Review on Enhancing Cybersecurity Through Intelligent Attack Detection And Monitoring

Abstract

Cyber security professionals look at the risk profile more and offer ways to reduce it. One objective set for the area of cyber security was the creation of effective approaches. The use of machine learning is also improving cyber defences. The use of clouds, networking, and evolutionary programming have all seen tremendous growth as a result of never before seen developments in storage, computing, and computational technology. As the world quickly goes digital, there is a growing demand need comprehensive and complex privacy and security issues. Moreover, strong defences against security issues. Due to various computer weaknesses, there is an increase in global internet terrorism. Using strategies based on machine learning, issues with global security of computers, like virus detection, ransom recognition, identifying fraudulent activity, and spoofing verification, were solved. The study looks at the use of online activity training for both offensive and defensive reasons, offering data on cyber risks using machine learning approaches and methods. The examination of the more prevalent types for cyber security concerns uses machine learning to explain the way machine acquiring is utilised for computer defence, including the discovering and avoiding of attacks, vulnerability examination and recognition, and open-source internet risk assessment.

Introduction

The increasing digitization of the world has heightened the importance of cybersecurity. As online communication and access to scientific knowledge improve, both legitimate users and malicious actors, including cybercriminals and rogue state actors, gain access to sensitive technologies. Machine learning (ML) has emerged as a powerful tool to enhance cybersecurity by detecting threats and responding to attacks more effectively.

This study explores how ML algorithms can be used to analyze internet traffic (specifically NetFlow data) to identify malware and botnet activity. It evaluates five ML techniques, finding that random forest models perform particularly well—detecting over 95% of botnets in most datasets.

Key Objectives:

1. Detect botnet/malware activity from NetFlow data.

2. Classify traffic as either malicious or normal, regardless of volume or platform.

3. Evaluate and recommend the best machine learning model for cybersecurity tasks.

Problem Identification:

• Cyberattacks are increasing in frequency and complexity.

• Traditional intrusion detection methods are no longer sufficient.

• ML offers an adaptive, scalable solution for identifying threats like spyware, botnets, and malware.

Cybersecurity Domains Covered:

1. Intrusion Detection – Uses both signature-based and anomaly-based systems.

2. Malware Analysis – Detects a wide range of malicious software types.

3. Mobile Malware Detection – Especially targeting Android apps using ML like K-NN and clustering.

4. Fraud and Spam Detection – Uses ML to identify and filter phishing and unwanted content.

5. Cyberattack Typologies – Covers a broad set of attack types affecting data confidentiality and integrity.

Machine Learning in Cybersecurity:

• Techniques like RNNs, CNNs, SVM, Naive Bayes, and KNN are applied.

• ML can outperform traditional rule-based systems.

• Effective in classification, anomaly detection, and prediction of cyber threats.

• A flowchart is used to guide data collection, preprocessing, model training, and evaluation.

Conclusion

Many different types of cyber security are addressed using machine learning techniques. Exciting solutions to network security challenges are provided by advancements in artificial intelligence and critical thinking. Determining which method is adequate for a certain task is necessary, though. Micro processes are required to maintain an extensive model against malicious software and to get findings that are extremely accurate. In order to solve cryptographic difficulties, the choosing of a particular architecture is crucial. In accordance with our technique, we first classified the protection functions according to their importance before creating a straightforward authentication system that likewise relied on trees and was based on the most crucial aspects that were chosen. People have cut the cost of computers and increased estimation accuracy of defending the model to uncertain unit tests by using the tactics specified in a lower proportion when developing the final leaf structure.

References

[1] S. Dolev and S. Lodha, In Proceedings of the First International Conference, CSCML 2017, Beer-Sheva, Israel, June 29-30, (2017). [2] G. A. Wang, M. Chau, and H. Chen., Proceedings. Cham, Switzerland: Springer, May 23, (2017). [3] J. Cano, ISACA Journal, 5, 1-5 (2016). [4] C. Hollingsworth, ISACA Journal, 5, 1-6 (2016). [5] X. Li, J. Wang, X. Zhang, J. Future Internet, (2017). [6] M. Nalini and A. Chakram, International Journal of Innovative Technology and Exploring Engineering, 8, 197-201(2019). [7] Y. Li, J. Xia, S. Zhang, J. Yan, X. Ai, K. Dai, Expert Syst. 39, 424–430, (2012). [8] W. Hu, Y. Liao, and V. R. Vemuri, Proceedings of the International Conference on Machine Learning & Applications—ICMLA 2003, Los Angeles, CA, USA, 23–24, 168–174 (2003). [9] C. Kruegel, D. Mutz, W. Robertson, and F. Valeur, Proceeding of the 19th International Computer Security Application Conference, Las Vegas, Nevada, USA, 14–23 (2003). [10] IEEE Communication Surveys and Tutorials, 15 (2013), by A. Almomani, B. Gupta, S. Atawneh, A. Meulenberg, and E. Almomani. [11] Proceedings of the 2019 annual IEEE Conference on Innovation in Technology for Information and Communication, edited by V. Padmanaban and M. Nalini, (2019). [12] M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and A. N. Venkatraman, 2019, R. Vinayakumar, and 7 [13] J. Gardiner, S. Nagaraja, ACM Comput Surv 49 1–59 (2016). [14] M. Nalini and S. Anbu, International Journal of Applied Engineering Research, 9 (2014). 030003-10. [15] Li, W., & Wang, S. (2009). Application of a KDD\'99 data set to DoS, Probe, U2R, and R2L attack detection using SVM with RBF kernel. Journal of Information Security, 4(2), 138-146. [16] Amiri, F., Yousefi, M. R., Lucas, C., Shakery, A., & Yazdani, N. (2011). Mutual information-based feature selection for intrusion detection systems. Journal of Network and Computer Applications, 34(4), 1184-1199. [17] Hu, W., Gao, J., Wang, Y., Tan, T., & Maybank, S. (2007). Learning activity patterns using fuzzy self-organizing neural networks. IEEE Transactions on Systems, Man, and Cybernetics, 37(4), 826-839. [18] Wagner, D., & Soto, R. (2002). Mimicry attacks on host-based intrusion detection systems. Proceedings of the 9th ACM Conference on Computer and Communications Security (CCS\'02), 255-264. [19] Kruegel, C., Mutz, D., Robertson, W., & Valeur, F. (2003). Bayesian event classification for intrusion detection. Proceedings of the 19th Annual Computer Security Applications Conference (ACSAC), 14-23. [20] Benferhat, S., & Tabia, K. (2004). A Bayesian approach for intrusion detection in large-scale networks. Proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2004), 41-49. [21] Koc, L., Mazzuchi, T. A., & Sarkani, S. (2012). A network intrusion detection system based on a Hidden Naive Bayes multiclass classifier. Expert Systems with Applications, 39(18), 13492-13500.

Copyright

Copyright © 2025 Mr. Simran Diliprao Patil, Dr. Gurudeo Sawarkar, Prof. Rahul Bhandekar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.