Abstract
With the emergence of big data era, the dimensions of data are enhanced exponentially and it becomes a difficult task to handle information of high dimensions in various sectors like text mining, machine learning and data analysis. Redundant and inappropriate feature enhances the complexities in dimensions that further results in poor performances. In the intrusion detection system, the feature selection is considered as one of the most significant processes to improve the performances of the system. Due to high dimensional data, there occurs a drop in accuracy and efficiency. To overcome such drawback, this paper proposes three major phases namely the data pre-processing, feature selection and classification phases. In data-pre processing phase, the input data comprising of various noise signals, high dimensional and redundant data, numerous irrelevant features etc. are extracted. The second phase involves the selection of features using cooperative and competitive (C2) search based learning algorithm. In the classification phase, the extracted features are classified optimally using Bonferroni based Hybrid k-nearest neighbour (B-HkNN) algorithm thereby obtaining an optimal intrusion detection system. Furthermore, the proposed approach based on intrusion detection system is evaluated by the standard CICIDS2017 and ADFA-LD datasets to determine the accuracy and efficiency of the system.
Similar content being viewed by others
Data Availability
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
References
Ahmad Z, Shahid Khan A, Wai Shiang C, Abdullah J, Ahmad F (2021) Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans Emerg Telecommun Technol 32(1):e4150
Sohi SM, Seifert JP, Ganji F (2021) RNNIDS: enhancing network intrusion detection systems through deep learning. Comput Secur 102:102151
Marteau PF (2021) Random partitioning forest for point-wise and collective anomaly detection-application to network intrusion detection. IEEE Trans Inf Forensics Secur 16:2157–2172
Ojugo AA, Yoro RE (2021) Forging a deep learning neural network intrusion detection framework to curb the distributed denial of service attack. Int J Electr Comput Eng 11(2):1498
Mulyanto M, Faisal M, Prakosa SW, Leu JS (2021) Effectiveness of focal loss for minority classification in network intrusion detection systems. Symmetry 13(1):4
Li X, Yi P, Wei W, Jiang Y, Tian L (2021) LNNLS-KH: a feature selection method for network intrusion detection. Secur Commun Netw. https://doi.org/10.1155/2021/8830431
Ajdani M, Ghaffary H (2021) Design network intrusion detection system using support vector machine. Int J Commun Syst 34(3):e4689
Nazir A, Khan RA (2021) Network intrusion detection: taxonomy and machine learning applications. In: Maleh Y, Shojafar M, Alazab M, Baddi Y (eds) Machine intelligence and big data analytics for cybersecurity applications. Springer, Cham, pp 3–28
Yao R, Wang N, Liu Z, Chen P, Sheng X (2021) Intrusion detection system in the advanced metering infrastructure: a cross-layer feature-fusion CNN-LSTM-based approach. Sensors 21(2):626
Sundararaj V, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288
Sundararaj V (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126
Sundararaj V (2019) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325
Sundararaj V, Anoop V, Dixit P, Arjaria A, Chourasia U, Bhambri P, Rejeesh MR, Sundararaj R (2020) CCGPA-MPPT: cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Prog Photovoltaics 28(11):1128–1145
Ravikumar S, Kavitha D (2021) CNN-OHGS: CNN-oppositional-based Henry gas solubility optimization model for autonomous vehicle control system. J Field Robot. https://doi.org/10.1002/rob.22020
Ravikumar S, Kavitha D (2020) IoT based home monitoring system with secure data storage by Keccak-Chaotic sequence in cloud server. J Ambient Intell Humaniz Comput 12:7475–7487
Rejeesh MR (2019) Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed Tools Appl 78(16):22691–22710
Kavitha D, Ravikumar S (2021) IOT and context-aware learning-based optimal neural network model for real-time health monitoring. Trans Emerg Telecommun Technol 32(1):e4132
Hassan BA, Rashid TA (2020) Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared with its counterpart algorithms. Data Brief 28:105046
Hassan BA (2020) CSCF: a chaotic sine cosine firefly algorithm for practical application problems. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05474-6
Hassan BA, Rashid TA, Mirjalili S (2021) Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star. Complex Intell Syst. https://doi.org/10.1007/s40747-021-00422-w
Haseena KS, Anees S, Madheswari N (2014) Power optimization using EPAR protocol in MANET. Int J Innov Sci Eng Technol 6:430–436
GowthulAlam MM, Baulkani S (2019) Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction. Knowl Inf Syst 60(2):971–1000
GowthulAlam MM, Baulkani S (2017) Reformulated query-based document retrieval using optimised kernel fuzzy clustering algorithm. Int J Bus Intell Data Min 12(3):299
GowthulAlam MM, Baulkani S (2019) Geometric structure information based multi-objective function to increase fuzzy clustering performance with artificial and real-life data. Soft Comput 23(4):1079–1098
Nisha S, Madheswari AN (2016) Secured authentication for internet voting in corporate companies to prevent phishing attacks. Int J Emerg Technol Comput Sci Electron (IJETCSE) 22(1):45–49
Zhou Q, Tan M, Xi H (2021) ACGANs-CNN: a novel intrusion detection method. J Phys 1757(1):012012
Haghnegahdar L, Wang Y (2020) A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection. Neural Comput Appl 32(13):9427–9441
Karataş G (2016) Genetic algorithm for intrusion detection system. In: 2016 24th Signal Processing and Communication Application Conference (SIU), IEEE, pp 1341–1344
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
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
Prasad M, Tripathi S, Dahal K (2020) Unsupervised feature selection and cluster center initialization based arbitrary shaped clusters for intrusion detection. Comput Secur 99:102062
Li X, Chen W, Zhang Q, Wu L (2020) Building auto-encoder intrusion detection system based on random forest feature selection. Comput Secur. https://doi.org/10.1016/j.cose.2020.101851
Zhou Y, Cheng G, Jiang S, Dai M (2020) Building an efficient intrusion detection system based on feature selection and ensemble classifier. Comput Netw 174:107247
Almomani O (2020) A feature selection model for network intrusion detection system based on PSO, GWO, FFA and GA algorithms. Symmetry 12(6):1046
Nagaraja A, Uma B, kumarGunupudi R (2020) UTTAMA: an intrusion detection system based on feature clustering and feature transformation. Found Sci 25(4):1049–1075
Ayo FE, Folorunso SO, Abayomi-Alli AA, Adekunle AO, Awotunde JB (2020) Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection. Inf Secur J 29(6):267–283
Chkirbene Z, Erbad A, Hamila R, Mohamed A, Guizani M, Hamdi M (2020) TIDCS: a dynamic intrusion detection and classification system based feature selection. IEEE Access 8:95864–95877
Kalaivani S, Gopinath G (2020) Modified bee colony with bacterial foraging optimization based hybrid feature selection technique for intrusion detection system classifier model. ICTACT J Soft Comput. https://doi.org/10.21917/ijsc.2020.0305
Zhang J, Lin Y, Jiang M, Li S, Tang Y, Tan KC (2020) Multi-label feature selection via global relevance and redundancy optimization. Int Joint Conf Artif Intell Organ. https://doi.org/10.24963/ijcai.2020/348
Shahee SA, Ananthakumar U (2020) An effective distance based feature selection approach for imbalanced data. Appl Intell 50(3):717–745
Du G, Zhang J, Luo Z, Ma F, Ma L, Li S (2020) Joint imbalanced classification and feature selection for hospital readmissions. Knowl-Based Syst 200:106020
Al-Utaibi KA, El-Alfy ES (2018) Intrusion detection taxonomy and data preprocessing mechanisms. J Intell Fuzzy Syst 34(3):1369–1383
Kasongo SM, Sun Y (2020) A deep learning method with wrapper based feature selection for wireless intrusion detection system. Comput Secur 92:101752
Feng ZK, Niu WJ, Liu S (2021) Cooperation search algorithm: a novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems. Appl Soft Comput 98:106734
Bouchekara HR (2020) Most valuable player algorithm: a novel optimization algorithm inspired from sport. Oper Res Int Journal 20(1):139–195
Kumbure MM, Luukka P, Collan M (2020) A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean. Pattern Recogn Lett 140:172–178
Pan Z, Wang Y, Pan Y (2020) A new locally adaptive k-nearest neighbor algorithm based on discrimination class. Knowl-Based Syst 204:106185
Vijayanand R, Devaraj D (2020) A novel feature selection method using whale optimization algorithm and genetic operators for intrusion detection system in wireless mesh network. IEEE Access 8:56847–56854
Chawla A, Brian L, Sheila F, Paul J (2018) Host based intrusion detection system with combined CNN/RNN model. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, pp 149–158
Sun P, Liu P, Li Q, Liu C, Lu X, Hao R, Chen J (2020) DL-IDS: extracting features using CNN-LSTM hybrid network for intrusion detection system. Secur Commun Netw. https://doi.org/10.1155/2020/8890306
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Research Involving Human and Animal Participants
This article does not contain any studies with human or animal subjects performed by any of the authors.
Informed Consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Balasaraswathi, V.R., Mary Shamala, L., Hamid, Y. et al. An Efficient Feature Selection for Intrusion Detection System Using B-HKNN and C2 Search Based Learning Model. Neural Process Lett 54, 5143–5167 (2022). https://doi.org/10.1007/s11063-022-10854-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-022-10854-1