2020
DOI: 10.35940/ijrte.e6580.018520
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A Novel Framework for NIDS Throuh Fast Knn Classifier on CICIDS 2017 Dataset

Abstract: This paper investigates the performance of a Fast kNearest Neighbor Classifier (FkNN) for Network Intrusion Detection System (NIDS) on Cloud Environment. For this study Variance Index based Partial Distance Search (VIPDS) kNN [7] is adopted as an FkNN classifier. A benchmark dataset CICIDS2017[16] is considered for the evaluation process because it is a 78 featured dataset with most updated cloud related attacks. To achieve this objective a frame work is proposed for implementing FkNN and compared with kNN cla… Show more

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Cited by 8 publications
(6 citation statements)
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“…Vamsi Krishna et al published an article to improve the kNN-based classification process in terms of time; therefore, a Fast kNN framework was proposed. 23 The proposed method was evaluated on the CICIDS2017 DDoS dataset. The Fast kNN exhibited high performance on the dataset, and the reported accuracy, precision, and recall values were above 99%, as in the present study.…”
Section: Discussionmentioning
confidence: 99%
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“…Vamsi Krishna et al published an article to improve the kNN-based classification process in terms of time; therefore, a Fast kNN framework was proposed. 23 The proposed method was evaluated on the CICIDS2017 DDoS dataset. The Fast kNN exhibited high performance on the dataset, and the reported accuracy, precision, and recall values were above 99%, as in the present study.…”
Section: Discussionmentioning
confidence: 99%
“…Vamsi Krishna et al published an article to improve the kNN‐based classification process in terms of time; therefore, a Fast kNN framework was proposed 23 . The proposed method was evaluated on the CICIDS2017 DDoS dataset.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…This uneven structure must be formulated, as evidenced by the system's efficiency. To address the problem of classimbalanced data, which frequently leads to a low rate of anomaly detection, random oversampling and synthetic minority oversampling technique (SMOTE) are used [41], which typically results in a low anomaly detection rate can be used to generate additional data in minority classes where there is a scarcity of data [12]. 𝐼𝑚𝑏𝑎𝑙𝑎𝑛𝑐𝑒𝑅𝑎𝑡𝑖𝑜 = 𝑚𝑎𝑥 𝑖 {𝑥 𝑖 } 𝑚𝑖𝑛 𝑖 {𝑥 𝑖 } may be used to compute the imbalanced ratio, which can then be utilized as the matrices [42].…”
Section: Class Imbalace Datasetmentioning
confidence: 99%
“…As an evaluation, use a more realistic IDS and a current security dataset with real network traffic CSE-CIC-IDS-2018 for a wide range of intrusions and normal behavior. The CSE-CIC-IDS-2018 dataset is big data, associated with specific properties, such as volume, variety, velocity, variability, value, and complexity [12], [13]. The term "volume" refers to the amount of data present.…”
Section: Introductionmentioning
confidence: 99%