A FeedForward–Convolutional Neural Network to Detect Low-Rate DoS in IoT

Ilango, Harun Surej; Su, Rong

doi:10.1016/j.engappai.2022.105059

Cited by 19 publications

(13 citation statements)

References 10 publications

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“…Similarly, the REP Tree [36] achieved comparable results for all metrics to those of J48. In all metrics, SMO [39] and IBK [39] had comparable outcomes, except for FPR, where IBK had a lower FPR of 2.20% and SMO had a higher FPR of 6%.…”

Section: Performance Analysis and Evaluationmentioning

confidence: 70%

“…However, the authors of [40] did not indicate the method's F1-score or its precision. As seen in table 5, The SVM [36] approach obtained the longest training and detection times among all methods of 223.86 s and 139.08 μs, respectively. MLP [36] had the quickest detection time among all approaches, but it had the lowest F1-score and recall rates of 64.70% and 47.90%, respectively.…”

Section: Performance Analysis and Evaluationmentioning

confidence: 94%

“…The last three LR DoS detection techniques [32], [33], and [35] presented above are signature-based and may not accurately identify new attacks. In another attempt to provide a solution for identifying LR DoS attacks in SD-IoT, the authors of [36] introduced a FeedForward-Convolutional Neural Network (FFCNN), an AI-based anomaly detection method. FFCNN detects LR DoS attacks by combining a FeedForward Neural Network (FFNN) with a CNN.…”

Section: Related Workmentioning

confidence: 99%

“…It is clear that the proposed RF-FMDADM model performs better than the existing ML methods in terms of accuracy, F1-score, recall, and false positive rate, with scores of 99.89%, 99.43%, 99.77%, and 00.21%, respectively. In terms of accuracy, the FFCNN [36] approach comes second after the proposed RF-FMDADM approach, with an accuracy rate of 99.0%. With a precision rate of 99.9%, J48 [36] was the most precise among the nine techniques included in the comparison.…”

Section: Performance Analysis and Evaluationmentioning

confidence: 99%

“…In terms of accuracy, the FFCNN [36] approach comes second after the proposed RF-FMDADM approach, with an accuracy rate of 99.0%. With a precision rate of 99.9%, J48 [36] was the most precise among the nine techniques included in the comparison. However, compared to other approaches, it has a very low recall, F1-score, and accuracy rate of 58.90%, 74.10%, and 93.70%, respectively.…”

Section: Performance Analysis and Evaluationmentioning

confidence: 99%

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FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

2023

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The absence of standards and the diverse nature of the Internet of Things (IoT) have made security and privacy concerns more acute. Attacks such as distributed denial of service (DDoS) are becoming increasingly widespread in IoT, and the need for ways to stop them is growing. The use of newly formed Software-Defined Networking (SDN) significantly lowers the computational burden on IoT network nodes and makes it possible to perform more security measurements. This paper proposes an SDNbased, four-module DDoS attack detection and mitigation framework for IoT networks called FMDADM. The proposed FMDADM framework comprises four main modules and five-tier architecture. The first module implements an early detection process based on the average drop rate (ADR) principle using a 32packet window size. The second module uses a novel double-check mapping function (DCMF), that aids in earlier attack detection at the data plane level. The third module is an ML-based detection application comprising four phases: data preprocessing, feature extraction, training and testing, and classification. This module detects DDoS attacks using only seven features: two selected and five newly computed features. The last module introduces an attack mitigation process. We applied the proposed framework to three test cases: one single-node attack test case and two multi-node attack test cases, all with real IoT traffic generated and deployed in Mininet-IoT. The proposed FMDADM framework efficiently detects DDoS attacks at high and low rates, can discriminate between attack traffic and flash crowds, and protects both local and remote IoT nodes by preventing infection from propagating to the ISP level. The FMDADM outperformed most existing cutting-edge approaches across ten different evaluation criteria. According to the experimental results, FMDADM achieved the following accuracy,

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Section: Performance Analysis and Evaluationmentioning

confidence: 70%