Abstract
Through widespread internet protocols and network standards, networking services are accessible. Insecure communication and assaults on traffic networks must be taken into consideration moreover the special benefits of networking services. There are several methods for defending against network assaults. In order to combat network abnormalities, network anomaly detection systems are often employed. Systems for detecting network anomalies have drawn a lot of interest in terms of applying machine learning techniques to monitor network traffic intelligently. In this study, an efficient autoencoder-based model for anomaly detection in network traffic is presented. The autoencoder picks up a fundamental depiction of data and how to rebuild it with the least amount of error employed as an anomaly metric. A novel framework is proposed for malicious data detection in network congestion. Workflow for this proposed framework is mentioned in this paper. Also gives a standard formula for setting the threshold and compare these values with anomaly score i.e., error. Anomaly and normal sample are categorized on the basis of threshold value. As a well-used dataset in the literature, utilize the CIDDS-2017 dataset.
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Singh, R., Srivastava, N., Kumar, A. (2023). Novel Approach for Network Anomaly Detection Using Autoencoder on CICIDS Dataset. In: Hasteer, N., McLoone, S., Khari, M., Sharma, P. (eds) Decision Intelligence Solutions. InCITe 2023. Lecture Notes in Electrical Engineering, vol 1080. Springer, Singapore. https://doi.org/10.1007/978-981-99-5994-5_19
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DOI: https://doi.org/10.1007/978-981-99-5994-5_19
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