Network Traffic Intrusion Detection Applications: Key Parameters and Techniques
Keywords:
Anomaly detection, Cybersecurity in defense, Defense network security, Intrusion detection systems (IDS), Malicious traffic detection, Military networks, Network traffic analysis, Packet flow analysis, Real-time threat detectionAbstract
Intrusion Detection Systems (IDS) play a critical role in safeguarding digital infrastructures by identifying and mitigating unauthorized access and malicious activities. This paper presents a comprehensive overview of the various IDS techniques, including signature-based, anomaly-Intrusion Detection Systems (IDS) play a vital role in protecting digital infrastructures by detecting and preventing unauthorized access and malicious activities. This paper provides a comprehensive overview of various IDS techniques, including signature-based, anomaly-based, specification-based, and modern machine learning and deep learning methods. Each approach is assessed based on its decision-making processes, input needs, detection abilities, and performance metrics. The study also examines hybrid and heuristic models that aim to improve detection accuracy by combining the strengths of multiple techniques. A detailed comparison is included in tabular form, outlining the advantages, disadvantages, and limitations of each method. While traditional techniques offer high accuracy for known threats, they often fail to detect new and emerging ones. Conversely, learning-based models are effective at identifying unknown intrusions but face challenges like high false positive rates and increased computational requirements. This analysis serves as a valuable reference for researchers and practitioners to choose or develop suitable IDS strategies based on system needs, data features, and the evolving threat landscape. Based on specification and modern machine learning and deep learning approaches, each method is evaluated in terms of its decision-making patterns, input requirements, detection capabilities, and performance metrics. The study further explores hybrid and heuristic-based models that aim to enhance detection accuracy by leveraging the strengths of multiple techniques. A detailed comparative analysis is provided in tabular form, highlighting the advantages, disadvantages, and limitations of each approach. While traditional methods offer high precision for known attacks, they struggle with detecting novel threats. Conversely, learning-based models excel at identifying unknown intrusions but face challenges such as high false positives and computational overhead. This analysis offers a foundational reference for researchers and practitioners to select or design appropriate IDS strategies based on system requirements, data characteristics, and threat landscapes.
References
V. A. Chastikova and A. I. Mitugov, “The method for detecting network attacks based on the neuroimmune approach,” Journal of Physics Conference Series, vol. 2094, no. 3, pp. 032035–032035, Nov. 2021, doi: https://doi.org/10.1088/1742-6596/2094/3/032035
Z. Jie Du, L. Ma, H. Li, Q. Li, G. Sun, and Z. Liu, “Network Traffic Anomaly Detection Based on Wavelet Analysis,” 2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), Jun. 2018, doi: https://doi.org/10.1109/sera.2018.8477230
Q. Ma, C. Sun, and B. Cui, “A Novel Model for Anomaly Detection in Network Traffic Based on Support Vector Machine and Clustering,” Security and Communication Networks, vol. 2021, p. e2170788, Nov. 2021, doi: https://doi.org/10.1155/2021/2170788
A. Ferriyan, A. H. Thamrin, K. Takeda, and J. Murai, “Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic,” Applied Sciences, vol. 11, no. 17, p. 7868, Aug. 2021, doi: https://doi.org/10.3390/app11177868
R. Damasevicius, A. Venckauskas, S. Grigaliunas, “LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion Detection,” Electronics, vol. 9, no. 5, p. 800, May 2020, doi: https://doi.org/10.3390/electronics9050800
J. McHugh, “Testing Intrusion Detection Systems,” ACM Transactions on Information and System Security (TISSEC), vol. 3, no. 4, pp. 262–294, Nov. 2000, doi: https://doi.org/10.1145/382912.382923
A. Sperotto, R. Sadre, F. van Vliet, and A. Pras, “A Labeled Data Set for Flow-Based Intrusion Detection,” IP Operations and Management, pp. 39–50, 2009, doi: https://doi.org/10.1007/978-3-642-04968-2_4
P. Dini, L. Diana, A. Elhanashi, and S. Saponara, “Overview of AI-Models and Tools in Embedded IIoT Applications,” Electronics, vol. 13, no. 12, p. 2322, Jan. 2024, doi: https://doi.org/10.3390/electronics13122322
A. Aldweesh, A. Derhab, and A. Z. Emam, “Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues,” Knowledge-Based Systems, vol. 189, p. 105124, Oct. 2019, doi: https://doi.org/10.1016/j.knosys.2019.105124.
Z. Yang, X. Liu, T. Li, “A systematic literature review of methods and datasets for anomaly-based network intrusion detection,” Computers & Security, vol. 116, p. 102675, May 2022, doi: https://doi.org/10.1016/j.cose.2022.102675
T. Saba, A. Rehman, T. Sadad, H. Kolivand, and S. A. Bahaj, “Anomaly-based intrusion detection system for IoT networks through deep learning model,” Computers and Electrical Engineering, vol. 99, p. 107810, Apr. 2022, doi: https://doi.org/10.1016/j.compeleceng.2022.107810
C. Wang and H. Zhu, “Wrongdoing Monitor: A Graph-Based Behavioral Anomaly Detection in Cyber Security,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2703–2718, 2022, doi: https://doi.org/10.1109/TIFS.2022.3191493
J. Jabez and B. Muthukumar, “Intrusion Detection System (IDS): Anomaly Detection Using Outlier Detection Approach,” Procedia Computer Science, vol. 48, pp. 338–346, 2015, doi: https://doi.org/10.1016/j.procs.2015.04.191
T. Ma, F. Wang, J. Cheng, Y. Yu, and X. Chen, “A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks,” Sensors, vol. 16, no. 10, p. 1701, Oct. 2016, doi: https://doi.org/10.3390/s16101701
S. Anwar et al., “From Intrusion Detection to an Intrusion Response System: Fundamentals, Requirements, and Future Directions,” Algorithms, vol. 10, no. 2, p. 39, Mar. 2017, doi: https://doi.org/10.3390/a10020039
S. Dhaliwal, A.-A. Nahid, and R. Abbas, “Effective Intrusion Detection System Using XGBoost,” Information, vol. 9, no. 7, p. 149, Jun. 2018, doi: https://doi.org/10.3390/info9070149.
A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, “Survey of intrusion detection systems: techniques, datasets and challenges,” Cybersecurity, vol. 2, no. 1, pp. 1–22, Jul. 2019, doi: https://doi.org/10.1186/s42400-019-0038-7
J. Arshad, M. A. Azad, R. Amad, K. Salah, M. Alazab, and R. Iqbal, “A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT,” Electronics, vol. 9, no. 4, p. 629, Apr. 2020, doi: https://doi.org/10.3390/electronics9040629
F. Panagiotis, K. Taxiarxchis, K. Georgios, L. Maglaras, and M. A. Ferrag, “Intrusion Detection in Critical Infrastructures: A Literature Review,” Smart Cities, vol. 4, no. 3, pp. 1146–1157, Aug. 2021, doi: https://doi.org/10.3390/smartcities4030061
Ahmad, Q. E. Ul Haq, M. Imran, M. O. Alassafi, and R. A. AlGhamdi, “An Efficient Network Intrusion Detection and Classification System,” Mathematics, vol. 10, no. 3, p. 530, Feb. 2022, doi: https://doi.org/10.3390/math10030530
P. Dini, A. Elhanashi, A. Begni, S. Saponara, Q. Zheng, and K. Gasmi, “Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity,” Applied Sciences, vol. 13, no. 13, p. 7507, Jan. 2023, Available: https://www.mdpi.com/2076-3417/13/13/7507
B. R. Kikissagbe and M. Adda, “Machine Learning-Based Intrusion Detection Methods in IoT Systems: A Comprehensive Review,” Electronics, vol. 13, no. 18, p. 3601, Sep. 2024, doi: https://doi.org/10.3390/electronics13183601
L. Diana, P. Dini, and D. Paolini, “Overview on Intrusion Detection Systems for Computer Networking Security,” Computers, vol. 14, no. 3, p. 87, Mar. 2025, doi: https://doi.org/10.3390/computers14030087
S. S, A. B. L, and S. Kumar, “Adaptive Layered Machine Learning Approach to Detect and Mitigate Behavioral Based Intrusions in Wireless Sensor Network,” 2024 Control Instrumentation System Conference (CISCON), pp. 1–7, Aug. 2024, doi: https://doi.org/10.1109/ciscon62171.2024.10696254
H. Yzzogh, H. Kandil, and H. Benaboud, “A comprehensive overview of AI-driven behavioral analysis for security in Internet of Things,” CRC Press eBooks, pp. 40–51, Oct. 2024, doi: https://doi.org/10.1201/9781032714806-4
A. Shamekhi, P. S. Babaki, and R. Javidan, “An intelligent behavioral-based DDOS attack detection method using adaptive time intervals,” Peer-to-peer networking and applications, Apr. 2024, doi: https://doi.org/10.1007/s12083-024-01690-2
D. P. Gaikwad and R. C. Thool, “Intrusion Detection System Using Bagging with Partial Decision Tree-Based Classifier,” Procedia Computer Science, vol. 49, pp. 92–98, 2015, doi: https://doi.org/10.1016/j.procs.2015.04.231
A. S. A. Aziz, S. E.-O. Hanafi, and A. E. Hassanien, “Comparison of classification techniques applied for network intrusion detection and classification,” Journal of Applied Logic, vol. 24, pp. 109–118, Nov. 2017, doi: https://doi.org/10.1016/j.jal.2016.11.018
N. Farnaaz and M. A. Jabbar, “Random Forest Modeling for Network Intrusion Detection System,” Procedia Computer Science, vol. 89, pp. 213–217, Jan. 2016, doi: https://doi.org/10.1016/j.procs.2016.06.047
Atefi, S. Yahya, A. Rezaei, and S. H. Binti Mohd Hashim, “Anomaly detection based on profile signature in network using machine learning technique,” 2016 IEEE Region 10 Symposium (TENSYMP), May 2016, doi: https://doi.org/10.1109/tenconspring.2016.7519380
B. B. Rao and K. Swathi, “Fast kNN Classifiers for Network Intrusion Detection System,” Indian Journal of Science and Technology, vol. 10, no. 14, pp. 1–10, Apr. 2017, doi: https://doi.org/10.17485/ijst/2017/v10i14/93690
