An Efficient Spotted Hyena Optimization Based Network Log Intrusions in Massive Server Infrastructure
Abstract
With advancement of information technology, intrusion is becoming more common in the internet era. The increased use of cloud services have also resulted in assaults on servers. In order to enhance network performance, this research offers a novel IDS (Intrusion Detection System) that can quickly identify large-scale server assaults in wireless networks. This work uses SHO (Spotted Hyena Optimisation) that mimics spotted hyena’s hunting behaviours. SHO, a swarm based meta-heuristics a method uses masses in solving issues and identifies important elements in server assaults. This optimisation algorithm improves detection and accuracy and is also used to detect web log hacking attacks and fake web pages. It outperforms other existing methods like SSO (Slap Swarm Optimisation), GWO (Grey Wolf Optimisation), and PSO (Particle Swarm Optimisation) algorithms in some applications. The goal of the experiment was to examine the suggested strategy using a common dataset. The suggested study appears to have produced notable outcomes for of F1 score, detection accuracy, and FAR (false alarm rate).
References
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F. Shahabi, F. Poorahangaryan, S.A. Edalatpanah, and H. Beheshti. “A multilevel image thresholding approach based on crow search algorithm and Otsu method,” International Journal of Computational Intelligence and Applications, vol. 19, no. 02, pp. 2050015, 2020.
J. Pokala, and B. Lalitha, “A novel intrusion detection system for RPL based IoT networks with bio-inspired feature selection and ensemble classifier,” Research square, pp. 2-24, 2021.
Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad. “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, pp. 1-29, 2021.
S. Meftah, T. Rachidi, and N. Assem. “Network based intrusion detection using the UNSW-NB15 dataset,” International Journal of Computing and Digital Systems, vol. 8, no. 5, pp. 477-487, 2019.
P. Deshpande, S.C. Sharma, S.K. Peddoju, and S. Junaid. “HIDS: A host based intrusion detection system for cloud computing environment,” International Journal of System Assurance Engineering and Management, vol. 9, pp. 567-576, 2018.
U.K. Raut. “Log based intrusion detection system,” IOSR Journal of Computer Engineering, 2018, vol. 20, no. 5, pp. 15-22.
M. Hasan, M.M. Islam, M.I.I. Zarif, and M.M.A. Hashem. “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches,” Internet of Things, vol. 7, pp. 100059, 2019.
Y. Zhang, and Y. Wang. “A novel energy-aware bio-inspired clustering scheme for IoT communication,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 10, pp. 4239-4248, 2020.
A. Forestiero. “Metaheuristic algorithm for anomaly detection in Internet of Things leveraging on a neural-driven multiagent system,” Knowledge-Based Systems, vol. 228, pp. 107241, 2021.
S. Smys, A. Basar, and H. Wang. “Hybrid intrusion detection system for internet of things (IoT),” Journal of ISMAC, vol.2, no. 04, pp. 190-199, 2020.
A. Brandao, and P. Georgieva. “Log Files Analysis for Network Intrusion Detection,” In 2020 IEEE 10th International Conference on Intelligent Systems (IS), Varna, Bulgaria, 28-30 August 2020, pp. 328-333.
W. Bul'ajoul, A. James, and M. Pannu. “Improving network intrusion detection system performance through quality of service configuration and parallel technology,” Journal of Computer and System Sciences, vol. 81, no. 6, pp. 981-999, 2015.
D. Attique, H. Wang, and P. Wang. “Fog-assisted deep-learning-empowered intrusion detection system for RPL-based resource-constrained smart industries,” Sensors, vol. 22, no, 23, pp. 1-17, 2022.
F. Jemili, and Bouras. “Intrusion detection based on big data fuzzy analytics,” In Open Data. IntechOpen,2021.
T.H. Morris, Z. Thornton, and I. Turnipseed. “Industrial control system simulation and data logging for intrusion detection system research,” 7th annual southeastern cyber security summit, pp. 3-4, 2015.
R. Vinayakumar, M. Alazab, K.P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman. “Deep learning approach for intelligent intrusion detection system,” IEEE Access, vol. 7, pp. 41525-41550, 2019.
A. Brandao, and P. Georgieva. “Automatic log analysis to prevent cyber attacks,” Advances in Intelligent Systems Research and Innovation, pp. 1-25,2022.
R.K. Vigneswaran, R. Vinayakumar, K.P. Soman, and P. Poornachandran. “Evaluating shallow and deep neural networks for network intrusion detection systems in cyber security," In 2018 9th International conference on computing, communication and networking technologies (ICCCNT), Bengaluru, India, 10-12 July 2018, pp. 1-6.
N. Dionísio, F. Alves, P.M. Ferreira, and A. Bessani. “Cyberthreat detection from twitter using deep neural networks,” In 2019 international joint conference on neural networks (IJCNN), Budapest, Hungary, 14-19 July 2019, pp. 1-8. IEEE.
M.A. Rassam, M. Maarof, and A. Zainal. “Big Data Analytics Adoption for Cybersecurity: A Review of Current Solutions, Requirements, Challenges and Trends,” Journal of Information Assurance & Security, vol. 12, no, 4, pp. 124-145, 2017.
E. Kabir, J. Hu, H. Wang, and G. Zhuo. “A novel statistical technique for intrusion detection systems,”Future Generation Computer Systems, vol. 79, pp. 1-39, 2018.
L. Ashiku, and C. Dagli. “Network intrusion detection system using deep learning,” Procedia Computer Science, vol. 185, pp. 239-247, 2021.
G. Dhiman, and V. Kumar. “Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications,”Advances in Engineering Software, vol. 114, pp. 48-70, 2017.
M. Sabahno, and F. Safara. “ISHO: improved spotted hyena optimization algorithm for phishing website detection,” Multimedia Tools and Applications, vol. 81, no. 24, pp. 34677-34696, 2022.
R. Taj, A Machine Learning Framework for Host Based Intrusion Detection Using System Call Abstraction. Master’s Thesis, Dalhousie University, Halifax, NS, Canada, 2020.
C.M. Nalayini, and J. Katiravan. “A new IDS for detecting DDoS attacks in wireless networks using spotted hyena optimization and fuzzy temporal CNN,” Journal of Internet Technology, vol. 24, no. 1, pp. 23-34, 2023.
A. Alsaleh, and W. Binsaeedan. “The influence of salp swarm algorithm-based feature selection on network anomaly intrusion detection,” IEEE Access, vol. 9, pp. 112466-112477, 2021.
A. Alzaqebah, I. Aljarah, O. Al-Kadi, and R. Damaševičius. “A modified grey wolf optimization algorithm for an intrusion detection system,” Mathematics, vol. 10, no. 6, pp. 1-16, 2022.
R.O. Ogundokun, J.B. Awotunde, P. Sadiku, E.A. Adeniyi, M. Abiodun, and O.I. Dauda. An enhanced intrusion detection system using particle swarm optimization feature extraction technique.Procedia Computer Science, vol. 193, pp.504-512, 2021.
G. Dhiman, and V. Kumar. “Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications,” Advances in Engineering Software, vol. 114, pp. 48-70, 2017.
C.M. Nalayini, and J. Katiravan. “A new IDS for detecting DDoS attacks in wireless networks using spotted hyena optimization and fuzzy temporal CNN,” Journal of Internet Technology, vol. 24, no. 1, pp. 23-34, 2023.
A.A. Almazroi, and N. Ayub. “Deep learning hybridization for improved malware detection in smart Internet of Things,”Scientific Reports, vol. 14, no. 1, pp. 1-18, 2024.
J. Zhang, J.D. Peter, A. Shankar, and W. Viriyasitavat. “Public cloud networks oriented deep neural networks for effective intrusion detection in online music education,” Computers and Electrical Engineering, vol. 115, pp. 109095, 2024.
S.A. Edalatpanah, F.S. Hassani, F. Smarandache, A. Sorourkhah, D. Pamucar, and B. Cui. “A hybrid time series forecasting method based on neutrosophic logic with applications in financial issues,” Engineering applications of artificial intelligence, vol. 129, pp. 107531, 2024.
D.S. Vladislav. “Leakage Detection in Water Pipes: An Approach of Smart Water,” Big Data and Computing Visions, vol. 3, no. 1, pp. 8-14, 2023.
C.C. Jorge, H.N. Jorge, M.R. Wendell, S.A. Edalatpanah, A.B. Shariq, S. Naz, J.C. Javier, and P.E. Gabriel. “Novel characterization and tuning methods for integrating processes,” International Journal of Information Technology, vol. 16, no. 3, 1387-1395. 2024.
S. Mohammadi, N. Hemati, and N. Mohammadi. “Speech recognition system based on machine learning in persian language,” Computational algorithms and numerical dimensions, vol. 1, no. 2, pp. 72-83, 2022.
M. Teimoori, H. Taghizadeh, J. Pourmahmoud, and Honarmand M. Azimi. “A multi-objective grey wolf optimization algorithm for aircraft landing problem,” Journal of applied research on industrial engineering, vol. 8, no. 4, pp. 386-398, 2021.
S. Ali, Q. Li, and A. Yousafzai. “Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: A survey,” Ad Hoc Networks, vol. 152, pp. 1-31, 2024.
M. Dirik. “Detection of counterfeit banknotes using genetic fuzzy system,” Journal of fuzzy extension and applications, vol. 3, no. 4, pp. 302-312, 2022.
P. Ghasemi, H. Hemmaty, A. Pourghader Chobar, M.R. Heidari, and M. Keramati. “A multi-objective and multi-level model for location-routing problem in the supply chain based on the customer’s time window” Journal of Applied Research on Industrial Engineering, vol. 10, no. 3, pp.412-426, 2023.
T. Wagner, A. Gepperth, and E. Engels. “A framework for the automated parameterization of a sensorless bearing fault detection pipeline,”arXiv preprint arXiv:2303.08858, pp. 1-8,2023.
M. Dirik. “Type-2 fuzzy logic controller design optimization using the PSO approach for ECG prediction,”Journal of fuzzy extension and applications, vol. 3, no. 2, pp. 158-168, 2022.
F. Shahabi, F. Poorahangaryan, S.A. Edalatpanah, and H. Beheshti. “A multilevel image thresholding approach based on crow search algorithm and Otsu method,” International Journal of Computational Intelligence and Applications, vol. 19, no. 02, pp. 2050015, 2020.
Published
2025-02-03
How to Cite
R, Rajalingam; K, Kavitha.
An Efficient Spotted Hyena Optimization Based Network Log Intrusions in Massive Server Infrastructure.
Yugoslav Journal of Operations Research, [S.l.], feb. 2025.
ISSN 2334-6043.
Available at: <https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/1317>. Date accessed: 11 feb. 2025.
doi: https://doi.org/10.2298/YJOR2402150057R.
Issue
Section
Research Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
