants of Cybersecurity Investments in Private Sector Firms,” J. Inf. Secur., vol. 09, no. 02, pp. 133–153, 2018, doi: 10.4236/jis.2018.92010.
A. A. Darem, A. A. Alhashmi, T. M. Alkhaldi, A. M. Alashjaee, S. M. Alanazi, and S. A. Ebad, “Cyber Threats Classifications and Countermeasures in Banking and Financial Sector,” IEEE Access, vol. 11, pp. 125138–125158, 2023, doi: 10.1109/ACCESS.2023.3327016.
J.-H. Syu, J. C.-W. Lin, and G. Srivastava, “AI-Based Electricity Grid Management for Sustainability, Reliability, and Security,” IEEE Consum. Electron. Mag., vol. 13, no. 1, pp. 91–96, Jan. 2024, doi: 10.1109/MCE.2023.3264884.
L. Papadopoulos et al., “Protection of critical infrastructures from advanced combined cyber and physical threats: The PRAETORIAN approach,” Int. J. Crit. Infrastruct. Prot., vol. 44, p. 100657, Mar. 2024, doi: 10.1016/j.ijcip.2023.100657.
A. Shehu, M. Umar, and A. Aliyu, “Cyber Kill Chain Analysis Using Artificial Intelligence,” Asian J. Res. Comput. Sci., vol. 16, no. 3, pp. 210–219, Aug. 2023, doi: 10.9734/ajrcos/2023/v16i3357.
N. Abbas, T. Ahmed, S. H. U. Shah, M. Omar, and H. Park, “Investigating the applications of artificial intelligence in cyber security,” Scientometrics, vol. 121, pp. 1189–1211, 2019, doi: 10.1007/s11192-019-03222-9.
S. S. Ahmad and Krishna Prasad K, “An Artificial Intelligence (AI) Enabled Framework for Cyber Security Using Machine Learning Techniques,” 2023.
G. Blessing, A. Azeta, S. Misra, V. Osamor, L. F. Sanz, and V. Pospelova, “The Emerging Threat of Ai-driven Cyber Attacks: A Review,” Appl. Artif. Intell., vol. 36, 2022, doi: 10.1080/08839514.2022.2037254.
B. Fakiha, “Enhancing Cyber Forensics with AI and Machine Learning: A Study on Automated Threat Analysis and Classification,” Int. J. Saf. Secur. Eng., 2023, doi: 10.18280/ijsse.130412.
J. Lee, J. Kim, I. Kim, and K. Han, “Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles,” IEEE Access, vol. 7, pp. 165607–165626, 2019, doi: 10.1109/ACCESS.2019.2953095.
P. R. Sai and K. S. Niraja, “Cyber Threat Detection Based on Artificial Neural Networks,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 10, pp. 1469–1472, Oct. 2023, doi: 10.22214/ijraset.2023.56193.
K. V. S. Ram, “Detecting Cybersecurity Threats Using AI Network,” vol. 5, no. 5, 2023.
V. S. Sree, C. S. Koganti, S. K. Kalyana, and P. Anudeep, “Artificial Intelligence Based Predictive Threat Hunting In The Field of Cyber Security,” 2021 2nd Glob. Conf. Adv. Technol. GCAT, pp. 1–6, 2021, doi: 10.1109/GCAT52182.2021.9587507.
S. Xun, X. Li, and Y. Gao, “AITI: An Automatic Identification Model of Threat Intelligence Based on Convolutional Neural Network,” in Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence, in ICIAI ’20. New York, NY, USA: Association for Computing Machinery, Jun. 2020, pp. 20–24. doi: 10.1145/3390557.3394305.
R. Maurya, “Analyzing the Role of AI in Cyber Security Threat Detection & Prevention,” Int. J. Res. Appl. Sci. Eng. Technol., 2023, doi: 10.22214/ijraset.2023.56510.
A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, “Survey of intrusion detection systems: techniques, datasets and challenges,” Cybersecurity, vol. 2, 2019, doi: 10.1186/s42400-019-0038-7.
H. Wang et al., “Design and research of network security threat detection and traceability system based on AI,” vol. 12079, pp. 120790–120790, 2021, doi: 10.1117/12.2622727.
M. Alanezi and Aldabagh, An Immune Inspired Multilayer IDS. 2011. doi: 10.13140/RG.2.2.30769.02405.
E. Aghaei and E. Al-Shaer, “ThreatZoom: neural network for automated vulnerability mitigation,” Proc. 6th Annu. Symp. Hot Top. Sci. Secur., 2019, doi: 10.1145/3314058.3318167.
H. Alavizadeh, J. Jang, T. Alpcan, and S. Çamtepe, “A Markov Game Model for AI-based Cyber Security Attack Mitigation,” ArXiv, vol. abs/2107.09258, 2021, Accessed: Mar. 11, 2024. [Online]. Available: https://consensus.app/papers/markov-game-model-aibased-cyber-security-attack-alavizadeh/eed995d94a6255b7b9e6dc4f5a4fb8f2/
N. Duan et al., “Mitigation Strategies Against Cyberattacks on Distributed Energy Resources,” 2021 IEEE Power Energy Soc. Innov. Smart Grid Technol. Conf. ISGT, pp. 1–5, 2021, doi: 10.1109/ISGT49243.2021.9372173.
A. Jha, R. Bahuguna, S. Kathuria, G. Sunil, M. Gupta, and V. Pachouri, “Role of AI in Combating Cyber Terrorism,” 2023 4th Int. Conf. Smart Electron. Commun. ICOSEC, pp. 1156–1160, 2023, doi: 10.1109/ICOSEC58147.2023.10275910.
R. Meier, A. Lavrenovs, K. Heinäaro, L. Gambazzi, and V. Lenders, “Towards an AI-powered Player in Cyber Defence Exercises,” 2021 13th Int. Conf. Cyber Confl. CyCon, pp. 309–326, 2021, doi: 10.23919/CyCon51939.2021.9467801.
M. Fazelnia, I. Khokhlov, and M. Mirakhorli, “Attacks, Defenses, And Tools: A Framework To Facilitate Robust AI/ML Systems,” ArXiv, vol. abs/2202.09465, 2022, Accessed: Mar. 13, 2024. [Online]. Available: https://consensus.app/papers/attacks-defenses-tools-framework-facilitate-robust-aiml-fazelnia/f158fd5a4ea058958c911cb772e58bbf/
R. Stevens, D. Votipka, E. M. Redmiles, C. Ahern, and M. L. Mazurek, “Applied Digital Threat Modeling: It Works,” IEEE Secur. Priv., vol. 17, pp. 35–42, 2019, doi: 10.1109/MSEC.2019.2909714.
R. Raj, J. Kumar, and A. Kumari, “HOW AI USED TO PREVENT CYBER THREATS,” Int. Res. J. Comput. Sci., 2022, doi: 10.26562/irjcs.2022.v0907.002.
M. Alanezi and Aldabagh, Using Two levels danger model of the Immune System for Malware Detection. 2012. doi: 10.13140/RG.2.2.36221.61924.
R. R. Shanthi, N. K. Sasi, and P. Gouthaman, “A New Era of Cybersecurity: The Influence of Artificial Intelligence,” 2023 Int. Conf. Netw. Commun. ICNWC, pp. 1–4, 2023, doi: 10.1109/ICNWC57852.2023.10127453.
M. A. Khder, S. Shorman, D. A. Showaiter, A. Zowayed, and S. I. Zowayed, “Review Study of the Impact of Artificial Intelligence on Cyber Security,” 2023 Int. Conf. IT Innov. Knowl. Discov. ITIKD, pp. 1–6, 2023, doi: 10.1109/ITIKD56332.2023.10099788.
N. K. et Kumar et al., “AI in Cybersecurity: Threat Detection and Response with Machine Learning,” Tuijin JishuJournal Propuls. Technol., vol. 44, no. 3, Art. no. 3, Sep. 2023, doi: 10.52783/tjjpt.v44.i3.237.
P. Chandana and C. M. Gulzar, “Securing Cyberspace: A Comprehensive Journey through AI’s Impact on Cyber Security,” Tuijin JishuJournal Propuls. Technol., 2023, doi: 10.52783/tjjpt.v44.i2.136.
A. Ali et al., “The Effect of Artificial Intelligence on Cybersecurity,” 2023 Int. Conf. Bus. Anal. Technol. Secur. ICBATS, pp. 1–7, 2023, doi: 10.1109/ICBATS57792.2023.10111151.
M. Coeckelbergh, “Artificial Intelligence: Some ethical issues and regulatory challenges,” vol. 2019, pp. 31–34, 2019, doi: 10.26116/TECHREG.2019.003.
D. Jackson, S. Matei, and E. Bertino, “Artificial Intelligence Ethics Education in Cybersecurity: Challenges and Opportunities: a focus group report,” ArXiv, vol. abs/2311.00903, 2023, doi: 10.48550/arXiv.2311.00903.
P. Bago, “Cyber security and artificial intelligence,” Econ. Amp Finance, 2023, doi: 10.33908/ef.2023.2.5.
S. Chahal, “AI-Enhanced Cyber Incident Response and Recovery,” Int. J. Sci. Res. IJSR, 2023, doi: 10.21275/sr231003163025.
S. Gerke, T. Minssen, and G. Cohen, “Ethical and legal challenges of artificial intelligence-driven healthcare,” Artif. Intell. Healthc., 2020, doi: 10.1016/B978-0-12-818438-7.00012-5.
N. Naik et al., “Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?,” Front. Surg., vol. 9, 2022, doi: 10.3389/fsurg.2022.862322.
P. Timmers, “Ethics of AI and Cybersecurity When Sovereignty is at Stake,” Minds Mach., vol. 29, pp. 635–645, 2019, doi: 10.1007/s11023-019-09508-4.
A. G. Navdeep, “The Role of Ethics in Developing Secure Cyber-Security Policies,” Tuijin JishuJournal Propuls. Technol., 2023, doi: 10.52783/tjjpt.v43.i4.2346.
A. Hummelholm, “AI-based quantum-safe cybersecurity automation and orchestration for edge intelligence in future networks,” Eur. Conf. Cyber Warf. Secur., 2023, doi: 10.34190/eccws.22.1.1211.
S. Y. . Mohammed and M. . Aljanabi, “From Text to Threat Detection: The Power of NLP in Cybersecurity”, SHIFRA, vol. 2024, pp. 1–7, Jan. 2024, doi: 10.70470/SHIFRA/2024/001
R. Ramakrishnan, “The Future of Cybersecurity and Its Potential Threats,” Int. J. Res. Appl. Sci. Eng. Technol., 2023, doi: 10.22214/ijraset.2023.54603.
F. Farahmand, J. Grossklags, J. Mirkovic, and B. Newhouse, “Integrating Cybersecurity and Artificial Intelligence Research in Engineering and Computer Science Education,” IEEE Secur. Priv., vol. 19, pp. 104–110, 2021, doi: 10.1109/MSEC.2021.3103460.
I. Molloy, J. Rao, and M. Stoecklin, “AI vs. AI: Exploring the Intersections of AI and Cybersecurity,” Proc. 2021 ACM Workshop Secur. Priv. Anal., 2021, doi: 10.1145/3445970.3456286.
I. H. Sarker, “Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview,” Secur. Priv., vol. 6, 2023, doi: 10.1002/spy2.295.
M. Corbett and S. Sajal, “AI in Cybersecurity,” 2023 Intermt. Eng. Technol. Comput. IETC, pp. 334–338, 2023, doi: 10.1109/IETC57902.2023.10152034.
J. Srinivas, A. Das, and N. Kumar, “Government regulations in cyber security: Framework, standards and recommendations,” Future Gener Comput Syst, vol. 92, pp. 178–188, 2019, doi: 10.1016/j.future.2018.09.063.
R. Clarke, “Regulatory alternatives for AI,” Comput Law Secur Rev, vol. 35, pp. 398–409, 2019, doi: 10.1016/J.CLSR.2019.04.008.
M. Burhanuddin, “Secure and Scalable Quantum Cryptographic Algorithms for Next-Generation Computer Networks”, KHWARIZMIA, vol. 2023, pp. 95–102, Jul. 2023, doi: 10.70470/KHWARIZMIA/2023/009
L. Hussain, “Fortifying AI Against Cyber Threats Advancing Resilient Systems to Combat Adversarial Attacks”, EDRAAK, vol. 2024, pp. 28–33, Mar. 2024, doi: 10.70470/EDRAAK/2024/004
E. Grames, A. N. Stillman, M. Tingley, and C. Elphick, “An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks,” Methods Ecol. Evol., vol. 10, pp. 1645–1654, 2019, doi: 10.1111/2041-210X.13268.
A. Booth et al., “Structured methodology review identified seven (RETREAT) criteria for selecting qualitative evidence synthesis approaches.,” J. Clin. Epidemiol., vol. 99, pp. 41–52, 2018, doi: 10.1016/j.jclinepi.2018.03.003.
V. Garousi and M. Felderer, “Experience-based guidelines for effective and efficient data extraction in systematic reviews in software engineering,” Proc. 21st Int. Conf. Eval. Assess. Softw. Eng., 2017, doi: 10.1145/3084226.3084238.
H. Li, J. Wu, H. Xu, G. Li, and M. Guizani, “Explainable Intelligence-Driven Defense Mechanism Against Advanced Persistent Threats: A Joint Edge Game and AI Approach,” IEEE Trans. Dependable Secure Comput., vol. 19, pp. 757–775, 2022, doi: 10.1109/tdsc.2021.3130944.