Securing the Future: AI-Driven Cyber Defenses in a Hyperconnected World |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-10 |
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| Year of Publication : 2024 | ||
| Authors : Sriharsha Daram | ||
| DOI : 10.14445/22312803/IJCTT-V72I10P124 | ||
How to Cite?
Sriharsha Daram, "Securing the Future: AI-Driven Cyber Defenses in a Hyperconnected World," International Journal of Computer Trends and Technology, vol. 72, no. 10, pp. 173-182, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I10P124
Abstract
In the current world, where most activities entail the use of technology, the increasing challenge of fighting cyber threats is complex. The number of devices, along with the usage of the cloud and IoT, has skyrocketed within the past years, and this has given a long list of opportunities for hackers. At the same time, conventional security measures fail to adapt to the speed of the process. This paper discusses the change Artificial Intelligence (AI) brings to present-day cybersecurity measures. AI has the ability to prevent cyber threats by using big data analytics, ML, NLP, and deep learning techniques to identify patterns and trends, making it capable of a proactive defense from developing threats in consideration of the ever-evolving threat environment. The first part exposes the evolution of cyber threats and threats, describing how current security measures are enough to combat complex attacks such as APTs, ransomware, and zero-day exploits. Subsequently, the paper reflects upon the development of AI integration in cybersecurity, which started with using AI in malware detection and signature-based cybersecurity systems. It propelled itself into AI-driven threat intelligence and behavioral analytics and AI-driven automated incident response. Under the methodology area, the authors explain how they adapted various AI-based cybersecurity measures and how they address data gathering, preparation, model identification, model building, and model deployment. Examples of how AI has helped to reduce breaches and the time required to respond to incidents in areas like finance, healthcare, and defense will be used. The study’s findings and analysis shall focus on parameters like the overall detection rate, observed false alarm rates, and time taken to react. In the final section, the prospects for applying AI in relation to cybersecurity and its further development will be reviewed, focusing on such aspects as ethically relevant ones and the use of explainability of AI systems (XAI) to develop more transparent and trustworthy systems.
Keywords
Artificial Intelligence, Cybersecurity, Machine Learning, Threat Detection, Explainable AI.
Reference
[1] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin, ““Why Should I Trust You?” Explaining the Predictions of Any Classifier,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135-1144, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Mohiuddin Ahmed, Abdun Naser Mahmood, and Jiankun Hu, “A Survey of Network Anomaly Detection Techniques,” Journal of Network and Computer Applications, vol. 60, pp. 19-31, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[3] G.N. Willard, “Understanding the Co-Evolution of Cyber Defenses and Attacks to Achieve Enhanced Cybersecurity,” Journal of Information Warfare, vol. 14, no. 2, pp. 16-30, 2015.
[Google Scholar] [Publisher Link]
[4] Ashar Aziz, “The Evolution of Cyber-Attacks and Next Generation Threat Protection,” RSA Conference, 2013.
[Google Scholar]
[5] Bhargava Reddy Maddireddy, and Bharat Reddy Maddireddy, “Evolutionary Algorithms in AI-Driven Cybersecurity Solutions for Adaptive Threat Mitigation,” International Journal of Advanced Engineering Technologies and Innovations, vol. 1, no. 2, pp. 17-43, 2021.
[Google Scholar] [Publisher Link]
[6] Ibra Him, “Innovating Cyber Defense: AI and ML for Next-Gen Threats,” 2022.
[Google Scholar]
[7] Anthony Donald, and Junaid Iqbal, Implementing Cyber Defense Strategies: Evolutionary Algorithms, Cyber Forensics, and AI-Driven Solutions for Enhanced Security.
[Google Scholar]
[8] Joseph M. Hatfield, “Social Engineering in Cybersecurity: The Evolution of a Concept,” Computers & Security, vol. 73, pp. 102-113, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Alicia An, “The Evolution of Cyber Security Threats in the Digital Age,” International Journal of Business Management and Visuals, vol. 5, no. 2, 2022.
[Google Scholar] [Publisher Link]
[10] Giovanni Apruzzese et al., “The Role of Machine Learning in Cybersecurity,” Digital Threats: Research and Practice, vol. 4, no. 1, pp. 1-38, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yadigar N. Imamverdiyev, and Fargana J. Abdullayeva, “Deep Learning in Cybersecurity: Challenges and Approaches,” International Journal of Cyber Warfare and Terrorism, vol. 10, no. 2, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] N. Sirisha et al., “IoT-based Data Quality and Data Preprocessing of Multinational Corporations,” The Journal of High Technology Management Research, vol. 34, no. 2, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Rajesh Gupta et al., “Machine Learning Models for Secure Data Analytics: A Taxonomy and Threat Model,” Computer Communications, vol. 153, pp. 406-440, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Arvid Kok et al., “Cyber Threat Prediction with Machine Learning,” Information & Security, vol. 47, no. 2, pp. 203-220, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Yu Liu et al., “Toward a Better Understanding of Model Validation Metrics,” Journal of Mechanical Design, vol. 133, no. 7, pp. 1-13, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Meraj Farheen Ansari et al., “The Impact and Limitations of Artificial Intelligence in Cybersecurity: A Literature Review,” International Journal of Advanced Research in Computer and Communication Engineering, 2022.
[Google Scholar] [Publisher Link]
[17] Kriangkrai Limthong et al., “Unsupervised Learning Model for Real-time Anomaly Detection in Computer Networks,” IEICE Transactions on Information and Systems, vol. 97, no. 8, pp. 2084-2094, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ali Bou Nassif et al., “Machine Learning for Anomaly Detection: A Systematic Review,” IEEE Access, vol. 9, pp. 78658-78700, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] B.H. Thacker et al., “Concepts of Model Verification and Validation,” General and Miscellaneous, vol. 36, no. 12, pp. 1-41, 2004.
[Google Scholar] [Publisher Link]
[20] Meghna Manoj Nair, Atharva Deshmukh, and Amit Kumar Tyagi, “Artificial Intelligence for Cyber Security: Current Trends and Future Challenges,” Automated Secure Computing for Next‐Generation Systems, 2023.
[CrossRef] [Google Scholar] [Publisher Link]