Peer-to-Peer Networking and Applications

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01.04.2025

Intrusion defense: Leveraging ant colony optimization for enhanced multi-optimization in network security

verfasst von: Chetan Gupta, Amit Kumar, Neelesh Kumar Jain

Erschienen in: Peer-to-Peer Networking and Applications | Ausgabe 2/2025

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Abstract

Der Artikel befasst sich mit den Herausforderungen der Netzwerksicherheit im Zeitalter der zunehmenden Internetnutzung und konzentriert sich dabei auf Intrusion Detection Systems (IDS). Er diskutiert die Grenzen bestehender IDS-Methoden und führt einen hybriden Ansatz ein, der Ameisenkolonien-Optimierung (ACO) und Assoziationsregeln-Mining (ARM) zur Verbesserung der Multioptimierung einsetzt. Die vorgeschlagene Methode zielt darauf ab, die Genauigkeit von IDS zu verbessern und gleichzeitig Fehlalarme zu verringern. Die Autoren heben die Vorteile ihres Ansatzes hervor, einschließlich eines besseren Umgangs mit großen und komplexen Datensätzen, und demonstrieren seine Wirksamkeit durch umfassende experimentelle Ergebnisse. Die Studie schließt mit der Betonung der potenziellen Anwendungen des ARM-ACO-Modells in verschiedenen Bereichen wie der wissenschaftlichen Forschung und realen Fragen wie Stimmungsanalyse und Krebserkennung.

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Vorheriger Artikel A highly secure and stable energy aware multi-objective constraints-based hybrid optimization algorithms for effective optimal cluster head selection and routing in wireless sensor networks

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Manimurugan S, Majdi A-Q, Mustaffa Mohmmed C, Narmatha RV (2020) Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system. Microprocess Microsyst. https://doi.org/10.1016/j.micpro.2020.103261CrossRef

Mhawi DN, Hashim SH (2023) Proposed Hybrid Ensemble Learning Algorithms for an Efficient Intrusion Detection System. Iraqi Journal of Computers, Communications, Control & Systems Engineering (IJCCCE). 22(2). https://doi.org/10.33103/uot.ijccce.22.2.7.

Ramesh Kumar M, Sudhakaran P (2024) Securing IoT networks: A robust intrusion detection system leveraging feature selection and LGBM. Peer-to-Peer Networking Applications 17:2921–2943. https://doi.org/10.1007/s12083-024-01721-yCrossRef

Qasem AA, Qutqut MH, Alhaj F et al (2024) SRFE: A stepwise recursive feature elimination approach for network intrusion detection systems. Peer-to-Peer Networking Appl. https://doi.org/10.1007/s12083-024-01763-2CrossRef

Nandhini U, SVN, S. (2024) An improved Harris Hawks optimizer based feature selection technique with effective two-staged classifier for network intrusion detection system. Peer-to-Peer Networking Appl 17:2944–2978. https://doi.org/10.1007/s12083-024-01727-6CrossRef

Mashuqur Rahman Mazumder AKM, Mohammed Kamruzzaman N, Akter N, Arbe N, Rahman MM. (2021) Network intrusion detection using hybrid machine learning model. International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). https://doi.org/10.1109/icaect49130.2021.9392483.

Narayanasami S, Sengan S, Khurram S, Arslan F, Murugaiyan SK, Rajan R, Peroumal V, Dubey AK, Srinivasan S, Sharma DK (2021) Biological feature selection and classification techniques for intrusion detection on BAT. Wireless Pers Commun. https://doi.org/10.1007/s11277-021-08721-8CrossRef

Ramaiah M, Chandrasekaran V, Ravi V, Kumar N (2021) An intrusion detection system using optimized deep neural network architecture. Transactions Emerg Telecommun Technol. 32(4). https://doi.org/10.1002/ett.4221

Mighan SN, Kahani M (2020) A novel scalable intrusion detection system based on deep learning. Int J Inf Secur 20(3):387–403. https://doi.org/10.1007/s10207-020-00508-5CrossRef

10.

Maseer ZK, Yusof R, Bahaman N, Mostafa SA, Foozy CFM (2021) Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset. IEEE Access 9:22351–22370. https://doi.org/10.1109/access.2021.3056614CrossRef

11.

Selvakumar B, Muneeswaran K (2019) Firefly algorithm based feature selection for network intrusion detection. Computers Secur 81:148–155. https://doi.org/10.1016/j.cose.2018.11.005CrossRef

12.

Subba B (2019) A neural network based NIDS framework for intrusion detection in contemporary network traffic. IEEE Int Conference Adv Networks Telecommun Systems (ANTS). https://doi.org/10.1109/ants47819.2019.9117966CrossRef

13.

Taher KA, Mohammed Yasin Jisan B, Rahman MM (2019) Network intrusion detection using supervised machine learning technique with feature selection. Int Confer Robotics, Electrical Signal Processing Techniques (ICREST). https://doi.org/10.1109/icrest.2019.8644161.

14.

Shao Y, Liu B, Li G, Wang S (2017) Software defect prediction based on class-association rules. Second Int Conference Reliability Systems Eng (ICRSE). https://doi.org/10.1109/icrse.2017.8030774CrossRef

15.

Devan P, Khare N (2020) An efficient XGBoost–DNN-based classification model for network intrusion detection system. Neural Comput Appl 32(16):12499–12514. https://doi.org/10.1007/s00521-020-04708-xCrossRef

16.

Rajadurai H, Gandhi UD (2020) A stacked ensemble learning model for intrusion detection in wireless network. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04986-5CrossRef

17.

Wei W, Chen S, Lin Q, Ji J, Chen J (2020) A multi-objective immune algorithm for intrusion feature selection. Appl Soft Comput 95:106522. https://doi.org/10.1016/j.asoc.2020.106522CrossRef

18.

Baek S, Kwon D, Suh SC, Kim H, Kim I, Kim J (2021) Clustering-based label estimation for network anomaly detection. Digital Commun Networks 7(1):37–44. https://doi.org/10.1016/j.dcan.2020.06.001CrossRef

19.

Kushwah GS, Ranga V (2020) Voting extreme learning machine based distributed denial of service attack detection in cloud computing. J Inform Security Appl 53:102532. https://doi.org/10.1016/j.jisa.2020.102532CrossRef

20.

Choudhary S, Kesswani N (2020) Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 datasets using deep learning in IoT. Procedia Computer Science 167:1561–1573. https://doi.org/10.1016/j.procs.2020.03.367CrossRef

21.

Almiani M, AbuGhazleh A, Al-Rahayfeh A, Atiewi S, Razaque A (2020) Deep recurrent neural network for IoT intrusion detection system. Simulation Modeling Practice Theory 101:102031. https://doi.org/10.1016/j.simpat.2019.102031CrossRef

22.

Singh S, Banerjee S (2020) Machine learning mechanisms for network anomaly detection system: A review. Int Conference Commun Signal Processing (ICCSP). https://doi.org/10.1109/iccsp48568.2020.9182197CrossRef

23.

Abrar I, Ayub Z, Masoodi F, Bamhdi AM (2020) A machine learning approach for intrusion detection system on NSL-KDD dataset. Int Conference Smart Electronics Commun (ICOSEC). https://doi.org/10.1109/icosec49089.2020.9215232CrossRef

24.

Su T, Sun H, Zhu J, Wang S, Li Y (2020) BAT: deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access 8:29575–29585. https://doi.org/10.1109/access.2020.2972627CrossRef

25.

Almasoudy FH, Al-Yaseen WL, Idrees AK (2020) Differential evolution wrapper feature selection for intrusion detection system. Procedia Computer Sci 167:1230–1239. https://doi.org/10.1016/j.procs.2020.03.438CrossRef

26.

Kunal, Dua M (2020). Attribute selection and ensemble classifier based novel approach to intrusion detection system. Procedia Computer Sci. 167:2191–2199https://doi.org/10.1016/j.procs.2020.03.271

27.

Yu Y, Bian N (2020) An intrusion detection method using few-shot learning. IEEE Access 8:49730–49740. https://doi.org/10.1109/access.2020.2980136CrossRef

28.

Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. Computational Intell Security. https://doi.org/10.1109/cisda.2009.5356528CrossRef

29.

Behal S, Kumar K (2016) Trends in validation of DDoS research. Procardia Computer Sci 85:7–15. https://doi.org/10.1016/j.procs.2016.05.170CrossRef

30.

Khraisat A, Gondal I, Vamplew P, Kamruzzaman J (2019) Survey of intrusion detection systems techniques, datasets and challenges. Cyber Security. 2(1). https://doi.org/10.1186/s42400-019-0038-7.

31.

Balasaraswathi VR, Sugumaran M, Hamid Y (2017) Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms. J Commun Inform Networks 2(4):107–119. https://doi.org/10.1007/s41650-017-0033-7CrossRef

32.

Tsai C, Hsu YF, Lin C, Lin W (2009) Intrusion detection by machine learning: A review. Expert Syst Appl 36(10):11994–12000. https://doi.org/10.1016/j.eswa.2009.05.029CrossRef

33.

Behal S, Kumar K (2016) Trends in validation of DDoS research. Procedia Computer Sci 85:7–15. https://doi.org/10.1016/j.procs.2016.05.170CrossRef

34.

Ibrahim ZK, Thanon MY (2021) Performance comparison of intrusion detection system using three different machine learning algorithms. 6th International Conference on Inventive Computation Technologies (ICICT). 1116–1124, https://doi.org/10.1109/ICICT50816.2021.9358775.

35.

Parimala G, Kayalvizhi R (2021). An effective intrusion detection system for securing IoT using feature selection and deep learning. Int Conference Comp Commun Informatics (ICCCI). 1–4. https://doi.org/10.1109/ICCCI50826.2021.9402562.

36.

Stiawan D et al (2021) An approach for optimizing ensemble intrusion detection systems. IEEE Access 9:6930–6947. https://doi.org/10.1109/ACCESS.2020.3046246CrossRef

37.

Li L-H, Ahmad R, Tsai W-C, Sharma AK (2021) A feature selection based DNN for intrusion detection system. 15th International Conference on Ubiquitous Information Management and Communication (IMCOM). 1–8, https://doi.org/10.1109/IMCOM51814.2021.9377405.

38.

Ibrahim ZK, Thanon MY (2021) Performance comparison of intrusion detection system using three different machine learning algorithms. International Conference on Inventive Computation Technologies (ICICT). 1116–1124. https://doi.org/10.1109/ICICT50816.2021.9358775.

39.

Saber A, Abbas M, Fergani B (2021) Two-dimensional intrusion detection system: a new feature selection technique. 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), 69–74. https://doi.org/10.1109/IHSH51661.2021.9378721.

40.

Sharma M, Sharma M, Elmiligi H, Gebali F (2021) A novel intrusion detection system for RPL-based cyber-physical systems. IEEE Canadian J Elec Comp Eng. https://doi.org/10.1109/ICJECE.2021.3053231CrossRef

41.

Ahmad HamdiQaiwmchi N, Amintoosi H, Mohajerzadeh A (2021) Intrusion detection system based on gradient corrected online sequential extreme learning machine. IEEE Access 9:4983–4999. https://doi.org/10.1109/ACCESS.2020.3047933CrossRef

42.

Jiang L, Kong G, Li C (2021) Wrapper framework for test-cost-sensitive feature selection. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51(3):1747–1756. https://doi.org/10.1109/TSMC.2019.2904662CrossRef

43.

Lifandali O, Abghour N, Chiba Z (2023) Feature selection using a combination of ant colony optimization and random forest algorithms applied to isolation forest based intrusion detection system. Procedia Computer Science 220:796–805. https://doi.org/10.1016/j.procs.2023.03.106CrossRef

44.

Alsarhan A, Alauthman M, Alshdaifat E, Al-Ghuwairi AR, Al-Dubai A (2021) Machine learning-driven optimization for svm-based intrusion detection system in vehicular ad hoc networks. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-02963-xCrossRef

45.

Oleiwi HW, Mhawi DN, Al-Raweshidy H (2022). MLTs-ADCNs: Machine Learning Techniques for Anomaly Detection in Communication Networks. In IEEE Access, 10:91006–91017. https://doi.org/10.1109/ACCESS.2022.3201869.

46.

Mhawi DN, Aldallal A, Hassan S (2022) Advanced Feature-Selection-Based Hybrid Ensemble Learning Algorithms for Network Intrusion Detection Systems. Symmetry 14(7):1461. https://doi.org/10.3390/sym14071461CrossRef

47.

Kocher G, Kumar G (2021) Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges. Soft Comput 25:9731–9763. https://doi.org/10.1007/s00500-021-05893-0CrossRef

48.

Lo W, Alqahtani H, Thakur K, Almadhor A, Chander S, Kumar G (2022) A hybrid deep learning based intrusion detection system using spatial-temporal representation of in-vehicle network traffic, Vehicular Communications, Volume 35. ISSN 100471:2214–2096. https://doi.org/10.1016/j.vehcom.2022.100471CrossRef

49.

Gulshan Kumar, Hamed Alqahtani (2022). Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions, CMES - Computer Modeling in Engineering and Sciences. 134(1):89–119, ISSN 1526–1492, https://doi.org/10.32604/cmes.2022.020724

50.

Kumar G, Kumar K, Sachdeva M (2010) The use of artificial intelligence based techniques for intrusion detection: a review. Artif Intell Rev 34:369–387. https://doi.org/10.1007/s10462-010-9179-5CrossRef

51.

Kumar G, Kumar K (2011) An information theoretic approach for feature selection. Security Commun Networks J 5:178–185. https://doi.org/10.1002/sec.303CrossRef

52.

Kumar G, Kumar K (2013) Design of an Evolutionary Approach for Intrusion Detection. Scientific World J. https://doi.org/10.1155/2013/962185CrossRef

53.

Dwivedi S, Vardhan M, Tripathi S (2021) Building an Efficient Intrusion detection system using grasshopper optimization algorithm for anomaly detection. Clust Comput 24(3):1881–1900. https://doi.org/10.1007/s10586-020-03229-5CrossRef

54.

Sumaiya Thaseen I, Saira Banu J, Lavanya K, Rukunuddin Ghalib M, Abhishek K (2020) An integrated intrusion detection system using correlation based attribute selection and artificial neural network. Transact Emerg Telecommun Technol. 32(2). https://doi.org/10.1002/ett.4014

55.

Ahmad Z, Shahid Khan A, Wai Shiang C, Abdullah J, Ahmad F (2020) Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transact Emerg Telecommun Technol. 32(1), https://doi.org/10.1002/ett.4150.

56.

Dubey S, Gupta C (2024) An Effective Model for Binary and Multi-classification Based on RFE and XGBoost Methods in Intrusion Detection System. Cyber Security and Digital Forensics. Lecture Notes in Networks and Systems, vol. 896. Springer, Singapore. https://doi.org/10.1007/978-981-99-9811-1_3.

57.

Gupta C, Kumar A, Jain NK (2024) An Enhanced Hybrid Intrusion Detection Based on Crow Search Analysis Optimizations and Artificial Neural Network. Wireless Personal Commun 134:43–68. https://doi.org/10.1007/s11277-024-10880-3CrossRef

58.

Gupta C, Kumar A, Jain NK (2023) A Detailed Analysis on Intrusion Detection Systems, Datasets, and Challenges. Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_26.

59.

Gheni HQ, Al-Yaseen WL (2024) Two-step data clustering for improved intrusion detection system using CICIoT2023 dataset, e-Prime - Advances in Electrical Engineering, Electronics and Energy, Volume 9. ISSN 100673:2772–6711. https://doi.org/10.1016/j.prime.2024.100673CrossRef

60.

Neto ECP, Dadkhah S, Ferreira R, Zohourian A, Lu R, Ghorbani AA (2023) CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment. Sensors 23(13):5941. https://doi.org/10.3390/s23135941CrossRef

Titel: Intrusion defense: Leveraging ant colony optimization for enhanced multi-optimization in network security
verfasst von: Chetan Gupta
Amit Kumar
Neelesh Kumar Jain
Publikationsdatum: 01.04.2025
Verlag: Springer US
Erschienen in: Peer-to-Peer Networking and Applications / Ausgabe 2/2025
Print ISSN: 1936-6442
Elektronische ISSN: 1936-6450
DOI: https://doi.org/10.1007/s12083-025-01911-2

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Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

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LVCA: An efficient voting-based consensus algorithm in private Blockchain for enhancing data security

A lightweight IoT device identification using enhanced behavioral-based features

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IPML-ANP: An integrated polynomial manifold learning model and anchor node placement for wireless sensor node localization

An efficient and private economic evaluation scheme in data markets