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Informative Article | Engineering Science | India | Volume 11 Issue 1, January 2022 | Popularity: 4.3 / 10
Percentile-Based DoS Attack Detection and Severity Level Identification in Computer Networking Using SS-LH-BiLSTM
Amaresan Venkatesan
Abstract: By overwhelming the network functions with a flood of illegitimate requests, Denial of Service (DoS) attacks disrupt them. But, identifying the severity of the DoS attacks wasn?t concentrated in any of the conventional studies. Thus, by utilizing Sinc-Softmin and Lecun-He-based Bidirectional Long Short-Term Memory (SS-LH-BiLSTM), a DoS attack detection and severity identification model is proposed. Primarily, the data are gathered and preprocessed. Next, the data are segmented as well as sorted in ascending order. Then, centered on the duration of data transmission as well as the total number of data packets, the percentile threshold is estimated. After that, by utilizing the percentile value, the normal and abnormal behavior of the data is determined. For the instances of abnormal data, features are extracted and optimal features are chosen for identifying the attack severity. By utilizing SS-LH-BiLSTM, the DoS attack types and their severity levels are detected. Rate limiting is processed if a low or medium-level attack is detected; or else, the data is blocked. As per the experimental analysis, the proposed system outperformed conventional systems by achieving 98.89% accuracy.
Keywords: DoS, Percentile threshold, Attack Detection System (ADS), Computer Network (CN), Sliding Window (SW), SZPi-Fuzzy Inference System (SZP-FIS), Glorot initialization-based Seagull Optimization Algorithm (GSOA), and Weighted Round Robin-based Token Bucket Algorithm (WRR-TBA)
Edition: Volume 11 Issue 1, January 2022
Pages: 1676 - 1686
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