Abstract
Security plays an important part in this Internet world because of the hasty improvement of Internet customers. Different Intrusion Detection Systems (IDS) have been advanced for various departments in history to describe and identify intruders utilizing data processing methods. Nonetheless, when using data processing, existing systems do not achieve adequate detection accuracy. For this reason, we suggest new IDS to offer preservation in statistics communications by completely describing intruders on wireless systems. Here, a new feature selection algorithm called enhanced conditional random field based feature selection to select the most contributed features and optimized hybrid deep neural network (OHDNN) is presented for the classification process. The hybrid deep neural network is a hybridization of convolution neural network (CNN) and long short-term memory (LSTM). To enhance the performance of the HDNN classifier, the parameters are optimized using adaptive golden eagle optimization. The performance of the presented approach is analyzed based on different metrics. For experimental analysis, the NSL-KDD and UNSW-NB15 datasets are used to compare its performance with other popular machine learning algorithms such as ANN, SVM, LSTM and CNN.
Similar content being viewed by others
References
Pan JS, Fan F, Chu SC, Zhao HQ, Liu GY (2021) A lightweight intelligent intrusion detection model for wireless sensor networks. Secur Commun Netw 2021:5540895. https://doi.org/10.1155/2021/5540895
Sadeghizadeh M (2022) A lightweight intrusion detection system based on RSSI for sybil attack detection in wireless sensor networks. Int J Nonlinear Anal Appl 13(1):305–320. https://doi.org/10.22075/ijnaa.2022.5491
Prabakar D, Swaminathan G, Sasikala S, Saravanan TR, Ramesh S (2021) Enhanced simulating annealing and SVM for intrusion detection system in wireless sensor networks. Res Square. https://doi.org/10.21203/rs.3.rs-193449/v1
Al-Qatf M, Lasheng Y, Al-Habib M, Al-Sabahi K (2018) ‘Deep learning approach combining sparse autoencoder with SVM for network intrusion detection.’ IEEE Access 6:52843–52856
Vinayakumar R, Alazab M, Soman KP, Poornachandran P, Al-Nemrat A, Venkatraman S (2019) ‘Deep learning approach for intelligent intrusion detection system.’ IEEE Access 7:41525–41550
Kumar KS, Nair SAH, Roy DG, Rajalingam B, Kumar RS (2021) Security and privacy-aware artificial intrusion detection system using federated machine learning. Comput Electr Eng 96:107440
Al S, Dener M (2021) STL-HDL: a new hybrid network intrusion detection system for imbalanced dataset on big data environment. Comput Secur 110:102435
Subba B, Gupta P (2021) A tfidfvectorizer and singular value decomposition based host intrusion detection system framework for detecting anomalous system processes. Comput Secur 100:102084
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
Singh A, Nagar J, Sharma S, Kotiyal V (2021) A Gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks. Expert Syst Appl 172:114603
Yazdinejadna A, Parizi RM, Dehghantanha A, Khan MS (2021) A kangaroo-based intrusion detection system on software-defined networks. Comput Netw 184:107688
Otoum S, Kantarci B, Mouftah HT (2019) On the feasibility of deep learning in sensor network intrusion detection. IEEE Netw Lett 1(2):68–71
Jan SU, Ahmed S, Shakhov V, Koo I (2019) Toward a lightweight intrusion detection system for the internet of things. IEEE Access 7:42450–42471
Khan MA, Karim M, Kim Y (2019) A scalable and hybrid intrusion detection system based on the convolutional-LSTM network. Symmetry 11(4):583
Anthi E, Williams L, Słowińska M, Theodorakopoulos G, Burnap P (2019) A supervised intrusion detection system for smart home IoT devices. IEEE Internet Things J 6(5):9042–9053
Swarna Priya RM, Maddikunta PKR, Parimala M, Koppu S, Gadekallu TR, Chowdhary CL, Alazab M (2020) An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Comput Commun 160:139–149
Du Y, Xia J, Ma J, Zhang W (2021) An optimal decision method for intrusion detection system in wireless sensor networks with enhanced cooperation mechanism. IEEE Access 9:69498–69512
Amouri A, Alaparthy VT, Morgera SD (2020) A machine learning based intrusion detection system for mobile Internet of Things. Sensors 20(2):461
Zhang R, Xiao X (2019) ‘Intrusion detection in wireless sensor networks with an improved NSA based on space division.’ J Sensors 2019:1–20
Maheswari M, Karthika RA (2021) A novel QoS based secure unequal clustering protocol with intrusion detection system in wireless sensor networks. Wirel Pers Commun 118(2):1535–1557
Wen W, Shang C, Dong Z, Keh HC, Roy DS (2021) An intrusion detection model using improved convolutional deep belief networks for wireless sensor networks. Int J Ad Hoc Ubiquitous Comput 36(1):20–31
Karthic S, Manoj Kumar S (2022) Wireless intrusion detection based on optimized lstm with stacked auto encoder network. Intell Autom Soft Comput 34(1):439–453
Krishnan R, Krishnan RS, Robinson YH, Julie EG, Long HV, Sangeetha A, Subramanian M, Kumar R (2021) An intrusion detection and prevention protocol for Internet of Things based wireless sensor networks
Hu L, Yuan X, Liu X, Xiong S, Luo X (2018) Efficiently detecting protein complexes from protein interaction networks via alternating direction method of multipliers. IEEE/ACM Trans Comput Biol Bioinf 16(6):1922–1935
Wu D, Luo X, Shang M, He Y, Wang G, Zhou M (2019) A deep latent factor model for high-dimensional and sparse matrices in recommender systems. IEEE Trans Syst Man Cybern Syst 51(7):4285–4296
Gupta K, Nath B, Kotagiri R (2010) Layered approach using conditional random fields for intrusion detection. IEEE Trans Dependable Secure Comput 7(1):35–49
Funding
The authors have not received any funding for this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Mr. S. Karthic declares that he has no conflict of interest. Dr. S. Manoj Kumar declares that he has no conflict of interest.
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
Karthic, S., Kumar, S.M. Hybrid Optimized Deep Neural Network with Enhanced Conditional Random Field Based Intrusion Detection on Wireless Sensor Network. Neural Process Lett 55, 459–479 (2023). https://doi.org/10.1007/s11063-022-10892-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-022-10892-9