Overview on Intrusion Detection Systems for Computers Networking Security

Lorenzo Diana; Pierpaolo Dini; Davide Paolini

doi:10.3390/computers14030087

journal article Open Access Mar 03, 2025

Overview on Intrusion Detection Systems for Computers Networking Security

Lorenzo Diana

Pierpaolo Dini

Davide Paolini

Computers Vol. 14 No. 3 pp. 87 · MDPI AG

View at Publisher Save 10.3390/computers14030087

Abstract

The rapid growth of digital communications and extensive data exchange have made computer networks integral to organizational operations. However, this increased connectivity has also expanded the attack surface, introducing significant security risks. This paper provides a comprehensive review of Intrusion Detection System (IDS) technologies for network security, examining both traditional methods and recent advancements. The review covers IDS architectures and types, key detection techniques, datasets and test environments, and implementations in modern network environments such as cloud computing, virtualized networks, Internet of Things (IoT), and industrial control systems. It also addresses current challenges, including scalability, performance, and the reduction of false positives and negatives. Special attention is given to the integration of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML), and the potential of distributed technologies such as blockchain. By maintaining a broad-spectrum analysis, this review aims to offer a holistic view of the state-of-the-art in IDSs, support a diverse audience, and identify future research and development directions in this critical area of cybersecurity.

Topics

No keywords indexed for this article. Browse by subject →

References

175

[1]

Elsayed "AdaptIDS: Adaptive Intrusion Detection for Mission-Critical Aerospace Vehicles" IEEE Trans. Intell. Transp. Syst. (2022) 10.1109/tits.2022.3214095

[2]

Mehedi "Dependable Intrusion Detection System for IoT: A Deep Transfer Learning Based Approach" IEEE Trans. Ind. Inform. (2023) 10.1109/tii.2022.3164770

[3]

Papamartzivanos "Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems" IEEE Access (2019) 10.1109/access.2019.2893871

[4]

Govea "Effectiveness of an Adaptive Deep Learning-Based Intrusion Detection System" IEEE Access (2024) 10.1109/access.2024.3512363

[5]

Uhm "Service-Aware Two-Level Partitioning for Machine Learning-Based Network Intrusion Detection With High Performance and High Scalability" IEEE Access (2021) 10.1109/access.2020.3048900

[6]

Khan, M.A., Karim, M.R., and Kim, Y. (2019). A scalable and hybrid intrusion detection system based on the convolutional-LSTM network. Symmetry, 11. 10.3390/sym11040583

[7]

Rahman "Scalable machine learning-based intrusion detection system for IoT-enabled smart cities" Sustain. Cities Soc. (2020) 10.1016/j.scs.2020.102324

[8]

Panigrahi, R., Borah, S., Bhoi, A.K., Ijaz, M.F., Pramanik, M., Jhaveri, R.H., and Chowdhary, C.L. (2021). Performance assessment of supervised classifiers for designing intrusion detection systems: A comprehensive review and recommendations for future research. Mathematics, 9. 10.3390/math9060690

[9]

Arshad, J., Azad, M.A., Amad, R., Salah, K., Alazab, M., and Iqbal, R. (2020). A review of performance, energy and privacy of intrusion detection systems for IoT. Electronics, 9. 10.3390/electronics9040629

[10]

Dini, P., Elhanashi, A., Begni, A., Saponara, S., Zheng, Q., and Gasmi, K. (2023). Overview on intrusion detection systems design exploiting machine learning for networking cybersecurity. Appl. Sci., 13. 10.3390/app13137507

[11]

Spathoulas "Reducing false positives in intrusion detection systems" Comput. Secur. (2010) 10.1016/j.cose.2009.07.008

[12]

Aljnidi "Anomaly detection optimization using big data and deep learning to reduce false-positive" J. Big Data (2020) 10.1186/s40537-020-00346-1

[13]

Khraisat "A critical review of intrusion detection systems in the internet of things: Techniques, deployment strategy, validation strategy, attacks, public datasets and challenges" Cybersecurity (2021) 10.1186/s42400-021-00077-7

[14]

Chaabouni "Network Intrusion Detection for IoT Security Based on Learning Techniques" IEEE Commun. Surv. Tutor. (2019) 10.1109/comst.2019.2896380

[15]

Ahmad "Network intrusion detection system: A systematic study of machine learning and deep learning approaches" Trans. Emerg. Telecommun. Technol. (2021) 10.1002/ett.4150

[16]

Liu, H., and Lang, B. (2019). Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey. Appl. Sci., 9. 10.3390/app9204396

[17]

Agrawal "Federated Learning for intrusion detection system: Concepts, challenges and future directions" Comput. Commun. (2022) 10.1016/j.comcom.2022.09.012

[18]

Saranya "Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review" Procedia Comput. Sci. (2020) 10.1016/j.procs.2020.04.133

[19]

Adele, G., Borah, A., Paranjothi, A., Khan, M.S., and Poulkov, V.K. (2024, January 29–31). A Comprehensive Systematic Review of Blockchain-Based Intrusion Detection Systems. Proceedings of the 2024 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA. 10.1109/aiiot61789.2024.10578958

[20]

Baziana "Optical Data Center Networking: A Comprehensive Review on Traffic, Switching, Bandwidth Allocation, and Challenges" IEEE Access (2024) 10.1109/access.2024.3513214

[21]

Chen "Empowering Network Security With Programmable Switches: A Comprehensive Survey" IEEE Commun. Surv. Tutor. (2023) 10.1109/comst.2023.3265984

[22]

Kanade "Analysis of wireless network security in internet of things and its applications" Indian J. Eng. (2024) 10.54905/disssi.v21i55.e1ije1675

[23]

Moura "On the Road to Proactive Vulnerability Analysis and Mitigation Leveraged by Software Defined Networks: A Systematic Review" IEEE Access (2024) 10.1109/access.2024.3429269

[24]

Yahaya "A Two-Stage Privacy Preservation and Secure Peer-to-Peer Energy Trading Model Using Blockchain and Cloud-Based Aggregator" IEEE Access (2021) 10.1109/access.2021.3120737

[25]

Jamil "Peer-to-Peer Energy Trading Mechanism Based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid" IEEE Access (2021) 10.1109/access.2021.3060457

[26]

Mohamed "A Secured Advanced Management Architecture in Peer-to-Peer Energy Trading for Multi-Microgrid in the Stochastic Environment" IEEE Access (2021) 10.1109/access.2021.3092834

[27]

Ray "An Introduction to Dew Computing: Definition, Concept and Implications" IEEE Access (2018) 10.1109/access.2017.2775042

[28]

Lim "C2CFTP: Direct and Indirect File Transfer Protocols Between Clients in Client-Server Architecture" IEEE Access (2020) 10.1109/access.2020.2998725

[29]

Azhdari "Reliability optimization of multi-state networks in a star configuration with bi-level performance sharing mechanism and transmission losses" Reliab. Eng. Syst. Saf. (2022) 10.1016/j.ress.2022.108556

[30]

Lin "Performance analysis for a wireless sensor network of star topology with random nodes deployment" Wirel. Pers. Commun. (2017) 10.1007/s11277-017-4711-4

[31]

Jiang "Hybrid Low-Power Wide-Area Mesh Network for IoT Applications" IEEE Internet Things J. (2021) 10.1109/jiot.2020.3009228

[32]

Ghori, M.R., Wan, T.C., and Sodhy, G.C. (2020). Bluetooth low energy mesh networks: Survey of communication and security protocols. Sensors, 20. 10.3390/s20123590

[33]

Badea, A., Croitoru, V., and Gheorghica, D. (2015, January 7–9). Computer network vulnerabilities and monitoring. Proceedings of the 2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE), Bucharest, Romania. 10.1109/atee.2015.7133678

[34]

Aslan, Ö., Aktuğ, S.S., Ozkan-Okay, M., Yilmaz, A.A., and Akin, E. (2023). A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions. Electronics, 12. 10.3390/electronics12061333

[35]

Arogundade, O.R. (2023). Network security concepts, dangers, and defense best practical. Comput. Eng. Intell. Syst., 14.

[36]

Heiding "Research communities in cyber security vulnerability assessments: A comprehensive literature review" Comput. Sci. Rev. (2023) 10.1016/j.cosrev.2023.100551

[37]

Hussain, K., Rahmatyar, A.R., Riskhan, B., Sheikh, M.A.U., and Sindiramutty, S.R. (2024, January 8–9). Threats and Vulnerabilities of Wireless Networks in the Internet of Things (IoT). Proceedings of the 2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC), Tandojam, Pakistan. 10.1109/khi-htc60760.2024.10482197

[38]

Drăguşin, S.A., Bizon, N., and Boştinaru, R.N. (2024, January 27–28). Comprehensive Analysis Of Cyber-Attack Techniques And Vulnerabilities In Communication Channels Of Embedded Systems. Proceedings of the 2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Iasi, Romania. 10.1109/ecai61503.2024.10607432

[39]

Almazrouei "A Review on Attack Graph Analysis for IoT Vulnerability Assessment: Challenges, Open Issues, and Future Directions" IEEE Access (2023) 10.1109/access.2023.3272053

[40]

Khan "Resource Allocation in Networking and Computing Systems: A Security and Dependability Perspective" IEEE Access (2023) 10.1109/access.2023.3306534

[41]

Hidouri, A., Hajlaoui, N., Touati, H., Hadded, M., and Muhlethaler, P. (2022). A survey on security attacks and intrusion detection mechanisms in named data networking. Computers, 11. 10.3390/computers11120186

[42]

Ring "A survey of network-based intrusion detection data sets" Comput. Secur. (2019) 10.1016/j.cose.2019.06.005

[43]

Ullah "TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks" Comput. Netw. (2023) 10.1016/j.comnet.2023.110072

[44]

Akleylek "A Systematic Literature Review on Host-Based Intrusion Detection Systems" IEEE Access (2024) 10.1109/access.2024.3367004

[45]

Nallakaruppan "Enhancing Security of Host-Based Intrusion Detection Systems for the Internet of Things" IEEE Access (2024) 10.1109/access.2024.3355794

[46]

Remya "Enhancing Security in LLNs Using a Hybrid Trust-Based Intrusion Detection System for RPL" IEEE Access (2024) 10.1109/access.2024.3391918

[47]

Bakro "Building a Cloud-IDS by Hybrid Bio-Inspired Feature Selection Algorithms Along with Random Forest Model" IEEE Access (2024) 10.1109/access.2024.3353055

[48]

Kwon, H.Y., Kim, T., and Lee, M.K. (2022). Advanced intrusion detection combining signature-based and behavior-based detection methods. Electronics, 11. 10.3390/electronics11060867

[49]

Otoum "As-ids: Anomaly and signature based ids for the internet of things" J. Netw. Syst. Manag. (2021) 10.1007/s10922-021-09589-6

[50]

Dini "Design and Testing Novel One-Class Classifier Based on Polynomial Interpolation with Application to Networking Security" IEEE Access (2022) 10.1109/access.2022.3186026

Showing 50 of 175 references

Metrics

75

Citations

175

References

Details

Published: Mar 03, 2025
Vol/Issue: 14(3)
Pages: 87
License: View

Authors

L

Lorenzo Diana

Independent Researcher, 56100 Pisa, Italy

P

Pierpaolo Dini

Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56100 Pisa, Italy

D

Davide Paolini

Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56100 Pisa, Italy

Funding

PNRR CN 1 “Centro Nazionale per Simulation, Calculation and Analysis of High-Performance Data”, Award: I53C22000690001

“HARdware supported Post Quantum Over-the-Air Software Update and Intrusion Detection System for NExt Generation Secure CarS” Award: I53C22000690001

Italian Ministry of Education and Research Award: I53C22000690001

Cite This Article

Lorenzo Diana, Pierpaolo Dini, Davide Paolini (2025). Overview on Intrusion Detection Systems for Computers Networking Security. Computers, 14(3), 87. https://doi.org/10.3390/computers14030087

Overview on Intrusion Detection Systems for Computers Networking Security

You May Also Like