The Most Recent Advances and Uses of AI in Cybersecurity
Keywords:
ethical issues, data privacy, artificial intelligence, cybersecurity, threat detection, automation, machine learning, natural language processing, Internet of Things, block chain, quantum computing, and case studies, security postureAbstract
The incorporation of modern technology into cybersecurity measures has become imperative due to the growing sophistication and frequency of cyber threats. This review delves into the most recent developments and uses of artificial intelligence (AI) in cybersecurity, emphasizing how it may improve threat detection, automate responses, and give businesses useful insights. The conversation covers the present state of artificial intelligence applications, such as automated threat intelligence, natural language processing, and machine learning, and it uses case studies from a variety of industries, including retail, healthcare, and finance, to demonstrate how effective they are. Important implementation hurdles for AI, such as data privacy difficulties, ethical concerns, and the high rate of false positives, are also covered, highlighting the necessity for enterprises to carefully manage these challenges. In terms of the future, the analysis points to several interesting avenues for AI in cybersecurity, such as enhanced automation, better predictive capabilities, and integration with cutting-edge innovations like quantum computing, block chain, and the Internet of Things (IoT). The review emphasizes how AI has the ability to completely change cybersecurity procedures and emphasizes how crucial it is to solve ethical and practical issues in order to reap the full benefits of this technology. Organizations may improve their cybersecurity posture and effectively respond to a changing threat landscape by implementing AI-driven solutions and cultivating a culture of continuous learning and adaptation.
References
REFERENCES
G. Dhayanidhi, "Research on IoT threats & implementation of AI/ML to address emerging cybersecurity issues in IoT with cloud computing," 2022.
V. Mallikarjunaradhya, A. S. Pothukuchi, and L. V. Kota, "An overview of the strategic advantages of AI-powered threat intelligence in the cloud," Journal of Science & Technology, vol. 4, no. 4, pp. 1-12, 2023.
W. Ahmad, A. Rasool, A. R. Javed, T. Baker, and Z. Jalil, "Cyber security in IoT-based cloud computing: A comprehensive survey," Electronics, vol. 11, no. 1, p. 16, 2021.
B. R. Maddireddy and B. R. Maddireddy, "Cybersecurity Threat Landscape: Predictive Modelling Using Advanced AI Algorithms," International Journal of Advanced Engineering Technologies and Innovations, vol. 1, no. 2, pp. 270-285, 2022.
F. Tao, M. S. Akhtar, and Z. Jiayuan, "The future of artificial intelligence in cybersecurity: A comprehensive survey," EAI Endorsed Transactions on Creative Technologies, vol. 8, no. 28, pp. e3-e3, 2021.
N. Mohamed, "Current trends in AI and ML for cybersecurity: A state-of-the-art survey," Cogent Engineering, vol. 10, no. 2, p. 2272358, 2023.
P. Radanliev et al., "Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial Internet of things and industry 4.0 supply chains," Cybersecurity, vol. 3, pp. 1-21, 2020.
R. P. Reddy and A. K. R. Ayyadapu, "DEFENDING THE CLOUD: HOW AI AND ML ARE REVOLUTIONIZING CYBERSECURITY," Journal of Research Administration, vol. 1, no. 2, pp. 83-94, 2019. Vol 1, Issue 8, August 2024 https://ijstindex.com/index.php/ijst
S. Rawat, "Navigating the Cybersecurity Landscape: Current Trends and Emerging Threats," Journal of Advanced Research in Library and Information Science, vol. 10, no. 3, pp. 13-19, 2023.
S. S. Gill et al., "AI for next-generation computing: Emerging trends and future directions," Internet of Things, vol. 19, p. 100514, 2022.
S. Al-Mansoori and M. B. Salem, "The role of artificial intelligence and machine learning in shaping the future of cybersecurity: trends, applications, and ethical considerations," International Journal of Social Analytics, vol. 8, no. 9, pp. 1-16, 2023.
J. Kinyua and L. Awuah, "AI/ML in Security Orchestration, Automation and Response: Future Research Directions," Intelligent Automation & Soft Computing, vol. 28, no. 2, 2021.
J.P. Ferreira, V.C. Ferreira, S.L. Nogueira, J.M. Faria, and J.A. Afonso, “A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming,” Information, vol. 15, no. 4, pp. 1-15, 2024. https://doi.org/10.3390/info15040213
P. Tang, Q. Liang, H. Li, and Y. Pang, “Application of Internet-of-Things Wireless Communication Technology in Agricultural Irrigation Management: A Review,” Sustainability, vol. 16, no. 9, pp. 1–19, 2024. https://doi.org/10.3390/su16093575
S.R. Raja, B. Subashini, and R.S. Prabu, “5G Technology in Smart Farming and Its Applications,” In: Balasubramanian, S. Natarajan, G. Chelliah, P.R. (eds) Intelligent Robots and Drones for Precision Agriculture. Signals and Communication Technology. Springer, 2024. https://doi.org/10.1007/978-3-031-51195-0_12
G. K. Akella, S. Wibowo, S. Grandhi, and S. Mubarak, “A Systematic Review of Blockchain Technology Adoption Barriers and Enablers for Smart and Sustainable Agriculture,” Big Data and Cognitive Computing, vol. 7, no. 2, pp. 1–22, 2023. https://doi.org/10.3390/bdcc7020086
Aliyu, and J. Liu, “Blockchain-Based Smart Farm Security Framework for the Internet of Things,” Sensors, vol. 23, no. 18, pp. 1–13, 2023. https://doi.org/10.3390/s23187992
O. H. Abdelkader, H. Bouzebiba, D. Pena, and A. P. Aguiar, “Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture,” Sensors, vol. 23, no. 18, pp. 1–20, 2023. https://doi.org/10.3390/s23187670
M. Escribà-Gelonch, S. Liang, P. van Schalkwyk, I. Fisk, N. V. D. Long, and V. Hessel, “Digital Twins in Agriculture: Orchestration and Applications,” Journal of agricultural and food chemistry, vol. 72, no. 19, pp. 10737– 10752, 2024. https://doi.org/10.1021/acs.jafc.4c01934
Y. Kalyani, L. M. Vorster, R. Whetton, and R. W. Collier, “Application Scenarios of Digital Twins for Smart Crop Farming through Cloud–Fog–Edge Infrastructure,” Future Internet, vol. 16, no. 3, pp. 1–16, 2024. https://doi.org/10.3390/fi16030100
C. Tagarakis, L. Benos, G. Kyriakarakos, S. Pearson, C. G. Sørensen, and D. Bochtis, “Digital Twins in Agriculture and Forestry: A Review,” Sensors, vol. 24, no. 10, pp. 1–26, 2024. https://doi.org/10.3390/s24103117
N. Peladarinos, D. Piromalis, V. Cheimaras, E. Tserepas, R. A. Munteanu, and P. Papageorgas, “Enhancing smart Agriculture by Implementing Digital Twins: A Comprehensive review,” Sensors, vol. 23, no. 16, pp. 1–38, 2023. https://doi.org/10.3390/s23167128
W. Purcell, and T. Neubauer, “Digital Twins in Agriculture: A State-of-the-art review,” Smart Agricultural Technology, vol. 3, pp. 1–11, 2023. https://doi.org/10.1016/j.atech.2022.100094
P. Catala-Roman, E. A. Navarro, J. Segura-Garcia, and M. Garcia-Pineda, “Harnessing Digital Twins for Agriculture 5.0: A Comparative Analysis of 3D Point Cloud Tools,” Applied Sciences, vol. 14, no. 5, pp. 1–19, 2024. https://doi.org/10.3390/app14051709
L. Wang, “Digital Twins in Agriculture: A Review of Recent Progress and Open Issues,” Electronics, vol. 13, no. 11, pp. 1–26, 2024. https://doi.org/10.3390/electronics13112209
M. Otieno, “An extensive survey of smart agriculture technologies: Current security posture,” World Journal of Advanced Research and Reviews, vol. 18, no. 3, pp. 1207–1231, 2023. https://doi.org/10.30574/wjarr.2023.18.3.1241
T. Ganetsos, Α. Κάνταρος, N. Gioldasis, and K. Brachos, “Applications of 3D Printing and Illustration in Industry,” 2023 17th International Conference on Engineering of Modern Electric Systems (EMES), Oradea, Romania, 09-10 June 2023, pp. 1–4. https://doi.org/10.1109/emes58375.2023.10171656
P. Lakkala, S. R. Munnangi, S. Bandari, and M. A. Repka, “Additive manufacturing technologies with emphasis on stereolithography 3D printing in pharmaceutical and medical applications: A review,” International Journal of Pharmaceutics: X, vol. 5, pp. 1–16, 2023. https://doi.org/10.1016/j.ijpx.2023.100159
M. Javaid, A. Haleem, R. P. Singh, R. Suman, and S. Rab, “Role of additive manufacturing applications towards environmental sustainability,” Advanced Industrial and Engineering Polymer Research, vol. 4, no. 4, pp. 312–322, 2021. https://doi.org/10.1016/j.aiepr.2021.07.005
D. J. S. Agron, and W. S. Kim, “3D Printing Technology: Role in Safeguarding Food Security,” Analytical chemistry, vol. 96, no. 11, pp. 4333–4342, 2024. https://doi.org/10.1021/acs.analchem.3c05190
D. Shikha, K. A. V. Sindhura, M. Rastogi, B. Saritha, S. N. Satapathy, S. Srivastava, and A. K. Kurdekar, “A Review on Propelling Agricultural Practices with Biotechnology into a New Era,” Journal of Advances in Biology and Biotechnology, vol. 27, no. 3, pp. 99–111, 2024. https://doi.org/10.9734/jabb/2024/v27i3725
L. Badadyan, “Research and Recent Achievements in Agriculture and Biotechnology with Innovative Technologies Application,” E3S Web of Conferences, vol. 493, pp. 1–11, 2024. https://doi.org/10.1051/e3sconf/202449301010
S. Gorjian, O. Fakhraei, A. Gorjian, A. Sharafkhani, and A. Aziznejad, “Sustainable Food and Agriculture: Employment of Renewable Energy Technologies,” Current Robotics Reports, vol. 3, no. 3, pp. 153–163, 2022. https://doi.org/10.1007/s43154-022-00080-x
Bathaei, and D. Štreimikienė, “Renewable Energy and Sustainable Agriculture: Review of Indicators,” Sustainability, vol. 15, no. 19, pp. 1–24, 2023. https://doi.org/10.3390/su151914307
S. Mandal, A. Yadav, F. A. Panme, K. M. Devi, and S. K. SM, “Adaption of smart applications in agriculture to enhance production,” Smart Agricultural Technology, vol. 7, pp. 1–11, 2024. https://doi.org/10.1016/j.atech.2024.100431
M. M. Mijwil, O. Adelaja, A. Badr, G. Ali, B. A. Buruga, and P. Thapa, “Innovative Livestock: A Survey of Artificial Intelligence Techniques in Livestock Farming Management,” Wasit Journal of Computer and Mathematics Science, vol. 2, no. 4, pp. 99–106, 2023. https://doi.org/10.31185/wjcms.206
S. Majumder, Y. Khandelwal, and K, Sornalakshmi. “Computer Vision and generative AI for yield prediction in digital agriculture,” 2024 2nd International Conference on Networking and Communications (ICNWC), 02-04 April 2024, Chennai, India, pp.1–6. https://doi.org/10.1109/icnwc60771.2024.10537337
F. Salehi, “The Role of Artificial Intelligence in Revolutionizing the Agriculture Industry in Canada,” Asian Journal of Research and Review in Agriculture, vol. 6, no. 1, pp. 70–78, 2024
M. Del-Coco, M. Leo, and P. Carcagnì, “Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?,” Information, vol. 15, no. 6, pp. 1–23, 2024. https://doi.org/10.3390/info15060306
P. Thongnim, V. Yuvanatemiya, and P. Srinil, “Smart Agriculture: Transforming Agriculture with Technology,” In Communications in computer and information science. Springer Nature, pp. 362–376, 2024. https://doi.org/10.1007/978-981-99-7240-1_29
E. E. K. Senoo, L. Anggraini, J. A. Kumi, L. B. Karolina, E. Akansah, H. A. Sulyman, Mendonça, I. and M. Aritsugi, “IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities,” Electronics, vol. 13, no. 10, pp. 1–89, 2024. https://doi.org/10.3390/electronics13101894
K. Bezas, and F. Filippidou, “The Role of Artificial Intelligence and Machine Learning in Smart and Precision Agriculture,” Indonesian Journal of Computer Science, vol. 12, no. 4, pp. 1576–1588, 2023.
B. Subedi, and G. Sharma, “Smart Agriculture: Components, Processes, Challenges, and Future Perspectives,” Journal of Data Mining and Management, vol. 8, no. 2, pp. 28–40, 2023.
M. Papri, D. Subhankar, C. Arindam, and D. Santosh, “Advanced Technologies in Smart Agriculture: Applications and Challenges,” In M. Sagar, G. J. Dinkar, and D. Santosh (Eds). Advances in Agricultural Technology. Griffon, pp. 81-99, 2023
S. K. Phang, T. Chiang, A. Happonen, and M. M. L. Chang, “From Satellite to UAV-Based Remote Sensing: A Review on Precision Agriculture,” IEEE Access, vol. 11, pp. 127057–127076, 2023. https://doi.org/10.1109/access.2023.3330886
S. Alam, “Security concerns in smart agriculture and blockchain-based solution,” 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON), Raigarh, Chhattisgarh, India, 08-10 February 2023, pp. 1–6. https://doi.org/10.1109/otcon56053.2023.10113953
M. R. M. Kassim, “Applications of IoT and blockchain in smart agriculture: architectures and challenges,” 2022 IEEE International Conference on Computing (ICOCO), Kota Kinabalu, Malaysia, 14-16 November 2022, pp. 253-258. https://doi.org/10.1109/icoco56118.2022.10031697
M. Niu, and T. Shi, “Application and Development of Smart Agriculture based on Internet of Things,” Frontiers in Computing and Intelligent Systems, vol. 3, no. 3, pp. 55–58, 2023. https://doi.org/10.54097/fcis.v3i3.8566
E. Bouali, M. R. Abid, E. Boufounas, T. A. Hamed, and D. Benhaddou, “Renewable Energy Integration into Cloud and IoT-Based Smart Agriculture,” IEEE Access, vol. 10, pp. 1175–1191, 2022. https://doi.org/10.1109/access.2021.3138160
R. Rani, J. Sahoo, S. Bellamkonda, S. Kumar, and S. K. Pippal, “Role of Artificial Intelligence in Agriculture: An Analysis and Advancements with Focus on Plant Diseases,” IEEE Access, vol. 11, pp. 137999–138019, 2023. https://doi.org/10.1109/access.2023.3339375
J. Kaur, S. M. H. Fard, M. Amiri-Zarandi, and R. Dara, “Protecting farmers’ data privacy and confidentiality: Recommendations and considerations,” Frontiers in Sustainable Food Systems, vol. 6, pp. 1–9, 2022. https://doi.org/10.3389/fsufs.2022.903230
G. Ali, M. M. Mijwil, B. A. Buruga, and M. Abotaleb, “A Comprehensive Review on Cybersecurity Issues and Their Mitigation Measures in FinTech,” Iraqi Journal for Computer Science and Mathematics, vol. 5, no. 3, pp. 45–91, 2024. https://doi.org/10.52866/ijcsm.2024.05.03.004
V. Kumar, K. V. Sharma, N. Kedam, A. Patel, T. R. Kate, and U. Rathnayake, “A comprehensive review on smart and sustainable agriculture using IoT technologies,” Smart Agricultural Technology, vol. 8, pp. 1–23, 2024. https://doi.org/10.1016/j.atech.2024.100487
Dargaoui, M. Azrour, A. E. Allaoui, A. Guezzaz, S. Benkirane, A. Alabdulatif, and F. Amounas, “Internet-ofThings-Enabled Smart Agriculture: security enhancement approaches,” 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Fez, Morocco, 16-17 May 2024, pp. 1–5. https://doi.org/10.1109/iraset60544.2024.10548705
S. Rudrakar, and P. Rughani, “IoT Based Agriculture (AG-IoT): A detailed study on architecture, security and forensics,” Information Processing in Agriculture, pp. 1–18, 2023. https://doi.org/10.1016/j.inpa.2023.09.002
O. Friha, M. A. Ferrag, Λ. Μαγλαράς, and L. Shu, “Digital Agriculture Security: Aspects, Threats, Mitigation Strategies, and Future Trends,” IEEE Internet of Things Magazine, vol. 5, no. 3, pp. 82–90, 2022. https://doi.org/10.1109/iotm.001.2100164
A. Yazdinejad, B. Zolfaghari, A. Azmoodeh, A. Dehghantanha, A. Dehghantanha, E. D. G. Fraser, A. G. Green, C. Russell, and E. Duncan, “A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures,” Applied Sciences, vol. 11, no. 16, pp. 1–24, 2021. https://doi.org/10.3390/app11167518
Qiu, H., Dong, T., Zhang, T., Lu, J., Memmi, G. and Qiu, M. (2020) Adversarial Attacks against Network Intrusion Detection in IoT Systems. IEEE Internet of Things Journal, 8, 10327-10335. https://doi.org/10.1109/JIOT.2020.3048038
Rosenberg, I., Shabtai, A., Elovici, Y. and Rokach, L. (2021) Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain. ACM Computing Surveys, 54, Article No. 108. https://doi.org/10.1145/3453158
Abdelkhalek, M., Ravikumar, G. and Govindarasu, M. (2022) ML-Based Anomaly Detection System for DER Communication in Smart Grid. 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), New Orleans, 24-28 April 2022, 1-5. https://doi.org/10.1109/ISGT50606.2022.9817481
T. Wolf et al., ‘‘Transformers: State-of-the-art natural language processing,’’in Proc. Conf. Empirical Methods Natural Lang. Process., Syst.Demonstrations, 2020, pp. 38–45.
M. M. Yamin, M. Ullah, H. Ullah, and B. Katt, ‘‘Weaponized AI for cyberattacks,’’ J. Inf. Secur. Appl., vol. 57, Mar. 2021, Art. No. 102722.
] S. Malhotra, G. Rajender, M. S. Bhatia, and T. B. Singh, “Effects of picture exchange communication system on communication and behavioral anomalies in autism,” Indian J. Psychol. Med., vol. 32, no. 2, pp. 141–143, Jul. 2010.
A. Naseer, H. Naseer, A. Ahmad, S. B. Maynard, and A. Masood Siddiqui, “Realtime analytics, incident response process agility and enterprise cybersecurity performance: A contingent resource-based analysis,” Int. J. Inf. Manage., vol. 59, p. 102334, Aug. 2021.
T. Novak, A. Treytl, and P. Palensky, “Common Approach to Functional Safety and System Security in Building Automation and Control Systems,” in 2007 IEEE Conference on Emerging Technologies and Factory Automation (EFTA 2007), 2007, pp. 1141–1148.
I. Mohanraj, K. Ashokumar, and J. Naren, “Field Monitoring and Automation Using IOT in Agriculture Domain,” Procedia Comput. Sci., vol. 93, pp. 931–939, Jan. 2016.
W. Issa, N. Moustafa, B. Turnbull, N. Sohrabi, and Z. Tari, “Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey,” ACM Comput. Surv., vol. 55, no. 9, pp. 1–43, Jan. 2023.
