Distributed Denial of Service Attack Detection Using Ant Bee Colony and Artificial Neural Network in Cloud Computing

Ali, Uzma; Dewangan, Kranti Kumar; Dewangan, Deepak Kumar

doi:10.1007/978-981-10-6747-1_19

Cited by 14 publications

(2 citation statements)

References 12 publications

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“…The results obtained confirmed that the proposed HHO-PSO-DLNN model is statistically better than all other models developed in this proposed research work. [41] 0.8989 0.9556 0.8777 0.9335 0.9150 GA-BPN [42] 0.9212 0.9618 0.9056 0.9450 0.9328 PSO-BPN [43] 0.9096 0.9618 0.8886 0.9434 0.9237 GA-MLP [44] 0.9140 0.9588 0.8972 0.9401 0.9270 MLP [45] 0.9229 0.9694 0.9025 0.9551 0.9347 LSTM [46] 0.9545 0.9607 0.9594 0.9480 0.9600 LSTM [47] 0.9542 0.9676 0.9527 0.9563 0.9601 C4.5 [34] 0.9370 0.9680 0.9248 0.9549 0.9459 KNN [34] 0.9170 0.9368 0.9189 0.9143 0.9278 SVM [34] 0.9494 0.9585 0.9528 0.9448 0.9557 BPN [48] 0.9272 0.9663 0.9111 0.9515 0.9379 MLP [49] 0…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Recurrent and Deep Learning Neural Network Models for DDoS Attack Detection

2022

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Distributed denial of service (DDoS) attack is a subclass of denial of service attack that performs severe attack in a cloud computing environment. It makes a malicious attempt to disturb the usual services of any network or server by using botnets. Hence, an efficient intrusion detection system (IDS) is essential to detect this attack. Some limitations in the existing IDS models for DDoS attack detection are delayed convergence, local stagnation issues, and local and global optimal trapping issues. These limitations are met by the recurrent neural network (RNN) and deep learning- (DL-) based proposed models that can utilize the previous states of the hidden neuron. The proposed research has used a long short-term memory (LSTM) recurrent neural network and autoencoder- and decoder-based deep learning strategy with gradient descent learning rule. The network parameters like weight vectors and bias coefficient are tuned optimally by employing the proposed a hybrid Harris Hawks optimization (HHO) and particle swarm optimization (PSO) algorithm. The proposed hybrid optimization algorithm selects the essential attributes, and the results obtained confirmed that the proposed LSTM and deep learning model outperformed all other models developed in the literature.

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Section: Journal Of Sensorsmentioning

confidence: 99%

Recurrent and Deep Learning Neural Network Models for DDoS Attack Detection

2022

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“…The main focus of ML is to develop programs that use data in the discovery process without human intervention. ML algorithms can be classified into Supervised ML algorithms-which enable predictions of output from given data; Unsupervised ML algorithms-which enable inferences to be drawn on structures which are not obvious from unknown data; and Semi-supervised ML algorithms-which enable blending of features of both Supervised and Unsupervised ML algorithms and are mostly used to quantify the training data [8]. A detailed comparison of ML based IDS approaches reviewed in this SLR is given in Table I.…”

Section: A Machine Learning Based Ids Approaches In Cloudmentioning

confidence: 99%