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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Innovative host-based intrusion detection systems : Safeguarding networks in the digital Era

, , , * ,

* Corresponding author · click or hover a name for details

pp. 1–22Online FirstMay 2026DOI: 10.47974/JIOS-1981XML
Received:
01 Oct 2024
Published Online:
22 May 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1981
Pages:
1–22

Abstract

The increasing frequency of attacks on computer systems, networks, and information systems, in general necessitate robust defense mechanisms and that can be detected by Intrusion-Detection Systems (IDS). IDS, which notify system administrators of potential intrusions in a system. As the scope and intricacy of network attacks continue to grow, there has been a clear shift in focus towards the network itself, rather than individual operating systems. Combatting these attacks through network-based intrusion detection poses its own set of challenges, including the management of the immense amount of data generated during network auditing. Moreover, the complex nature of these attacks often results in related events being spread out across various network locations. To overcome these hurdles, this article proposes a fresh and seamless technique for detecting intrusions. Fueled by a combination of frameworks and the Python programming language, this method aims to enhance the effectiveness of intrusion detection systems by addressing the intricacies inherent in network attacks. The system’s defense against viruses and unauthorized network or system access is its main objective. Network-based IDS and host-based IDS are the two different categories of intrusion detection systems. Furthermore, the article discusses the integration of this host-based IDS method within existing security frameworks, emphasizing its adaptability and compatibility with diverse system architectures. The exploration of this novel approach provides valuable insights into the ongoing efforts to fortify information systems against evolving threats, showcasing the importance of innovation in the field of intrusion detection. In summary, this research contributes to the ongoing discourse on intrusion detection by proposing a novel method that aligns with contemporary security needs. The implementation of this method promises to reinforce the defenses of computer systems against the ever-evolving landscape of cyber threats.

Keywords

Host-based IDSNetwork security monitoringSecurity alertsScapyThreat detectionAnomalyNetwork traffic analysis

Subject Classifications

68M2568M1068M12

References

[1] A. Pal Singh and M. Deep Singh, “Analysis of host-based and network-based intrusion detection system,” International Journal of Computer Network and Information Security, vol. 6, no. 8, pp. 41–47 (Aug. 2014), doi: 10.5815/ijcnis.2014.08.06.
[2] S. Kumar, S. Gupta, and S. Arora, “Research trends in network-based intrusion detection systems: A review,” IEEE Access, vol. 9, pp. 157761–157779 (2021), doi: 10.1109/ACCESS.2021.3129775.
[3] B. Ghosh and R. Sil, “Analysis of cyber risk in finance: The state-of-the-art and future research trends,” in Contemporary Social Physics, A. Rai, S. Karmakar, S. Chatterjee, and J. K. Pandey, Eds. Cham, Switzerland: Springer (2024), doi: 10.1007/978-3-031-77596-3_17.
[4] A. Thakkar and R. Lohiya, “A survey on intrusion detection system: Feature selection, model, performance measures, application perspective, challenges, and future research directions,” Artificial Intelligence Review, vol. 55, no. 1, pp. 453–563 (2021), doi: 10.1007/s10462-021-10037-9.
[5] J. Liu, K. Xiao, L. Luo, Y. Li, and L. Chen, “An intrusion detection system integrating network-level intrusion detection and host-level intrusion detection,” in Proc. IEEE 20th Int. Conf. Softw. Quality, Rel. Security (QRS) (2020), doi: 10.1109/QRS51102.2020.00028.
[6] A. Biswas, R. Sil, and A. Roy, “A study on application of interplanetary file system,” in Communication and Intelligent Systems, H. Sharma, M. K. Gupta, G. S. Tomar, and W. Lipo, Eds., vol. 204. Singapore: Springer (2021), doi: 10.1007/978-981-16-1089-9_79.
[7] Y. Shin and K. Kim, “Comparison of anomaly detection accuracy of host-based intrusion detection systems based on different machine learning algorithms,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 2 (2020), doi: 10.14569/IJACSA.2020.0110233.
[8] A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A deep learning approach for network intrusion detection system,” in Proc. 9th EAI Int. Conf. Bio-Inspired Inf. Commun. Technol. (BIONETICS) (2016), doi: 10.4108/eai.3-12-2015.2262516.
[9] N. A. Azeez, T. M. Bada, S. Misra, A. Adewumi, C. Van der Vyver, and R. Ahuja, “Intrusion detection and prevention systems: An updated review,” in Data Management, Analytics and Innovation, pp. 685–696 (2019), doi: 10.1007/978-981-32-9949-8_48.
[10] Q. Kang, L. Shi, M. Zhou, X. Wang, Q. Wu, and Z. Wei, “A distance-based weighted undersampling scheme for support vector machines and its application to imbalanced classification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4152–4165 (2018), doi: 10.1109/TNNLS.2017.2755595.
[11] R. A. Bridges, T. R. Glass-Vanderlan, M. D. Iannacone, M. S. Vincent, and Q. Chen, “A survey of intrusion detection systems leveraging host data,” ACM Computing Surveys, vol. 52, no. 6, pp. 1–35 (2019), doi: 10.1145/3344382.
[12] Y. Ou, Y. Lin, Y. Zhang, and Y. Ou, “The design and implementation of host-based intrusion detection system,” in Proc. 3rd Int. Symp. Intell. Inf. Technol. Security Informatics (IITSI) (2010), doi: 10.1109/IITSI.2010.127.
[13] R. Gassais, N. Ezzati-Jivan, J. M. Fernandez, D. Aloise, and M. R. Dagenais, “Multi-level host-based intrusion detection system for internet of things,” Journal of Cloud Computing, vol. 9, no. 1 (2020), doi: 10.1186/s13677-020-00206-6.
[14] S. N. Chari and P.-C. Cheng, “Bluebox,” ACM Transactions on Information and System Security, vol. 6, no. 2, pp. 173–200 (May 2003), doi: 10.1145/762476.762477.
[15] L. Vokorokos and A. Balaz, “Host-based intrusion detection system,” in Proc. IEEE 14th Int. Conf. Intell. Eng. Syst. (INES) (2010), doi: 10.1109/INES.2010.5483815.
[16] M. Liu, Z. Xue, X. Xu, C. Zhong, and J. Chen, “Host-based intrusion detection system with system calls,” ACM Computing Surveys, vol. 51, no. 5, pp. 1–36 (2018), doi: 10.1145/3214304.
[17] S. Saif, A. A. Ansari, S. Biswas, and D. Giri, “A comprehensive analysis of machine learning-based intrusion detection systems: Evaluating datasets and algorithms for internet of things,” Journal of Cyber Security Technology, vol. 10, no. 1, pp. 1–27 (2026), doi: 10.1080/23742917.2024.2447124.
[18] M. Wolf, K. Bergner, P. Förtsch, and D. Landes, “Implications of applying emulation data to machine learning-based intrusion detection systems,” Journal of Information Security and Applications, vol. 97, Art. no. 104362 (Mar. 2026), doi: 10.1016/j.jisa.2025.104362.
[19] P. M. S. Makhdoomi, M. Ikhlas, A. Khursheed, F. Hilal, Z. A. Najar, J. Hameed, and S. Sharma, “Network-based intrusion detection: A comparative analysis of machine learning approaches for improved security,” Journal of Cyber Security Technology, vol. 10, no. 1, pp. 1–28 (2026), doi: 10.1080/23742917.2024.2447119.
[20] H. Bostani and M. Sheikhan, “Hybrid of anomaly-based and specification-based IDS for internet of things using unsupervised OPF based on MapReduce approach,” Computer Communications, vol. 98, pp. 52–71 (2017), doi: 10.1016/j.comcom.2016.12.001.
[21] I. Martins, J. S. Resende, P. R. Sousa, S. Silva, L. Antunes, and J. Gama, “Host-based IDS: A review and open issues of an anomaly detection system in IoT,” Future Generation Computer Systems, vol. 133, pp. 95–113 (2022), doi: 10.1016/j.future.2022.03.001.
[22] F. S. Masoodi, “Intrusion detection in the age of deep learning: An introduction,” in Deep Learning for Intrusion Detection, F. S. Masoodi and A. Bamhdi, Eds. (2026), doi: 10.1002/9781394285198.ch1.
[23]  O. Ajayi, A. Gangopadhyay, R. F. Erbacher, and C. Bursat, “Developing cross-domain host-based intrusion detection,” Electronics, vol. 11, no. 21, Art. no. 3631 (2022), doi: 10.3390/electronics11213631.
[24] F. Alenezi, S. Almowuena, A. Alenezi, and M. J. F. Alenazi, “CIDS: A collaborative intrusion detection system approach for SDN-based distributed industrial plants,” Trans. Emerg. Telecommun. Technol., vol. 37, no. 1, Art. no. e70327 (2026), doi: 10.1002/ett.70327.
[25] X. Zhao, M. He, and X. Wang, “A real-time channel-level intrusion detection system based on multimodal learning,” IEEE Internet of Things Journal, vol. 13, no. 7, pp. 12598–12612 (Apr. 1, 2026), doi: 10.1109/JIOT.2026.3651905.
[26] A. Tedyyana and O. Ghazali, “Real-time hypertext transfer protocol intrusion detection system on web server using Firebase cloud messaging,” in Proc. 4th Int. Conf. Appl. Sci. Technol. Eng. Sci. (2021), doi: 10.5220/0010946300003260.
[27] L. Liu, M. Simsek, B. Kantarci, M. Bagheri, and P. Djukic, “Collaborative feature maps of networks and hosts for AI-driven intrusion detection,” in IEEE Global Communications Conference 2022, pp. 2662–2667 (2022), doi: 10.1109/GLOBECOM48099.2022.10000985.
[28] J. H. Ring, C. M. Van Oort, S. Durst, V. White, J. P. Near, and C. Skalka, “Methods for host-based intrusion detection with deep learning,” Digital Threats: Research and Practice, vol. 2, no. 4, pp. 1–29 (2021), doi: 10.1145/3461462.
[29] V. Bukac, P. Tucek, and M. Deutsch, “Advances and challenges in standalone host-based intrusion detection systems,” in Trust, Privacy and Security in Digital Business, pp. 105–117 (2012), doi: 10.1007/978-3-642-32287-7_9.
[30] T. B. Ogunseyi, G. Thiyagarajan, H. He, V. Bist, and Z. Du, “Performance analysis of explainable deep learning-based intrusion detection systems for IoT networks: A systematic review,” Sensors, vol. 26, no. 2, Art. no. 363 (2026), doi: 10.3390/s26020363.
[31] D. Talapula, A. Kumar, K. Ravulakollu, and M. Kumar, “A novel intrusion detection system in cloud infrastructure using deep learning technique,” J. Discrete Math. Sci. Cryptogr., vol. 26, no. 5, pp. 1277–1303 (2023), doi: 10.47974/JDMSC-1739.
[32] R. Kowtharapu, C. H. S. Kiran, and P. A. Kumari, “Dynamic intrusion detection for internet of drones using glowworm swarm optimization,” Scientific Reports (2026), doi: 10.1038/s41598-026-44789-7.

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