Abstract
The remarkable development of network and communication technologies has increased human activities in cyberspace. This change has incited an open, baffling, and uncontrolled system of the Internet which engages an astonishing stage for the cyberattack. Due to the phenomenal increase in cyberattack incidents, the development of more innovative and effective detection mechanisms has been regarded as an immediate requirement. Consequently, intrusion detection systems (IDSs) have become a necessary component of network security. There exist various approaches to detecting intrusions, but none are entirely reliable, which calls for the need for an improvement on the existing models. Traditional signature-based detection methods are not very effective. Therefore, machine learning (ML) algorithms are used to classify network traffic. To perform the classification of network traffic, five ML algorithms—decision tree, AdaBoost, random forest, Gaussian Naive Bayes, and KNN— were built. To improve the classification model, a hybrid model was built using three decision trees. The hybrid model yielded the best results, exhibiting the highest accuracy and the lowest execution time.
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References
Hoque MS et al (2012) An implementation of an intrusion detection system using genetic algorithm. arXiv preprint arXiv:1204.1336
Elmrabit N et al (2020) Evaluation of machine learning algorithms for anomaly detection. In: 2020 International conference on cyber security and protection of digital services (cyber security). IEEE
Ashoor AS, Gore S (2011) Importance of intrusion detection system (IDS). Int J Sci Eng Res 2(1):1–4
Kumar V, Sangwan OP (2012) Signature-based intrusion detection system using SNORT. Int J Comput Appl Inf Technol 1(3):35–41
Vijayanand R, Devaraj D, Kannapiran B (2019) A novel deep learning-based intrusion detection system for smart meter communication network. In: 2019 IEEE international conference on intelligent techniques in control, optimization and signal processing (INCOS). IEEE
Hossain F, Akter M, Uddin MN (2021) Cyber attack detection model (CADM) based on machine learning approach. In: 2021 2nd International conference on robotics, electrical and signal processing techniques (ICREST). IEEE
Russell S, Norvig P (2002) Artificial intelligence: a modern approach
Eltom AA, Intrusion detection systems. Int J Mod Commun Technol Res 2(9):265768
Lee B et al (2018) Comparative study of deep learning models for network intrusion detection. SMU Data Sci Rev 1(1):8
Stiawan D et al (2020) CICIDS-2017 dataset feature analysis with information gain for anomaly detection. IEEE Access 8:132911–132921
Shah A et al (2020) Building multiclass classification baselines for anomaly-based network intrusion detection systems. In: 2020 IEEE 7th International conference on data science and advanced analytics (DSAA). IEEE
Guowei ZHU et al (2021) Research on network intrusion detection method of power system based on random forest algorithm. In: 2021 13th International conference on measuring technology and mechatronics automation (ICMTMA). IEEE
Yedukondalu G et al (2021) Intrusion detection system framework using machine learning. In: 2021 Third international conference on inventive research in computing applications (ICIRCA). IEEE
Widulinski P, Wawryn K (2020) A human immunity inspired intrusion detection system to search for infections in an operating system. In: 2020 27th International conference on mixed design of integrated circuits and system (MIXDES). IEEE
Chen JI, Lai KL (2021) Deep convolution neural network model for credit-card fraud detection and alert. J Artif Intell 3(2):101–112
Sathesh A (2019) Enhanced soft computing approaches for intrusion detection schemes in social media networks. J Soft Comput Paradigm (JSCP) 1(02):69–79
Mugunthan SR (2019) Soft computing based autonomous low rate DDOS attack detection and security for cloud computing. J Soft Comput Paradigm (JSCP) 1(02):80–90
Sharma R, Sungheetha A (2021) An efficient dimension reduction based fusion of CNN and SVM model for detection of abnormal incident in video surveillance. J Soft Comput Paradigm (JSCP) 3(02):55–69
Swain PH, Hauska H (1977) The decision tree classifier: design and potential. IEEE Trans Geosci Electron 15(3):142–147
Schapire RE (2013) Explaining adaboost. In: Empirical inference. Springer, Berlin, Heidelberg, pp 37–52
Biau G, Scornet E (2016) A random forest guided tour. TEST 25(2):197–227
Bouckaert RR (2004) Naive Bayes classifiers that perform well with continuous variables. In: Australasian joint conference on artificial intelligence. Springer, Berlin, Heidelberg
Guo G et al (2003) KNN model-based approach in classification. In: OTM confederated international conferences “On the move to meaningful internet systems”. Springer, Berlin, Heidelberg
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Amrutha, B.S., Meghana, I., Tejas, R., Pilare, H.V., Annapurna, D. (2022). An Efficient Automated Intrusion Detection System Using Hybrid Decision Tree. In: Suma, V., Baig, Z., Kolandapalayam Shanmugam, S., Lorenz, P. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-19-1012-8_49
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DOI: https://doi.org/10.1007/978-981-19-1012-8_49
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