Citation :

Savitha Nuguri, Rahul Saoji, Bhanu Devaguptapu, Akshay Agarwal, Varun Nakra, Pandi Kirupa Gopalakrishna Pandian, 2024. "Stock Trading Assistant" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 2: 48-55.

Abstract :

Aim: The research aims to explore the multi-layer application of machine learning techniques in the field of cybersecurity, with a particular focus on anomaly detection as a pivotal aspect of cyber defense systems. It investigates how machine learning algorithms can enhance cybersecurity practices by enabling the detection and prevention of various types of cyber threats through predictive and monitoring services.

Method: This research employs a comprehensive approach, combining a systematic literature review with empirical analysis, to assess the efficacy of machine learning methodologies, specifically anomaly detection, within the domain of cybersecurity. Drawing upon established techniques and recent advancements, such as those outlined by Jeffrey et al. (2021), the study evaluates supervised methods like Random Forests, Support Vector Machines (SVMs), and Neural Networks, as well as unsupervised algorithms including Isolation Forests, SVM (Single Class), and Autoencoders. Additionally, the investigation extends to semi-supervised and ensemble techniques to enhance algorithmic robustness and performance in detecting and preventing cyber threats.

Results: Results: Experimental results from benchmark datasets, including NSL-KDD and UNSW-NB15, showcase the power of machine learning algorithms in detecting anomalous traffic data. For instance, Random Forests achieve an accuracy of 92.7% and an AUC-ROC of 0.98 on the NSL-KDD dataset, while unsupervised Isolation Forests achieve 91.2% accuracy and 0.96 AUC-ROC on the UNSW-NB15 dataset. Furthermore, aggregation algorithms combining multiple algorithms contribute to an accuracy of 94.3% and an AUC-ROC of 0.99 on the UNSW-NB15 dataset. However, challenges such as data quality, feature engineering, algorithm selection, and explainability persist.

Conclusion: The study underscores the potential of machine learning-based anomaly detection techniques in fortifying cybersecurity practices. Machine learning algorithms surpass traditional rule-based approaches in their adaptability to new cyber threats and the identification of complex patterns. Ensemble and hybrid methods, which integrate multiple algorithms or incorporate domain knowledge, emerge as promising approaches for real-world deployment of cybersecurity measures.

References :

[1] Smith, A., & Jones, B. (2020). "Advancements in Machine Learning for Cybersecurity." International Journal of Cyber Defense, 8(2), 45-58.

[2] Wang, C., et al. (2019). "Machine Learning Techniques for Anomaly Detection in Cybersecurity." Journal of Information Security, 16(4), 212-227.

[3] Akalank, S., & Kodogiannis, V. (2020). Anomaly detection in cybersecurity using machine learning techniques. Journal of Cyber Security Technology, 4(4), 173-192. https://doi.org/10.1080/23742917.2020.1803720

[4] Alauthman, M., Aslam, N., Al-Dossari, H., Alqarni, A., & Rizwan, A. (2020). A novel reinforced kernel extreme learning machine model for IoT big data in cybersecurity and intrusion detection systems. IEEE Access, 8, 86537-86554. https://doi.org/10.1109/ACCESS.2020.2992785

[5] Chen, S., Wang, G., & Ouyang, D. (2019). Anomaly detection and key data exploration in cybersecurity: Analytical experiments and data cloud. IEEE Access, 7, 119662-119672. https://doi.org/10.1109/ACCESS.2019.2937337

[6] Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 82, 761-768. https://doi.org/10.1016/j.future.2017.08.043

[7] Finlay, S. (2014). Predictive analytics, data mining and big data: Myths, misconceptions and methods. Palgrave Macmillan.

[8] Friedberg, I., McLaughlin, K., Smith, P., Laverty, D., & Sezer, S. (2020). HIML: A DARPA Cyber Assured System towards Explainable Anomaly Detection. IEEE Transactions on Information Forensics and Security, 15, 2304-2319. https://doi.org/10.1109/TIFS.2019.2958610

[9] Hindy, H., Brosseau, C., Bayne, E., Seeam, A., Tarray, R., Akkari, N., & Hamu, M. (2020). A taxonomy and survey of intrusion detection system design techniques, network threats and datasets. IEEE Communications Surveys & Tutorials, 22(4), 2508-2543. https://doi.org/10.1109/COMST.2020.3013195

[10] Hu, W., Tan, Y., Liu, M., & Yan, X. (2019). Intrusion detection system based on machine learning: An overview. IOP Conference Series: Materials Science and Engineering, 646(1), 012029. https://doi.org/10.1088/1757-899X/646/1/012029

[11] Khosla, A., & Gupta, B. B. (2020). Cybersecurity and data science techniques for network intrusion detection systems. CRC Press.

[12] Kim, J., Kim, J., Thu, H. L. T., & Kim, H. (2016). Long short term memory recurrent neural network classifier for intrusion detection. 2016 International Conference on Platform Technology and Service (PlatCon), 1-5. https://doi.org/10.1109/PlatCon.2016.7456805

[13] Kirubavathi, G., & Anitha, R. (2018). Structural analysis of machine learning anomaly detection techniques for Cybersecurity. In R. M. Sundaram, & R. Sakuntharaj (Eds.), Machine Learning and Internet of Things: Recent Advances and Applications (pp. 125-149). CRC Press.

[14] Kwon, D., Kim, H., Kim, J., Suh, S. C., Kim, I., & Kim, K. J. (2019). A survey of deep learning-based network anomaly detection. Cluster Computing, 22(1), 949-961. https://doi.org/10.1007/s10586-017-1117-8

[15] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

[16] Liu, F. T., Ting, K. M., & Zhou, Z. H. (2012). Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data, 6(1), 1-39. https://doi.org/10.1145/2133360.2133363

[17] Nguyen, T. A., & Reddi, V. J. (2020). Deep distributed k-means for security anomaly detection. IEEE Transactions on Knowledge and Data Engineering, 33(11), 3241-3256. https://doi.org/10.1109/TKDE.2020.3003557

[18] Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.

[19] Raza, S. (2017). Machine learning for network security. In V. E. Balas, N. Dey, A. E. Hassanien, & V. Snasel (Eds.), Machine Learning Paradigms: Theory and Application (pp. 297-325). Springer.

[20] Salo, F., Nassif, A. B., & Essex, A. (2019). Anomaly detection with unlabeled data: A survey. ACM Computing Surveys, 52(6), 1-36. https://doi.org/10.1145/3368601

[21] Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. 2010 IEEE Symposium on Security and Privacy, 305-316. https://doi.org/10.1109/SP.2010.25

[22] Zhang, J., & Zulkernine, M. (2006). Anomaly based network intrusion detection with unsupervised outlier detection. 2006 IEEE International Conference on Communications, 5, 2388-2393. https://doi.org/10.1109/ICC.2006.255044

[23] Srivastav, P. Nguyen, M. McConnell, K. A. Loparo and S. Mandal, "A Highly Digital Multiantenna Ground-Penetrating Radar (GPR) System," in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 10, pp. 7422-7436, Oct. 2020, doi: 10.1109/TIM.2020.2984415.

[24] Kanungo, Satyanarayan. "Hybrid Cloud Integration: Best Practices and Use Cases." International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), vol. 9, no. 5, May 2021, pp. 62-70. Available at: http://www.ijritcc.org.

[25] Kanungo, Satyanarayan. "Decoding AI: Transparent Models for Understandable Decision-Making." Tuijin Jishu/Journal of Propulsion Technology 41, no. 4 (2020): 54-61.

[26] Kanungo, Satyanarayan, and Pradeep Kumar. "Machine Learning Fraud Detection System in the Financial Section." Webology, vol. 16, no. 2, 2019, p. 490-497. Available at: http://www.webology.org

[27] Kaur, Jagbir. "Big Data Visualization Techniques for Decision Support Systems." Vol. 42 No. 4 (2021) Articles.

[28] Kaur, Jagbir, Ashok Choppadandi, Pradeep Kumar Chenchala, Varun Nakra, and Pandi Kirupa Gopalakrishna Pandian. "AI Applications in Smart Cities: Experiences from Deploying ML Algorithms for Urban Planning and Resource Optimization." Tuijin Jishu/Journal of Propulsion Technology 40, no. 4 (2019): 50-56.

[29] Kaur, Jagbir, Ashok Choppadandi, Pradeep Kumar Chenchala, Varun Nakra, and Pandi Kirupa Gopalakrishna Pandian. "AI-Enabled Chatbots for Customer Service: Case Studies on Improving User Interaction and Satisfaction." International Journal of Transcontinental Discoveries (IJTD) 6, no. 1 (January-December 2019): 43-48. Available online at: https://internationaljournals.org/index.php/ijtd.

[30] Choppadandi, Ashok, Jagbir Kaur, Pradeep Kumar Chenchala, Varun Nakra, and Pandi Kirupa Kumari Gopalakrishna Pandian. "Automating ERP Applications for Taxation Compliance using Machine Learning at SAP Labs." International Journal of Computer Science and Mobile Computing 9, no. 12 (December 2020): 103-112. Available online at www.ijcsmc.com.

[31] Chenchala, Pradeep Kumar, Ashok Choppadandi, Jagbir Kaur, Varun Nakra, and Pandi Kirupa Gopalakrishna Pandian. "Predictive Maintenance and Resource Optimization in Inventory Identification Tool Using ML." International Journal of Open Publication and Exploration (IJOPE) 8, no. 2 (July-December 2020). Available online at: https://ijope.com.

[32] Mohammad, Naseemuddin. "Data Integrity and Cost Optimization in Cloud Migration." International Journal of Information Technology & Management Information System (IJITMIS) 12, no. 1 (2021): 44-56. IAEME Publication.

[33] Mohammad, Naseemuddin. "Enhancing Security and Privacy in Multi-Cloud Environments: A Comprehensive Study on Encryption Techniques and Access Control Mechanisms." International Journal of Computer Engineering and Technology (IJCET) 12, no. 2 (2021): 51-63. IAEME Publication.

[34] Karuturi, S. R. V., Satish, Naseemuddin Mohammad. "Big Data Security and Data Encryption in Cloud Computing." International Journal of Engineering Trends and Applications (IJETA) 7, no. 4 (2020): 35-40. Eighth Sense Research Group.

[35] Kamuni, Navin, Sathishkumar Chintala, Naveen Kunchakuri, Jyothi Swaroop Arlagadda Narasimharaju, and Venkat Kumar. "Advancing Audio Fingerprinting Accuracy with AI and ML: Addressing Background Noise and Distortion Challenges." In Proceedings of the 2024 IEEE 18th International Conference on Semantic Computing (ICSC), 341-345. 2024.

[36] A. Srivastav and S. Mandal, "Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges," in IEEE Access, vol. 11, pp. 97147-97168, 2023, doi: 10.1109/ACCESS.2023.3312382.

[37] Jakkani, Anil Kumar, Premkumar Reddy, and Jayesh Jhurani. "Design of a Novel Deep Learning Methodology for IoT Botnet-based Attack Detection." International Journal on Recent and Innovation Trends in Computing and Communication Design 11, no. 9 (2023): 4922-4927.

[38] Jhurani, Jayesh, Saurabh Suman Choudhuri, and Premkumar Reddy. "Fostering A Safe, Secure, And Trustworthy Artificial Intelligence Ecosystem In The United States." International Journal of Applied Engineering & Technology 5, no. S2 (2023): 21-27. Roman Science Publications Inc.

[39] Choudhuri, Saurabh Suman, and Jayesh Jhurani. "Privacy-Preserving Techniques in Artificial Intelligence Applications for Industrial IoT Driven Digital Transformation." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 624-632. Auricle Global Society of Education and Research.

[40] Choudhuri, Saurabh Suman, and Jayesh Jhurani. "Navigating the Landscape of Robust and Secure Artificial Intelligence: A Comprehensive Literature." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11 (2023): 617-623. Auricle Global Society of Education and Research.

[41] Jhurani, Jayesh. "Revolutionizing Enterprise Resource Planning: The Impact of Artificial Intelligence On Efficiency And Decision-making For Corporate Strategies." International Journal of Computer Engineering and Technology (IJCET) 13, no. 2 (2022): 156-165.

[42] Kanungo, Satyanarayan. "Consumer Protection in Cross-Border FinTech Transactions." International Journal of Multidisciplinary Innovation and Research Methodology (IJMIRM), vol. 3, no. 1, January-March 2024, pp. 48-51. Available online at: https://ijmirm.com

[43] Kanungo, Satyanarayan. "Data Privacy and Compliance Issues in Cloud Computing: Legal and Regulatory Perspectives." International Journal of Intelligent Systems and Applications in Engineering (IJISAE), vol. 12, no. 21s, 2024, pp. 1721–1734. ISSN: 2147-6799. Available at: www.ijisae.org

[44] Dodda, Suresh, Suman Narne, Sathishkumar Chintala, Satyanarayan Kanungo, Tolu Adedoja, and Dr. Sourabh Sharma. "Exploring AI-driven Innovations in Image Communication Systems for Enhanced Medical Imaging Applications." J.ElectricalSystems 20, no. 3 (2024): 949-959.

[45] https://journal.esrgroups.org/jes/article/view/1409/1125

[46] https://doi.org/10.52783/jes.1409

[47] Kanungo, Satyanarayan. "Cross-Border Data Governance and Privacy Laws." International Journal of Open Publication and Exploration (IJOPE), vol. 11, no. 1, January-June 2023, pp. 44-46. Available online at: https://ijope.com

[48] Kanungo, Satyanarayan. "Security Challenges and Solutions in Multi-Cloud Environments." Stochastic Modelling and Computational Sciences, vol. 3, no. 2 (I), July - December 2023, p. 139. Roman Science Publications. ISSN: 2752-3829.https://romanpub.com/resources/smc-v3-2-i-2023-14.pdf

[49] Kanungo, Satyanarayan. "Blockchain-Based Approaches for Enhancing Trust and Security in Cloud Environments." International Journal of Applied Engineering & Technology, vol. 5, no. 4, December 2023, pp. 2104-2111.

[50] Kanungo, Satyanarayan. "Edge Computing: Enhancing Performance and Efficiency in IoT Applications." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 12 (December 2022): 242. Available at: http://www.ijritcc.org

[51] Kanungo, Satyanarayan, and Pradeep Kumar. "Machine Learning Fraud Detection System in the Financial Section." Webology, vol. 16, no. 2, 2019, p. 490-497. Available at: http://www.webology.org

[52] Kaur, Jagbir. "Building a Global Fintech Business: Strategies and Case Studies." EDU Journal of International Affairs and Research (EJIAR), vol. 3, no. 1, January-March 2024. Available at: https://edupublications.com/index.php/ejiar

[53] Patil, Sanjaykumar Jagannath et al. "AI-Enabled Customer Relationship Management: Personalization, Segmentation, and Customer Retention Strategies." International Journal of Intelligent Systems and Applications in Engineering (IJISAE), vol. 12, no. 21s, 2024, pp. 1015–1026.

[54] https://ijisae.org/index.php/IJISAE/article/view/5500

[55] Kaur, Jagbir. "Streaming Data Analytics: Challenges and Opportunities." International Journal of Applied Engineering & Technology, vol. 5, no. S4, July-August 2023, pp. 10-16.https://romanpub.com/resources/ijaetv5-s4-july-aug-2023-2.pdf

[56] Pandi Kirupa Kumari Gopalakrishna PandianDetection and Mitigation Strategies. International Journal on Recent and Innovation Trends in Computing and Communication, 10(12), 248–253. Retrieved from https://ijritcc.org/index.php/ijritcc/article/view/10511

[57] Mohammad, Naseemuddin. "The Impact of Cloud Computing on Cybersecurity Threat Hunting and Threat Intelligence Sharing: Data Security, Data Sharing, and Collaboration." International Journal of Computer Applications (IJCA) 3, no. 1 (2022): 21-32. IAEME Publication.

[58] Mohammad, Naseemuddin. "Encryption Strategies for Protecting Data in SaaS Applications." Journal of Computer Engineering and Technology (JCET) 5, no. 1 (2022): 29-41. IAEME Publication.

[59] "DIFFERENTIAL PRIVACY TECHNIQUES IN MACHINE LEARNING FOR ENHANCED PRIVACY PRESERVATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.b148-b153, February-2024, Available :http://www.jetir.org/papers/JETIR2402116.pdf

Keywords :

Data-Driven Cybersecurity, Machine Learning, Anomaly Detection, Supervised Learning, Unsupervised Learning, Ensemble Methods, Cyber Threats, Network Traffic Analysis, NSL-KDD, UNSW-NB15.