Prevention and Detection of Network Attacks (2025)

Abstract

The rapid expansion of digital networks has escalated the prevalence and sophistication of network attacks, necessitating advanced strategies for prevention and detection. Our objective is to explore contemporary methodologies to safeguard networks against malicious activities. Prevention strategies focus on proactive measures, including robust encryption protocols, stringent access controls, and regular security audits. These measures aim to fortify network defenses, minimizing vulnerabilities that attackers could exploit. Detection strategies emphasize timely identification and response to breaches. Techniques such as anomaly detection, intrusion detection systems (IDS), and artificial intelligence (AI)-driven analytics play a crucial role in recognizing unusual patterns indicative of potential threats. Machine learning algorithms enhance these systems by continuously learning from network traffic to improve accuracy in detecting anomalies. The integration of these methodologies into a comprehensive cybersecurity framework is crucial for maintaining the integrity, confidentiality, and availability of network resources. Additionally, the importance of incident response planning and user education is highlighted in reinforcing network security. By adopting a multi-layered defense approach, organizations can better mitigate the risks associated with network attacks, ensuring a resilient digital infrastructure.

Introduction

Overview

The rapid growth of digital networks has improved communication and data exchange but also increased exposure to cyberattacks like DoS, malware, and unauthorized access. To combat these threats, this study proposes a dual-strategy model that integrates preventive measures (e.g., encryption, access control) with detection techniques (e.g., IDS, AI-based analysis).

Key Contributions

1. Proposed Approach

• Combines proactive prevention with AI-driven detection.

• Utilizes the Random Forest classifier to analyze network traffic and detect anomalies.

• Trained on the KDD Cup dataset, which contains labeled data on normal and attack traffic.

2. Literature Review Highlights

• Traditional security methods (e.g., firewalls, signature-based IDS) detect known threats but struggle with unknown attacks.

• Anomaly detection offers dynamic detection but can lead to high false positives.

• Machine learning (ML) methods like Random Forest, SVM, and KNN improve detection rates.

• Hybrid models integrating prevention (e.g., encryption) and detection (e.g., ML/IDS) provide balanced security.

• Gaps include high false positive rates, scalability issues, and data quality dependencies.

3. Experimental Setup

• Dataset: KDD Cup (41 features + labels).

• Preprocessing: Categorical encoding, feature scaling, and feature-target splitting.

• Tools & Technologies:

  • Python (main language)

  • Libraries: scikit-learn, pandas, NumPy, matplotlib, seaborn

  • Web components: HTML/CSS, JavaScript (for UI and interactivity)

4. Performance Comparison

• Random Forest model offered the highest accuracy with good balance between false positives and speed.

• Hybrid models provided strong, balanced performance leveraging strengths of both AI and traditional methods.

• Signature-based IDS had fast, accurate detection for known threats but lacked real-time capability and adaptability.

Conclusion

The project demonstrates a solid foundation for network intrusion detection using a Random Forest Classifier, achieving a baseline accuracy of 87%. While the model performs well on common attack types, opportunities exist to enhance accuracy through advanced techniques like class balancing, feature engineering, and hyperparameter tuning. Exploring alternative models and modern datasets could further improve performance, making the system more robust against evolving network threats.

References

[1] Hu, Y.C., Perrig, A., & Johnson, D.B. (2006). Wormhole attacks in wireless networks. IEEE Journal on Selected Areas in Communications, 24(2), 370-380.https://ieeexplore.ieee.org/document/1589115/ [2] Ahmad, T., Truscan, D., Vain, J., & Porres, I. (2022, April). Early detection of network attacks using deep learning. In 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) (pp. 30-39). IEEE.https://arxiv.org/abs/2201.11628 [3] Guembe, B., Azeta, A., Misra, S., Osamor, V.C., Fernandez-Sanz, L., & Pospelova, V. (2022). The emerging threat of AI-driven cyber attacks: A review. Applied Artificial Intelligence, 36(1), 2037254.https://www.tandfonline.com/doi/full/10.1080/08839514.2022.2037254 [4] Borkar, G.M., Patil, L.H., Dalgade, D., & Hutke, A. (2019). A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN: A data mining concept. Sustainable Computing: Informatics and Systems, 23, 120-135.https://www.sciencedirect.com/science/article/abs/pii/S2210537918300723 [5] KDD Cup Dataset: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.Scikit-learn Documentation: https://scikit-learn.org [6] Khan, M. U., & Khan, F. A. (2023). Prevention and Detection of Network Attacks: A Comprehensive Study. Retrieved from https://www.researchgate.net/publication/370849099_Prevention_and_Detection_of_Network_Attacks_A_Comprehensive_Study [7] Kumar, A., & Singh, S. (2023). A Comprehensive Study of Network Attack Prevention Techniques. IEEE. Retrieved from https://ieeexplore.ieee.org/document/9615288 [8] Verma, S., & Gupta, R. (2023). Literature Review on Cyber Attacks Detection and Prevention Schemes. Retrieved from https://www.researchgate.net/publication/356553583_Literature_Review_on_Cyber_Attacks_Detection_and_Prevention_Schemes [9] Singh, A., & Kumar, S. (2023). Emerging Techniques in Cybersecurity: An Overview. ScienceDirect. Retrieved from https://www.sciencedirect.com/science/article/pii/S1877050923006695 [10] Tan, J., & Zhou, Y. (2023). An Overview of Intrusion Detection Systems Using Machine Learning. MDPI. Retrieved from https://www.mdpi.com/1424-8220/21/21/7070

Copyright

Copyright © 2025 Palla Sravani, Allam Greeshma, Virlanki Rohith, Doddi Mani Bhushan, Kilugu Bharath Kumar. 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.