Enhancing Cyber security through Artificial Intelligence: Opportunities and Challenges

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Abstract

 As cyber threats come to be more sophisticated and frequent, there’s a growing want for solutions to strengthen cyber security defences. on this
 record, we’ll explore how artificial intelligence (AI) can be used to bolster
 virtual defences against cyber security attacks. AI may be used in a diffusion  of methods in cybersecurity, from anomaly detection and danger intelligence analysis to behavioural analytics. We’ll observe how AI can assist companies discover and mitigate cyber threats, examine huge quantities of records quickly,  identify subtle anomalies that could be assaults, and adapt to changing hazard  environments. We’ll also have a look at the demanding situations and troubles  that come with AI in cybersecurity frameworks, from statistics privateness  and algorithm bias to adversarial attacks and the ethical ramifications of AI  decision-making inside safety operations.[1] And we’ll talk why it’s crucial  for groups to be transparent and accountable whilst deploying AI structures,  so that believe and self-belief can be constructed. From industry specialists to  academic studies to case studies, right here’s a have a look at what’s going on  in cybersecurity nowadays and where we’re headed in the destiny. Ultimately,  this document serves as a complete useful resource for policymakers,  cybersecurity specialists, and technology fans seeking to leverage AI to
 safeguard digital assets and mitigate the ever-evolving cyber threats landscape.  [2] by embracing AI-pushed improvements responsibly, groups can support  their cybersecurity posture and shield against emerging cyber risks in an  increasing number of interconnected international.

References

Bai, J., Wu, Y., Wang, G., Yang, S. X., & Qiu, W. (2006). A very distinctive intrusion detection model based on multilayer self-organizing maps and principal part analysis. In Advances in Neural Networks. Springer.

Abuadbba, A., & Salah, K. (2019). Machine learning based cybersecurity intrusion detection: Techniques, applications, and future directions. Journal of King Saud University – Computer and Information Sciences.

Mittal, S., & Sharma, S. (2020). Cybersecurity: A review of artificial intelligence, machine learning, and big data-enabled technologies. Journal of Big Data.

Dhanalakshmi, R., & Srinivasan, K. (2020). A review on machine learning approaches in cybersecurity. Journal of Network and Computer Applications.

Alsharif, M. H., Mahmoud, Q. H., & Safa, N. S. (2020). Cyber security threats and challenges: A comprehensive survey. Journal of King Saud University – Computer and Information Sciences.

Rathore, S., Sharma, A., & Park, J. H. (2020). Artificial intelligence and machine learning for secure cybersecurity paradigms: A systematic review. Journal of Ambient Intelligence and Humanized Computing.

Bostrom, N. (2015). TED Talk on Artificial Intelligence. Retrieved from: https://en.tiny.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are

Lunt, T. F., & Jagannathan, R. (1988). An example intrusion detection system. Proceedings of the IEEE Conference on Security and Privacy.

Panimalar, A., Giri, P. U., & Khan, S. (2018). Artificial Intelligence techniques in cyber security. International Research Journal of Engineering and Technology, 5(3).

Kotenko, I., & Ulanov, A. (2007). Multi-agent framework for simulation of adaptive cooperative defence against internet attacks. Lecture Notes in Computer Science, 4476, 212–228. https://doi.org/10.1007/978-3-540-72839-9_18

Shankrapani, M. K., Ramamoorthy, S., Movva, R. S., & Mukamalla, S. (2011). Malware detection using assembly and API call sequences. Journal in Computer Virology, 7(2), 107–119. https://doi.org/10.1007/s11416-010-0141-5

Venkatesh, G. K., Nadarajan, R. A., & Botnet, H. (2017). HTTP Botnet Detection using Adaptive Learning Rate Multilayer Feed-forward Neural Network. HAL Archive (hal-01534315).

Aarthi, J. Design of Advanced Encryption Standard (AES) based Rijndael Algorithm.

Rosenblatt, F. (1957). The Perceptron – A perceiving and recognizing automaton. Cornell Aeronautical Laboratory, Report 85, 460–461. https://doi.org/85-460-1