A Comprehensive Review of AI Applications in Cybersecurity

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Published: Mar 23, 2024

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Siva Subrahmanyam Balantrapu

Abstract

The rapid advancement of artificial intelligence (AI) technologies has ushered in a new era in cybersecurity, offering innovative solutions to combat an increasingly complex threat landscape. This research paper presents a comprehensive review of AI applications in cybersecurity, exploring various techniques, methodologies, and real-world implementations. We examine key AI technologies, including machine learning, natural language processing, and deep learning, and their effectiveness in threat detection, incident response, and vulnerability management. The paper categorizes AI applications into areas such as intrusion detection systems (IDS), malware detection, phishing prevention, and behavioral analytics, highlighting case studies that demonstrate successful implementations across different sectors. Furthermore, we address the challenges and limitations of integrating AI into cybersecurity frameworks, including concerns related to data privacy, algorithmic bias, and the need for interpretability in AI models. By synthesizing findings from current literature and industry practices, this review underscores the transformative potential of AI in enhancing cybersecurity defenses while emphasizing the importance of ethical considerations and ongoing research to optimize AI-driven security solutions. Ultimately, the paper serves as a valuable resource for practitioners, researchers, and policymakers seeking to understand the role of AI in shaping the future of cybersecurity.

Article Details

How to Cite
A Comprehensive Review of AI Applications in Cybersecurity. (2024). International Machine Learning Journal and Computer Engineering, 7(7). https://mljce.in/index.php/Imljce/article/view/39
Issue
Vol. 7 No. 7 (2024): IMLJCE
Section
Articles

How to Cite

A Comprehensive Review of AI Applications in Cybersecurity. (2024). International Machine Learning Journal and Computer Engineering, 7(7). https://mljce.in/index.php/Imljce/article/view/39

References

Adams, J. (2019). Cybersecurity in Critical Infrastructure: Challenges and Solutions. Journal of Critical Infrastructure Protection, 10(2), 45-58.

Barranco, M., & Sanchez-Anguix, V. (2020). Machine Learning for Anomaly Detection in Cyber-Physical Systems: A Review. IEEE Transactions on Industrial Informatics, 16(6), 3872-3881.

Chen, C., & Liu, C. (2018). Anomaly Detection in Cyber-Physical Systems Using Machine Learning Techniques: A Review. IEEE Transactions on Industrial Electronics, 65(5), 4399-4409.

Department of Homeland Security. (2021). Critical Infrastructure Cybersecurity: A Review of Policies and Practices. Washington, DC: Government Printing Office.

European Union Agency for Cybersecurity. (2020). Machine Learning for Anomaly Detection in Critical Infrastructure: Challenges and Opportunities. Brussels, Belgium: Publications Office of the European Union.

Federal Energy Regulatory Commission. (2019). Cybersecurity Considerations for Critical Infrastructure Protection. Washington, DC: Government Printing Office.

International Organization for Standardization. (2019). ISO/IEC 27001: Information Security Management Systems - Requirements. Geneva, Switzerland: ISO.

Jajodia, S., Subrahmanian, V., & Swarup, V. (Eds.). (2017). Handbook of SCADA/Control Systems Security. Boca Raton, FL: CRC Press.

Jones, T., & O’Neill, J. (2018). Cybersecurity Challenges in Critical Infrastructure Protection: A Case Study of the Energy Sector. Journal of Cybersecurity, 3(2), 189-203.

Vegesna, V. V. (2018). Analysis of Artificial Intelligence Techniques for Network Intrusion Detection and Intrusion Prevention for Enhanced User Privacy. Asian Journal of Applied Science and Technology (AJAST) Volume, 2, 315-330.

Vegesna, V. V. (2019). Investigations on Different Security Techniques for Data Protection in Cloud Computing using Cryptography Schemes. Indo-Iranian Journal of Scientific Research (IIJSR) Volume, 3, 69-84.

Aghera, S. (2021). SECURING CI/CD PIPELINES USING AUTOMATED ENDPOINT SECURITY HARDENING. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 18(1).

Aghera, S. (2022). IMPLEMENTING ZERO TRUST SECURITY MODEL IN DEVOPS ENVIRONMENTS. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 19(1).

Aghera, S. (2021). SECURING CI/CD PIPELINES USING AUTOMATED ENDPOINT SECURITY HARDENING. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 18(1).

Dhiman, V. (2022). INTELLIGENT RISK ASSESSMENT FRAMEWORK FOR SOFTWARE SECURITY COMPLIANCE USING AI. International Journal of Innovation Studies, 6(3).

Dhiman, V. (2021). ARCHITECTURAL DECISION-MAKING USING REINFORCEMENT LEARNING IN LARGE-SCALE SOFTWARE SYSTEMS. International Journal of Innovation Studies, 5(1).

Dhiman, V. (2020). PROACTIVE SECURITY COMPLIANCE: LEVERAGING PREDICTIVE ANALYTICS IN WEB APPLICATIONS. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 17(1).

Dhiman, V. (2019). DYNAMIC ANALYSIS TECHNIQUES FOR WEB APPLICATION VULNERABILITY DETECTION. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 16(1).

Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., Zhao, J., ... & Borejdo, J. (2011). Cross-bridge kinetics in myofibrils containing familial hypertrophic cardiomyopathy R58Q mutation in the regulatory light chain of myosin. Journal of theoretical biology, 284(1), 71-81.

Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., & Borejdo, J. (2010). Kinetics of a single cross-bridge in familial hypertrophic cardiomyopathy heart muscle measured by reverse Kretschmann fluorescence. Journal of Biomedical Optics, 15(1), 017011-017011.

Mettikolla, P., Luchowski, R., Gryczynski, I., Gryczynski, Z., Szczesna-Cordary, D., & Borejdo, J. (2009). Fluorescence lifetime of actin in the familial hypertrophic cardiomyopathy transgenic heart. Biochemistry, 48(6), 1264-1271.

Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., & Borejdo, J. (2010). Observing cycling of a few cross‐bridges during isometric contraction of skeletal muscle. Cytoskeleton, 67(6), 400-411.

Muthu, P., Mettikolla, P., Calander, N., & Luchowski, R. 458 Gryczynski Z, Szczesna-Cordary D, and Borejdo J. Single molecule kinetics in, 459, 989-998.

Borejdo, J., Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., & Gryczynski, Z. (2021). Surface plasmon assisted microscopy: Reverse kretschmann fluorescence analysis of kinetics of hypertrophic cardiomyopathy heart.

Yadav, H. (2023). Securing and Enhancing Efficiency in IoT for Healthcare Through Sensor Networks and Data Management. International Journal of Sustainable Development Through AI, ML and IoT, 2(2), 1-9.

Yadav, H. (2023). Enhanced Security, Privacy, and Data Integrity in IoT Through Blockchain Integration. International Journal of Sustainable Development in Computing Science, 5(4), 1-10.

Yadav, H. (2023). Advancements in LoRaWAN Technology: Scalability and Energy Efficiency for IoT Applications. International Numeric Journal of Machine Learning and Robots, 7(7), 1-9.

Yadav, H. (2024). Scalable ETL pipelines for aggregating and manipulating IoT data for customer analytics and machine learning. International Journal of Creative Research In Computer Technology and Design, 6(6), 1-30.

Yadav, H. (2024). Anomaly detection using Machine Learning for temperature/humidity/leak detection IoT. International Transactions in Artificial Intelligence, 8(8), 1-18.

Yadav, H. (2024). Structuring SQL/NoSQL databases for IoT data. International Journal of Machine Learning and Artificial Intelligence, 5(5), 1-12.

Whig, P., Remala, R., Mudunuru, K. R., & Quraishi, S. J. (2024). Integrating AI and Quantum Technologies for Sustainable Supply Chain Management. In Quantum Computing and Supply Chain Management: A New Era of Optimization (pp. 267-283). IGI Global.

Whig, P., Mudunuru, K. R., & Remala, R. (2024). Quantum-Inspired Data-Driven Decision Making for Supply Chain Logistics. In Quantum Computing and Supply Chain Management: A New Era of Optimization (pp. 85-98). IGI Global.

Mudunuru, K. R., Remala, R., & Nagarajan, S. K. S. (2024). AI-Driven Data Analytics Unveiling Sales Insights from Demographics and Beyond.

Remala, R., Mudunuru, K. R., Gami, S. J., & Nagarajan, S. K. S. (2024). Optimizing Data Management Strategies: Analyzing Snowflake and DynamoDB for SQL and NoSQL. Journal Homepage: http://www. ijmra. us, 14(8).

Remala, R., Marupaka, D., & Mudunuru, K. R. (2024). Beyond Volume: Enhancing Data Quality in Big Data Analytics through Frameworks and Metrics.

Nagarajan, S. K. S., Remala, R., Mudunuru, K. R., & Gami, S. J. Automated Validation Framework in Machine Learning Operations for Consistent Data Processing.

Mudunuru, K. R., Remala, R., & Nagarajan, S. K. S. Leveraging IoT and Data Analytics in Logistics: Optimized Routing, Safety, and Resource Planning.

Remala, R., Mudunuru, K. R., & Nagarajan, S. K. S. Optimizing Data Ingestion Processes using a Serverless Framework on Amazon Web Services.