Abstract
Combating the cyber threats, particularly attack detection, is a challenging area of the intrusion detection system (IDS). Exceptional development and internet usage raise concerns about how digital data can be securely communicated and protected. This work has proposed a multi demeanor fusion-based intrusion detection system where stream data mining based on ST-SR (stochastic relaxation). Thus it includes uncertain c capitals clustering with multi data fusion is incorporated to classify the fused network traffic information effectively. Subsequently, classified data would be sent to the web usage mining based on a stochastic Latent Semantic and synthetic Analyzer which analyzes the traffic information. Even though being classified and analyzed the network traffic information itself can’t get connected to the secured network due to its dynamic nature so to handle this situation, this work has incorporated IDS model (intrusion detection model based on parallel ensemble using bagging) which predicts the quality of service of each network during network traffic and enables the user to get connected with a secured network which holds high packet delivery ratio, less packet loss, and high throughput.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Aljawarneh SA, Raja MA, Abdelsalam MM (2016) Investigations of automatic methods for detecting the polymorphic worms signatures. Future Gener Comput Syst 60:67–77
Benisha RB, Ratna SR (2020) Detection of data integrity attacks by constructing an effective intrusion detection system. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01850-1
Benzina H, Jean G-L (2010) Some ideas on virtualized system security, and monitors. In: Data privacy management and autonomous spontaneous security. pp 244–258
Besharati E, Naderan M, Namjoo E (2019) LR-HIDS: logistic regression host-based intrusion detection system for cloud environments. J Ambient Intell Humaniz Comput 10(9):3669–3692
Bhushan K, Gupta BB (2019) Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment. J Ambient Intell Humaniz Comput 10(5):1985–1997
Cheruvu A, Aljawarneh S, Radhakrishna V (2018) VRKSHA: a novel multi-tree based sequential approach for seasonal pattern mining. In: Proceedings of the fourth international conference on engineering & MIS 37
De la Hoz E, De La Hoz E, Ortiz A, Ortega J, Prieto B (2015) PCA filtering and probabilistic SOM for network intrusion detection. Neurocomputing 164:71–81
Depren O, Murat T, Emin A, Kemal CM (2005) An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert Syst Appl 29(4):713–722
Gaikwad DP, Ravindra TC (2015) Intrusion detection system using bagging with partial decision treebase classifier. Procedia Comput Sci 49:92–98
Janaki V, Radhakrishna V, Kumar PV (2018) SRIHASS-a similarity measure for discovery of hidden time profiled temporal associations. Multimed Tools Appl 77(14):17643–17692
Kumar PV, Radhakrishna V, Janaki V, Krishna S (2018) A Z-space similarity measure. In: Proceedings of the fourth international conference on engineering & MIS, 44
Liao H-J, Chun-Hung RL, Ying-Chih L, Kuang-Yuan T (2013) Intrusion detection system: a comprehensive review. J Netw Comput Appl 36(1):16–24
Meira J, Andrade R, Praça I, Carneiro J, Bolón-Canedo V, Alonso-Betanzos A, Marreiros G (2019) Performance evaluation of unsupervised techniques in cyber-attack anomaly detection. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-019-01417-9
Parveen P, Zackary WR, Bhavani T, Kevin H, Latifur K (2011) Supervised learning for insider threat detection using stream mining. In: 2011 IEEE 23rd international conference on tools with artificial intelligence. pp 1032–1039
Pawar SN, Rajankumar SB (2015) Genetic algorithm with variable length chromosomes for network intrusion detection. Int J Autom Comput 12(3):337–342
Radhakrishna V, Aljawarneh S, Kumar PV, Cheruvu A (2018) Kaala vrksha: extending vrksha for time profiled temporal association mining. In: Proceedings of the first international conference on data science, E-learning and information systems, 30
Ravale U, Nilesh M, Puja P (2015) Feature selection based hybrid anomaly intrusion detection system using K means and RBF kernel function. Procedia Comput Sci 45:428–435
Sabhnani M, Gürsel S (2003) Application of machine learning algorithms to KDD intrusion detection dataset within misuse detection context. In: MLMTA. pp 209–215
Shadi A, Radhakrishna V, Reddy GS (2018) Mantra: a novel imputation measure for disease classification and prediction. In: Proceedings of the first international conference on data science, E-learning and information systems, vol 25. ACM
Tekerek A, Cemal G, Omer FB (2014) Development of a hybrid web application firewall to prevent web based attacks. In: 2014 IEEE 8th international conference on application of information and communication technologies (AICT) IEEE. pp 1–4
Uppuluri P, Sekar R (2001) Experiences with specification-based intrusion detection. In: International workshop on recent advances in intrusion detection. Springer, Berlin, pp 172–189
Vangipuram R, Kumar PV, Janaki V, Aljawarneh S (2018) GANDIVA-Time profiled temporal pattern tree. In: Proceedings of the fourth international conference on engineering & MIS, 36
Author information
Authors and Affiliations
Corresponding author
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
Gupta, A.R., Agrawal, J. The multi-demeanor fusion based robust intrusion detection system for anomaly and misuse detection in computer networks. J Ambient Intell Human Comput 12, 303–319 (2021). https://doi.org/10.1007/s12652-020-01974-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-01974-4