Real-Time Detection Schemes for Memory DoS (M-DoS) Attacks on Cloud Computing Applications
نویسندگان
چکیده
Memory Denial of Service (M-DoS) attacks refer to a class cyber-attacks that aim exhaust the memory resources system, rendering it unavailable legitimate users. This type attack is particularly dangerous in cloud computing environments, where multiple users share same resources. Detection and mitigation M-DoS real-time challenging task, as they often involve large number low-rate requests, making difficult distinguish them from traffic. Several detection schemes have been proposed identify mitigate environments. These can be broadly classified into two categories: signature-based anomaly-based detection. Signature-based methods rely on identification specific patterns or characteristics known techniques, while abnormal behaviour deviates normal pattern usage. study presents hybrid model for MDOS using SVM-KNN-LR. The dataset used this was collected various sources pre-processed extract relevant features A feature selection process also applied most important achieved an accuracy 96%, outperforming other individual models such SVM, KNN, LR, Naive Bayes, Decision Trees, Extra Bagging Random Forests. Confusion matrices were evaluate performance each model. In discussion section, we examined detecting found had high precision score 0.97. However, recall lower at 0.87, indicating not able detect all instances attacks.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3290910