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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Abstract

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively.

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References

  1. Kamilaris A, Pitsillides A (2016) Mobile phone computing and the Internet of things: A survey. IEEE Internet Things J 3(6):885–898

    Google Scholar 

  2. Khan MA, Hussain S (2020) Energy efficient direction-based topology control algorithm for WSN. Wirel Sens Netw 12(3):37–47

    Google Scholar 

  3. Ndunagu JN et al (2022) Development of a Wireless Sensor Network and IoT-based Smart Irrigation System. Appl Environ Soil Sci 2022

  4. Chang J-Y, Shen T-H (2016) An efficient tree-based power saving scheme for wireless sensor networks with mobile sink. IEEE Sens J 16(20):7545–7557

    Google Scholar 

  5. Jondhale SR, Maheswar R, Lloret J (2022) Fundamentals of Wireless Sensor Networks. Received Signal Strength Based Target Localization and Tracking Using Wireless Sensor Networks. Springer, pp 1–19

    Google Scholar 

  6. Ullo SL, Sinha GR (2020) Advances in smart environment monitoring systems using IoT and sensors. Sensors 20(11):3113

    Google Scholar 

  7. Nguyen LT et al (2008) An energy efficient routing scheme for mobile wireless sensor networks. in 2008 IEEE International Symposium on Wireless Communication Systems. IEEE.

  8. Balid W, Tafish H, Refai HH (2017) Intelligent vehicle counting and classification sensor for real-time traffic surveillance. IEEE Trans Intell Transp Syst 19(6):1784–1794

    Google Scholar 

  9. Du X, Chen H-H (2008) Security in wireless sensor networks. IEEE Wirel Commun 15(4):60–66

    Google Scholar 

  10. Sert OC et al (2022) Temptracker: a service oriented temporal natural language processing based tool for document data characterization and social network analysis. Int Arab J Inf Technol 19(3):342–352

    MathSciNet  Google Scholar 

  11. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330

    Google Scholar 

  12. Sharma H, Haque A, Blaabjerg F (2021) Machine learning in wireless sensor networks for smart cities: a survey. Electronics 10(9):1012

    Google Scholar 

  13. Al-Fuhaidi B et al (2020) An efficient deployment model for maximizing coverage of heterogeneous wireless sensor network based on harmony search algorithm. J Sens 2020

  14. Sun Z et al (2017) An intrusion detection model for wireless sensor networks with an improved V-detector algorithm. IEEE Sens J 18(5):1971–1984

    Google Scholar 

  15. Latif S et al (2021) Intrusion detection framework for the Internet of things using a dense random neural network. IEEE Trans Industr Inf 18(9):6435–6444

    Google Scholar 

  16. Abdel-Basset M et al (2021) Semi-supervised spatiotemporal deep learning for intrusions detection in IoT networks. IEEE Internet Things J 8(15):12251–12265

    Google Scholar 

  17. Salim MM, Singh SK, Park JH (2021) Securing Smart Cities using LSTM algorithm and lightweight containers against botnet attacks. Appl Soft Comput 113:107859

    Google Scholar 

  18. Singh SK et al (2021) DeepBlockScheme: A deep learning-based blockchain driven scheme for secure smart city. HCIS 11(12):1–13

    MathSciNet  Google Scholar 

  19. Huang X (2021) Network intrusion detection based on an improved long-short-term memory model in combination with multiple spatiotemporal structures. Wirel Commun Mob Comput 2021

  20. Mohammadi M et al (2021) A comprehensive survey and taxonomy of the SVM-based intrusion detection systems. J Netw Comput Appl 178:102983

    Google Scholar 

  21. Makkar A, Park JH (2022) SecureCPS: Cognitive inspired framework for detection of cyber attacks in cyber–physical systems. Inf Process Manage 59(3):102914

    Google Scholar 

  22. Karami A (2018) An anomaly-based intrusion detection system in presence of benign outliers with visualization capabilities. Expert Syst Appl 108:36–60

    Google Scholar 

  23. Mohammadi S et al (2019) Cyber intrusion detection by combined feature selection algorithm. J Inf Secur Appl 44:80–88

    Google Scholar 

  24. Abualigah L et al (2023) Revolutionizing sustainable supply chain management: A review of metaheuristics. Eng Appl Artif Intell 126:106839

    Google Scholar 

  25. Safaldin M, Otair M, Abualigah L (2021) Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. J Ambient Intell Humaniz Comput 12(2):1559–1576

    Google Scholar 

  26. Otair M et al (2022) An enhanced grey wolf optimizer based particle swarm optimizer for intrusion detection system in wireless sensor networks. Wireless Netw 28(2):721–744

    Google Scholar 

  27. Al-Shourbaji I et al (2023) Artificial Ecosystem-Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems. Int J Comput Intell Syst 16(1):1–24

    Google Scholar 

  28. Chen H et al (2023) Hybrid slime mold and arithmetic optimization algorithm with random center learning and restart mutation. Biomimetics 8(5):396

    Google Scholar 

  29. Huh J-H (2018) Implementation of lightweight intrusion detection model for security of smart green house and vertical farm. Int J Distrib Sens Netw 14(4):1550147718767630

    Google Scholar 

  30. Houssein EH et al (2022) An efficient equilibrium optimizer with support vector regression for stock market prediction. Neural Comput Appl 34(4):3165–3200

    Google Scholar 

  31. Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer

  32. Mostafa RR et al (2022) Boosting chameleon swarm algorithm with consumption AEO operator for global optimization and feature selection. Knowl-Based Syst 246:108743

    Google Scholar 

  33. Wu D et al (2022) Enhance teaching-learning-based optimization for tsallis-entropy-based feature selection classification approach. Processes 10(2):360

    Google Scholar 

  34. Abualigah L, Diabat A (2022) Chaotic binary group search optimizer for feature selection. Expert Syst Appl 192:116368

    Google Scholar 

  35. BaturŞahin C, Abualigah L (2021) A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection. Neural Comput Appl 33(20):14049–14067

    Google Scholar 

  36. Panousopoulou A, Azkune M, Tsakalides P (2016) Feature selection for performance characterization in multi-hop wireless sensor networks. Ad Hoc Netw 49:70–89

    Google Scholar 

  37. Zhang Y (2012) Support vector machine classification algorithm and its application. in International conference on information computing and applications. Springer.

  38. Nadimi-Shahraki MH et al (2021) Mtv-mfo: Multi-trial vector-based moth-flame optimization algorithm. Symmetry 13(12):2388

    MathSciNet  Google Scholar 

  39. Nadimi-Shahraki MH et al (2021) An improved moth-flame optimization algorithm with adaptation mechanism to solve numerical and mechanical engineering problems. Entropy 23(12):1637

    MathSciNet  Google Scholar 

  40. Abualigah L et al (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250

    Google Scholar 

  41. Agushaka JO, Ezugwu AE, Abualigah L (2022) Dwarf mongoose optimization algorithm. Comput Methods Appl Mech Eng 391:114570

    MathSciNet  Google Scholar 

  42. Abualigah L et al (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    MathSciNet  Google Scholar 

  43. Oyelade ON et al (2022) Ebola optimization search algorithm: A new nature-inspired metaheuristic optimization algorithm. IEEE Access 10:16150–16177

    Google Scholar 

  44. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  45. Zamani H, Nadimi-Shahraki MH, Gandomi AH (2022) Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization. Comput Methods Appl Mech Eng 392:114616

    MathSciNet  Google Scholar 

  46. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  47. Abualigah L et al (2022) Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158

    Google Scholar 

  48. Nadimi-Shahraki MH et al (2022) GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems. J Comput Sci 61:101636

    Google Scholar 

  49. Nadimi-Shahraki MH et al (2021) Migration-based moth-flame optimization algorithm. Processes 9(12):2276

    MathSciNet  Google Scholar 

  50. Liu G et al (2022) An Enhanced Intrusion Detection Model Based on Improved kNN in WSNs. Sensors 22(4):1407

    Google Scholar 

  51. Ifzarne S, Hafidi I, Idrissi N (2021) Secure data collection for wireless sensor network. Emerging Trends in ICT for Sustainable Development. Springer, pp 241–248

    Google Scholar 

  52. Ifzarne S et al. (2021) Anomaly detection using machine learning techniques in wireless sensor networks. in Journal of Physics: Conference Series. IOP Publishing.

  53. Alazzam H, Sharieh A, Sabri KE (2020) A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Syst Appl 148:113249

    Google Scholar 

  54. Lv L et al (2020) A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine. Knowl-Based Syst 195:105648

    Google Scholar 

  55. Khalvati L, Keshtgary M, Rikhtegar N (2018) Intrusion detection based on a novel hybrid learning approach. J AI Data Min 6(1):157–162

    Google Scholar 

  56. Javeed D et al (2023) An Explainable and Resilient Intrusion Detection System for Industry 5.0. IEEE Transactions on Consumer Electronics

  57. Xun Y et al. (2023) Side Channel Analysis: A Novel Intrusion Detection System Based on Vehicle Voltage Signals. IEEE Transactions on Vehicular Technology

  58. Javeed D et al (2023) An Intelligent Intrusion Detection System for Smart Consumer Electronics Network. IEEE Transactions on Consumer Electronics

  59. Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Google Scholar 

  60. Farahani G (2020) Feature selection based on cross-correlation for the intrusion detection system. Security and Communication Networks, 2020

  61. Ekinci S et al (2022) An effective control design approach based on novel enhanced aquila optimizer for automatic voltage regulator. Artif Intell Rev 1–32.

  62. Ewees AA et al (2022) A cox proportional-hazards model based on an improved aquila optimizer with whale optimization algorithm operators. Mathematics 10(8):1273

    Google Scholar 

  63. Wang S et al (2021) An improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problems. Processes 9(9):1551

    Google Scholar 

  64. Salgotra R, Singh U (2017) Application of mutation operators to flower pollination algorithm. Expert Syst Appl 79:112–129

    Google Scholar 

  65. Tavallaee M et al (2009) A detailed analysis of the KDD CUP 99 data set. in 2009 IEEE symposium on computational intelligence for security and defense applications. Ieee

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Authors and Affiliations

  1. Computer Science Department, Al Al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  2. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  3. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah & Anas Ratib Alsoud

  4. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  5. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  6. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia

    Laith Abualigah & Saba Hussein Ahmed

  7. School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia

    Laith Abualigah

  8. Department of Mathematics, Facility of Science, The Hashemite University, P.O box 330127, Zarqa, 13133, Jordan

    Mohammad H. Almomani

  9. Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, UAE

    Raed Abu Zitar

  10. Department of Computer Science, Wenzhou-Kean University, Wenzhou, China

    Belal Abuhaija

  11. Department of Computer Information System, Zarqa University, P.O. Box 13132, Zarqa, Jordan

    Essam Said Hanandeh

  12. School of Information Engineering, Sanming University, Sanming, 365004, China

    Heming Jia

  13. Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 12311, Egypt

    Diaa Salama Abd Elminaam

  14. Computer Science Department, Faculty of Computer Science, Misr International University, Cairo, 12585, Egypt

    Diaa Salama Abd Elminaam

  15. Faculty of Computer Science & Engineering, Galala University, Suze, 435611, Egypt

    Mohamed Abd Elaziz

  16. Artificial Intelligence Research Center (AIRC), Ajman University, 346, Ajman, United Arab Emirates

    Mohamed Abd Elaziz

  17. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt

    Mohamed Abd Elaziz

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  1. Laith Abualigah
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  2. Saba Hussein Ahmed
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Correspondence to Laith Abualigah or Belal Abuhaija.

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Abualigah, L., Ahmed, S.H., Almomani, M.H. et al. Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System. Multimed Tools Appl 83, 59887–59913 (2024). https://doi.org/10.1007/s11042-023-17886-2

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